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The effects of human behavior on the efficiency of routing policies in order picking: The case of route deviations
Abstract
Retrieving items from storage locations in warehouses, commonly referred to as order picking, is often performed by human workers in practice. The high amount of human work involved in order picking turns this activity into a time- and cost-intensive process step in warehouse operations. Due to the cost impact of manual order picking, researchers have developed various planning methods that support practitioners in realizing an efficient order picking process. Among these planning approaches, methods that support the routing of order pickers through the warehouse have been a very popular research topic in recent years, with the focus being both on the development of optimal and heuristic routing policies. Surprisingly, problems that may arise when implementing picker routes for human workers in practice have not been investigated so far. There is, however, empirical evidence that order pickers tend to deviate from optimal routes in practice, putting the efficiency of these routing approaches at stake.
Supplementary resource (1)
... In practice, route deviation by the order picker is very common. Deviation may occur in two ways, either by skipping an aisle containing items from the picklist or by skipping items in an aisle, or both [12]. In either case, deviation is a behavioral issue that is detrimental to the efficiency of routing policies. ...
... To eliminate pickers' deviations and thus decrease operating cost, Ref. [12] proposed that training be held and handheld guiding devices be provided to the order pickers to help them adhere to the optimal routing or the predetermined route. However, integrating technologies into the picking Publisher's Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. ...
... This is in line with our previous results in Table 6 where almost half of the subsets for warehouse 1 contain the midpoint heuristic. This supports the literature Ref. [12] that the midpoint outperforms the traversal and return policies for orders containing a small number of picks. The midpoint-voice picking combination results in the best order picking performance for warehouse 1. ...
Recent literature demonstrates that warehouse order picking performance is reflected in the logistics performance of downstream retailers. Warehouse solutions and policies significantly contribute to the improvement of distribution and delivery to retailers. This paper therefore reports an analysis of the joint performance of routing policies and picking technologies, and provides insights into the best ways to combine routing strategies and paperless solutions in order to optimize cost efficiency. We follow a multistage approach that combines mixed integer linear programming algorithms, data envelopment analysis (DEA), and ranking and selection. The results show that traversal-voice picking and midpoint-voice picking combinations are equally distributed over the most efficient subsets and that superior technology can enhance picking efficiency only to a certain level. The study provides guidelines for logistics managers on ways to combine warehouse solutions and policies in order to better streamline the operations. It offers an original framework to analyze the joint performance of picking routing and picking solutions by considering the effect of picking errors.
... Zulj et al. [17] proposed a model allowing the identification of a picking-routing strategy based on stacking constraints. The effects of the human behavior on the efficiency of routing policies in order picking are investigated in [26]. The study is focused on how deviations from routes impact the efficiency of different routing policies. ...
... The objective in (12) is minimizing the total cost, which is the sum of the penalty cost related to makespan over all of the jobs (weighted by π I ), the penalty cost related to the completion time of the overall battery recharging process (weighted by π II ), and the total electricity cost for charging batteries of forklifts (weighted by π III ). Constraints (13)- (16) are related to the job scheduling, whilst (17)- (26) and (27)- (36) are aimed at scheduling the optimal battery changes and determining the optimal recharging cost strategies, respectively. Finally, Constraints (37)-(40) specify the integrality conditions on the defined decision variables. ...
... Job index k [ 1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30 ] Job priority p k [ 30,29,28,27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2,1 ] Job processing duration l k [ 9,9,9,6,3,4 Table 3. Forklifts and charging station parameters. We further assume that the energy cost is time-dependent. ...
In recent years, the continuous increase of greenhouse gas emissions has led many companies to investigate the activities that have the greatest impact on the environment. Recent studies estimate that around 10% of worldwide CO2 emissions derive from logistical supply chains. The considerable amount of energy required for heating, cooling, and lighting as well as material handling equipment (MHE) in warehouses represents about 20% of the overall logistical costs. The reduction of warehouses’ energy consumption would thus lead to a significant benefit from an environmental point of view. In this context, sustainable strategies allowing the minimization of the cost of energy consumption due to MHE represent a new challenge in warehouse management. Consistent with this purpose, a two-step optimization model based on integer programming is developed in this paper to automatically identify an optimal schedule of the material handling activities of electric mobile MHEs (MMHEs) (i.e., forklifts) in labor-intensive warehouses from profit and sustainability perspectives. The resulting scheduling aims at minimizing the total cost, which is the sum of the penalty cost related to the makespan of the material handling activities and the total electricity cost of charging batteries. The approach ensures that jobs are executed in accordance with priority queuing and that the completion time of battery recharging is minimized. Realistic numerical experiments are conducted to evaluate the effects of integrating the scheduling of electric loads into the scheduling of material handling operations. The obtained results show the effectiveness of the model in identifying the optimal battery-charging schedule for a fleet of electric MMHEs from economic and environmental perspectives simultaneously.
... A segunda decisão, envolvendo as operações de separação de pedidos, refere-se às políticas de roteamento que são responsáveis por estabelecer a sequência em que as SKUs devem ser recuperadas [Elbert et al. 2017]. Os tipos mais empregados são: transversal, retorno e combinada [Chan & Chan 2011]. ...
... Essas políticas podem ser estabelecidas através de heurísticas ou procedimentos ótimos [Petersen 1999]. Contudo, Elbert et al. [2017] destacaram que procedimentos heurísticos são as mais populares, apesar de não garantir a obtenção de uma rota de comprimento mínimo ótimo. Além disso, Gu et al. [2007] argumentaram que a sequência é definida buscando, geralmente, minimizar o custo total com o manuseio de materiais. ...
... The results indicated that OPSs with standby units were less sensitive to availability. Elbert et al. 9 investigated the influence of human behavior on the efficiency of routing problems in OPSs. The researchers compared different routing policies and quantified the effects of deviations from predetermined routes using agent-based simulation. ...
... The second row represents the lower and upper limits of interval alteration values, calculated using Eqs. (9) and (10). The third row contains the lower and upper values of criterion weights calculated using Eqs. ...
Optimizing order-picking systems (OPSs) while considering human factors and integrating key decisions is a major challenge for warehouse managers. This study presents a two-stage framework based on multi-attribute decision-making (MADM) and multi-objective decision-making (MODM) models to integrate decisions on picker selection, order batching, batch assignment, picker routing, and scheduling. In the first stage, the human factors affecting picker selection are considered as the problem’s criteria and the available pickers are treated as alternatives. The fuzzy entropy method and fuzzy COmplex PRoportional ASsessment (COPRAS) are used to weight the factors and rank the pickers, respectively. In the second stage, a three-objective mathematical model is formulated to minimize makespan and the operating costs of picking while maximizing the total scores of the selected pickers. The improved augmented epsilon constraint method (AUGMECON2) and the non-dominated sorting genetic algorithm II (NSGA-II) are applied to solve the proposed model. The performance of the two methods is tested on well-known benchmark instances and a real-world case study. The NSGA-II algorithm can generate optimal results using only about 6.58% of the CPU time required by AUGMECON2 to solve the problem. Our computational experiments show that increasing the number of pickers from 2 to 8 and doubling their capacity reduces the makespan by 2.61% and 2.74%, respectively.
... This ensures that human pickers and robots are both capable of retrieving each item from the shelves. The warehouse has a rectangular shape and consists of 10 picking aisles, which has practical applicability and is frequently adopted in the literature (e.g., Elbert et al., 2017;. Each picking aisle is 3-m wide, which allows a maximum of two pickers moving in parallel (two-way lane). ...
... Three cross-aisles (each 3-m wide) exist at the front, back, and middle of the warehouse, separating the storage area into two parts (zones A and B). A single depot, as the start and end points of picking activities, is located in the middle of the front aisle (see also Elbert et al., 2017;Franzke et al., 2017). ...
... In batching, Kulak et al. (2012) and Glock et al. (2019) have studied the impact of physical aspect of order pickers, while Gue et al. (2006) have considered the impact of psychosocial aspect. Finally, regarding routing, Kulak et al. (2012) and Glock et al. (2019) have examined the impact of physical aspect while Franzke et al. (2017) and Elbert et al. (2017) have investigated the impact of mental one. ...
... Considering the physical aspect, Ho & Chien et al. (2006) Grosse et al. (2013) and Grosse & Glock (2015) have measured the mental workload based on learning and forgetting. Franzke et al. (2017) and Elbert et al (2017) have measured the mental workload based on behavior of order pickers. Considering the perceptual aspect, Brynzér & Johansson (1996) have measured the perceptual workload based on the information processing ability of order pickers. ...
Manual picker-to-parts order picking system had been considered as one of the most labor-intensive tasks in warehouses. These manual tasks might cause a negative impact on the order pickers with musculoskeletal disorders, low back pain, etc. which thereby led to reduced performance of the order picking system. Therefore, large companies have invested to automate the order picking systems, however some companies are not able to automate to the level of large companies because of the huge investment and risk. Thus, partially automated warehouses had come into play. Through a systematic literature review, this paper will introduce the state-of-the-art solutions that have been currently explored in picker-to-parts order picking systems. Thereafter, the paper provides some future research opportunities that could be investigated.
... This ensures that human pickers and robots are both capable of retrieving each item from the shelves. The warehouse has a rectangular shape and consists of 10 picking aisles, which has practical applicability and is frequently adopted in the literature (e.g., Elbert et al., 2017;. Each picking aisle is 3-m wide, which allows a maximum of two pickers moving in parallel (two-way lane). ...
... Three cross-aisles (each 3-m wide) exist at the front, back, and middle of the warehouse, separating the storage area into two parts (zones A and B). A single depot, as the start and end points of picking activities, is located in the middle of the front aisle (see also Elbert et al., 2017;Franzke et al., 2017). ...
Order picking is a labor-intensive and costly process in supply chains, which is performed manually in most cases. Recently, picking robots have been developed which are capable of working together with human pickers in a shared working space. Such hybrid order picking system can ease human pickers’ workload and provide ergonomic improvements, because it partially automates the order picking process. We propose a simulation model to measure the energy expenditure of human pickers who work with the support of picking robots. The hybrid order picking system is evaluated based on its operational costs, efficiency, and ergonomic characteristics. Preliminary results presented in this study show that there are assignment rules for items to workers and robots that reduce human energy expenditure and costs per pick, as well as maintain average throughput time at a certain level. The aim of this preliminary study is to closely analyze the hybrid order picking system, evaluate managerial implications, and detect research opportunities for future works.
... However, human factors like learning and fatiguing effect are generally overlooked. In recent years, some researchers have proposed to consider such ergonomic effects [8][9][10][11][12]. erefore, this study focuses on the special order picking system of fresh products with potential human fatiguing effects, which could be considered a practical exploration in this new area. ...
... Picking line ID 1-5 has a small initial unit picking time in range (1, 10). Picking line ID 6-10 has medium-scale initial unit picking time in range (11,20). Picking line ID 11-15 has a large initial unit picking time in range (21,30). ...
In this research, we study an extended version of the joint order batching and scheduling optimization for manual vegetable order picking and packing lines with consideration of workers’ fatiguing effect. This problem is faced by many B2C fresh produce grocers in China on a daily basis which could severely decrease overall workflow efficiency in distribution center and customer satisfaction. In this order batching and sequencing problem, the setup time for processing each batch is volume-dependent and similarity dependent, as less ergonomic motion is needed in replenishing and picking similar orders. In addition, each worker’s fatiguing effect, usually caused by late shift and repetitive operation, which affects order processing times, is assumed to follow a general form of logistic growth with respect to the start time of order processing. We develop a heuristic approach to solve the resultant NP-hard problem for minimization of the total completion time. For order batching, a revised similarity index takes into account not only the number of common items in any two orders but also the proportion of these items based on the vegetable order feature. To sequence batches, the genetic algorithm is adapted and improved with proposed several efficient initialization and precedence rules. Within each batch, a revised nondecreasing item quantity algorithm is used. The performance of the proposed heuristic solution approach is evaluated using numerical instances generated from practical warehouse operations of our partnering B2C grocer. The efficiency of the proposed heuristic approach is demonstrated.
... Congestion and waiting times are considered in [41], while [42] include preparation times, human energy expenditure, and fatigue during the picking process. Elbert et al. [43] analyze the relative efficiency of routing deviation policies. Ardjmand et al. [16] minimize makespan and total travel time in a pick-up system but do not consider due dates for individual orders nor the possibility of being earlier or later with respect to them. ...
This paper investigates the performance of a two-stage multi-criteria decision-making procedure for order scheduling problems. These problems are represented by a novel nonlinear mixed integer program. Hybridizations of three Multi-Objective Evolutionary Algorithms (MOEAs) based on dominance relations are studied and compared to solve small, medium, and large instances of the joint order batching and picking problem in storage systems with multiple blocks of two and three dimensions. The performance of these methods is compared using a set of well-known metrics and running an extensive battery of simulations based on a methodology widely used in the literature. The main contributions of this paper are (1) the hybridization of MOEAs to deal efficiently with the combination of orders in one or several picking tours, scheduling them for each picker, and (2) a multi-criteria approach to scheduling multiple picking teams for each wave of orders. Based on the experimental results obtained, it can be stated that, in environments with a large number of different items and orders with high variability in volume, the proposed approach can significantly reduce operating costs while allowing the decision-maker to anticipate the positioning of orders in the dispatch area.
... Battini et al. [34] consider human energy expenditure, which is affected by item characteristics, item popularity, order profiles, and physical dimensions of shelves, and integrate the energy expenditure rate into time estimation in order-picking optimization. Elbert et al. [35] research the effect of route deviation resulting from behavioral factors and evaluate order-picking efficiency, taking human pickers' behavior into account. In these studies, the physical aspects of human pickers have a major impact on the estimation of order-picking efficiency, since order-picking tasks consist of continuous physical activities. ...
The robotic mobile fulfillment (RMF) system is a parts-to-picker warehousing system and a sustainable technology used in human–robot collaborative order picking. Storage location assignment (SLA) tactically benefits order-picking efficiency. Most studies focus on the retrieval efficiency of robots to solve SLA problems. To further consider the crucial role played by human pickers in RMF systems, especially in the context that the sustainable performance of human workers should be paid attention to in human–robot collaboration, we solve the SLA problem by aiming to improve human–robot collaborative order-picking efficiency. This study specifically makes decisions on assigning multiple items of various products to the slots of pods in the RMF system, in which human behavioral factors are taken into account. To obtain the solution in one mathematical model, we propose the heuristic algorithm under a two-stage optimization method. The results show that assigning correlated products to pods improves the retrieval efficiency of robots compared to class-based assignment. We also find that assigning items of each product to slots of pods, considering behavioral factors, benefits the operation efficiency of human pickers compared to random assignment. Improving human–robot collaborative order-picking efficiency and increasing the capacity usage of pods benefits sustainable warehousing management.
... Dazu zählen bspw. Arbeiten, die kognitive menschliche Eigenschaften berücksichtigen, indem die Performance-Auswirkungen von menschlichem Lernen bei der Zuteilung von Mitarbeitern auf Lagerzonen (Grosse und Glock, 2013;2015;Stinson und Wehking, 2016) oder menschliches Verhalten bei der Ausführung und Umsetzung von Lageraufgaben (Elbert et al., 2017;de Vries et al., 2016) untersucht werden. Zudem existieren Ansätze, bei denen mitarbeiterspezifische Leistungswerte bei der Personaleinsatzplanung in der Intralogistik genutzt werden, um die Bearbeitungszeiten der Aufträge genauer prognostizieren zu können und Durchlaufzeiten zu reduzieren (Stinson et al., 2014;Matusiak et al., 2017). ...
... As HWs in cobotic operations cannot be centrally controlled, their self-interested behaviors are a major source of warehouse congestion. Glock et al. (2017) and Elbert et al. (2017) find that HWs deviate from guided routes to shortcuts that save walking time because they believe their own route choices are better than those suggested by the WMS. Daily operational data from one state-of-the-art warehouse shows that only 33 out of 68 (48%) human-operated forklift trips were compliant with the routing policy provided by the WMS (Halawa et al., 2020). ...
Motivated by the emerging mixed autonomous paradigm in cobotic order picking operations, we investigate the optimal information design to navigate human workers (HWs) who cooperate with autonomous mobile robots (AMRs) within an intralogistics system. We incorporate asymmetric information between AMRs and HWs in a routing game where connected AMRs are informed of the congestion state while HWs rely on information provided by the system. The system designs a communication policy aiming to navigate HWs away from congestion. Without strategic communications, we show that the deployment of AMRs cannot mitigate congestion unless the automation level reaches a threshold. Interestingly, we illustrate a substitution effect between automation and strategic communications when information distortion is mild. In contrast, severe information distortion complements automation due to exacerbated congestion. Furthermore, an in‐house AMR fleet is economically more efficient than a third‐party logistics service. Consequently, in‐house automation can be achieved with mild information distortion, while severe information distortion is required to complement the lack of efficiency in the third‐party AMR fleet. With simulated numerical examples to complement the analytical results, we provide managerial insights concerning the optimal information policies under different levels of automation, guiding warehouse managers in their communications with workers to achieve the best performance of the cobotic system.
... As a result, the human pickers may modify their own work schedule referred to this as "maverick picking"), which can negatively affect their performance. In addition, we refer to the work of Elbert et al. (2017) for a quantified analysis of human route deviations in order picking. Here, we apply four routing strategies only in the robot team in the HOPS with the assignment rule A/BC, namely, S-shape ("S"), return ("Re"), largest gap ("LG"), and aisle-by-aisle ("AbA"). ...
Warehouses are important nodes in almost every supply chain. Within warehouses, order picking is a crucial task that is extremely time- and cost-intensive. While order picking systems (OPSs) have traditionally been operated manually, new technologies offer opportunities for reducing the workload of warehouse workers. These technologies include autonomous picking robots that can function in combination with human pickers within a shared workspace. This technology enables human–robot collaboration and enhances flexibility in system design, as robots can either support humans or work independently. Research on the advantages of these hybrid OPSs (HOPSs) for improving operational performance is still scarce, however. To contribute to closing this research gap, we propose an agent-based simulation model to investigate how HOPSs reduce the daily workload of human order pickers. The results reveal that HOPSs – if certain assignment rules for the picking tasks are considered – can reduce both the operational costs of the system and human workload compared to a pure manual or a fully automated OPS. Nonetheless, attention should be paid to control the item weight pickers are supposed to handle, as HOPSs reduce the travel distance of human pickers, resulting in a higher frequency of picking activities and an increased ergonomic risk for musculoskeletal disorders.
... With respect to picking performance, the optimal routing policy is obviously superior to the other routing policies because it leads to shorter picking tours. However, a shortcoming of using the optimal routing policy is that pickers may get confused by the complex routes, and therefore, tend to deviate from optimal routing patterns (see, e.g., Elbert et al. 2017). Deviating from given routes can have a negative impact on picking efficiency and also increase the risk of Content courtesy of Springer Nature, terms of use apply. ...
The rapid and severe outbreak of COVID-19 caused by SARS-CoV-2 has heavily impacted warehouse operations around the world. In particular, picker-to-parts warehousing systems, in which human pickers collect requested items by moving from picking location to picking location, are very susceptible to the spread of infection among pickers because the latter generally work close to each other. This paper aims to mitigate the risk of infection in manual order picking. Given multiple pickers, each associated with a given sequence of picking tours for collecting the items specified by a picking order, we aim to execute the tours in a way that minimizes the time pickers simultaneously spend in the same picking aisles, but without changing the distance traveled by the pickers. To achieve this, we exploit the degrees of freedom induced by the fact that picking tours contain cycles which can be traversed in both directions, i.e., at the entry to each of these cycles, the decision makers can decide between the two possible directions. We formulate the resulting picking tour execution problem as a mixed integer program and propose an efficient iterated local search heuristic to solve it. In extensive numerical studies, we show that an average reduction of 50% of the total temporal overlap between pickers can be achieved compared to randomly executing the picking tours. Moreover, we compare our approach to a zone picking approach, in which infection risk between pickers can be almost eliminated. However, compared to our approach, the results show that the zone picking approach increases the makespan by up to 1066%.
... Out of the 32 papers (or series of papers) reviewed on order picking, 14 use heuristic algorithms and 8 use empirical optimization methods based on physical analogies. Interestingly, there is evidence that order pickers often deviate from the optimal routes assigned to them [44,45]. ...
This work presents a procedure to plan the storage, picking sequences, and picking paths in warehouses, focusing on small businesses that may not afford the construction of designed-for-purposes storage facilities or use sophisticated picking strategies. The procedure considers storage spaces of irregular shapes and assumes the use of a picker-to-parts strategy. It considers constraints, such as proximity constraints and comprises three nested loops. The innermost loop uses the NetworkX package to find the shortest path between two locations. The intermediate loop uses the traveling salesperson problem implementation of Google OR-Tools to find the optimal path to collect the items in a given order. The third loop uses the simulated annealing method for optimal storage allocations. A class-based approach to simulated annealing optimization proposed in this work systematically improves the storage/picking configuration.
... Therefore, this achieves optimum space utilization as well. (Elbert et al., 2017) has mainly focused on the case of human interaction in the manual order picking systems because human movement impact makes deviations from prescheduled routines at developed routines policies. The paper presents an agent simulation model to quantify the mentioned deviations due to human interactions. ...
Warehousing involves all the material handling activities that take place within a warehouse in a supply chain. Typical warehouse operations involve integrated assignment decisions that could be optimized. The review in this paper presents a comparative analysis of optimization models used for warehouse space allocation proposed in the recent literature. Through the review, it was identified that considerable challenges exist in applying these models at present, due to the vast amount of information required to be processed, the significant number of possible alternatives, and the degree of integration of decisions required in the modern warehousing context. The objectives of this paper are comparison of previous research related to the warehouse space optimization and demonstrate warehouse space optimization using linear programming (LP) and goal programming (GP). Therefore, it is expected that the paper serves as a reference for further research in the area. The paper proposes a simple and effective linear programming (LP) model and goal programming (GP) model to optimize warehouse storage space by efficient palletizing. The quantity of total pallets required per day is derived based on the available demand per day and other constraints related to warehousing operations in a multi-product manufacturing context. The models generated feasible solutions; all constraints are satisfied.
... In manual order picking systems, worker fatigue and workload can have a large impact on picking times and error rates. The large amount of human labor associated with order picking turns this activity into a time-consuming and costly step in the warehouse process (Elbert et al., 2017). ...
... Previous research showed that a considerable amount of the variability of performance resides within people (Dalal et al., 2009). Another example, Elbert et al. (2017) built a simulation model to quantify the effects of deviations from pre-specified routes in order picking on workers' performance. They confirmed that the individual attitude of workers can induce them to modify their routes, reducing their expected performance. ...
Purpose The purpose of the research is to conduct an exploratory investigation of the material handling activities of an Italian logistics hub. Wearable sensors and other smart tools were used for collecting human and environmental features during working activities. These factors were correlated with workers' performance and well-being. Design/methodology/approach Human and environmental factors play an important role in operations management activities since they significantly influence employees' performance, well-being and safety. Surprisingly, empirical studies about the impact of such aspects on logistics operations are still very limited. Trying to fill this gap, the research empirically explores human and environmental factors affecting the performance of logistics workers exploiting smart tools. Findings Results suggest that human attitudes, interactions, emotions and environmental conditions remarkably influence workers' performance and well-being, however, showing different relationships depending on individual characteristics of each worker. Practical implications The authors' research opens up new avenues for profiling employees and adopting an individualized human resource management, providing managers with an operational system capable to potentially check and improve workers' well-being and performance. Originality/value The originality of the study comes from the in-depth exploration of human and environmental factors using body-worn sensors during work activities, by recording individual, collaborative and environmental data in real-time. To the best of the authors' knowledge, the current paper is the first time that such a detailed analysis has been carried out in real-world logistics operations.
... Previous research showed that a considerable amount of the variability of performance resides within people (Dalal et al., 2009). Another example, Elbert et al. (2017) built a simulation model to quantify the effects of deviations from pre-specified routes in order picking on workers' performance. They confirmed that the individual attitude of workers can induce them to modify their routes, reducing their expected performance. ...
Purpose
The purpose of the research is to conduct an exploratory investigation of the material handling activities of an Italian logistics hub. Wearable sensors and other smart tools were used for collecting human and environmental features during working activities. These factors were correlated with workers' performance and well-being.
Design/methodology/approach
Human and environmental factors play an important role in operations management activities since they significantly influence employees' performance, well-being and safety. Surprisingly, empirical studies about the impact of such aspects on logistics operations are still very limited. Trying to fill this gap, the research empirically explores human and environmental factors affecting the performance of logistics workers exploiting smart tools.
Findings
Results suggest that human attitudes, interactions, emotions and environmental conditions remarkably influence workers' performance and well-being, however, showing different relationships depending on individual characteristics of each worker.
Practical implications
The authors' research opens up new avenues for profiling employees and adopting an individualized human resource management, providing managers with an operational system capable to potentially check and improve workers' well-being and performance.
Originality/value
The originality of the study comes from the in-depth exploration of human and environmental factors using body-worn sensors during work activities, by recording individual, collaborative and environmental data in real-time. To the best of the authors' knowledge, the current paper is the first time that such a detailed analysis has been carried out in real-world logistics operations.
... Damit dieser erfolgreich ist, muss er von den Beschäftigten vertretbar sein. Eine Studie von Elbert et al. (2017) zeigt, dass die Einführung eines Assistenzsystems zur Lagernavigation oft nicht zielführend eingesetzt werden konnte. Kommissionierer verfolgten weiter ihre gewohnten Routen und wichen von den optimalen Routen, die das System vorschlug, ab. ...
Zusammenfassung
Der E‑Commerce-Bereich erfährt aufgrund der Digitalisierung einen kontinuierlichen Aufschwung in der Logistik. Der Mensch bleibt vor dem Hintergrund der mehrheitlich manuell ausgeführten Tätigkeiten eine entscheidende Ressource im Lager, die es, mit besonderem Blick auf den demografischen Wandel, zu halten und zu integrieren gilt. Im Beitrag werden die Befragungsergebnisse einer Feldstudie mit dem Fokus auf den Bereich der Kommissionierung, die bei einem großen Versandhändler durchgeführt wurde, exemplarisch beschrieben und mit Befragungsergebnissen von anderen Betrieben verglichen. In Anlehnung an das Anforderungs-Ressourcen-Modell werden neben den Zielgrößen Arbeitsfähigkeit, Arbeitszufriedenheit und Gesundheit, Stressoren, Herausforderungen und Ressourcen beleuchtet. Die Ergebnisse deuten darauf hin, dass sich Stressoren und Arbeitsressourcen in etwa ausgleichen, auch wenn sich z. B. Arbeitszufriedenheit und Gesundheit lediglich auf einem mittleren Niveau befinden. Vor allem die Autonomie der Beschäftigten birgt noch Handlungsbedarf. Die digitale Transformation wird als Chance gesehen, den Handlungs- und Entscheidungsspielraum zu erweitern.
Praktische Relevanz: Die operative Logistik birgt aufgrund der manuell geprägten Tätigkeiten und der vorgegebenen Prozesse eine Vielzahl an Arbeitsanforderungen, die zu psychischer Fehlbeanspruchungen und langfristig zu Störungen oder Erkrankungen führen können. Zur Kompensation der Stressoren ist die Erweiterung von arbeitsbedingten Ressourcen notwendig. Die Digitalisierung wird als Chance gesehen, diese auszubauen.
... Finally, pickers follow an S-shape routing policy and packers are fixed at a packing station. The S-shape routing policy has been widely used in practice for years due to its simplicity and performance (Elbert et al., 2017;Scholz et al., 2017) and it is prevalent today (Hong & Kim, 2017;Žulj et al., 2018a). ...
Motivated by recent claims on the potential value of integration in warehouse management, this study evaluates the benefits arising from integrating the planning of order picking and packing processes in e-commerce warehouses. A set of research questions are proposed for exploring various benefits under different operational conditions and an experimental study is designed to answer them. In order to have a concrete model to represent the integrated planning method, a mixed-integer nonlinear programming model is developed, and then compared against a non-integrated variation. The experimental study makes the comparisons by analysing the collected empirical data from a real-life warehouse. Our findings indicate that integrated picking and packing planning can yield improved performance in different aspects under different configurations of objectives, order quantities, order categories or workforce allocation.
... In manual order picking systems, employee fatigue and workload can have a big impact on picking times and picking error rates. A large amount of human work related to order picking turns this activity into a time-consuming and costly stage in warehouse operations (Elbert et al., 2017). At the same time, there is empirical evidence that order pickers tend to deviate from optimal routes, which jeopardizes the effectiveness of various methods of planning picking routes for employees and putting them into practice. ...
... This model is then deemed ideal for assessing times in conventional 2-block warehouses [17]. Finally, a linear programming model identified a 15% opportunity for improvement within the routing issues reported by the warehouse being assessed [18]. ...
The outsourcing of logistics services has become an increasingly relevant practice for trading companies as it significantly improves their costs and productivity. However, service providers often fail to maintain standards that may guarantee adequate customer satisfaction levels. This is reflected in the logistics service quality issues faced by many Latin–American medium-sized logistics service providers. Within this context, the present research study aims to find a model that can diagnose the causes of these service quality issues in a timely manner. The proposed model uses operational mapping tools, simple information gathering, correlations, and control charts. In fact, the authors’ motivation for this study lies in conducting more complete assessments and reducing undiagnosed spaces along with increasing knowledge in the use of diagnostic tools as a whole within the logistics services sector.
... While simple routing heuristics lead to routing patterns that may be similar for subsequent orders and that are usually easy to memorise (albeit at the expense of longer travel distances), optimal routes may differ quite substantially from order to order. Prior research has therefore discovered that order pickers may find optimal routes confusing, inducing them to deviate from these routes Elbert et al. 2017;Gademann and Velde 2005). To make it easier for order pickers to correctly execute order picking tours, warehouses can use handheld devices for communicating the tours to the order pickers. ...
Artificial lighting is a constant companion in everyday private and working life, influencing visibility in interior spaces as well as outdoors. In recent years, new technical solutions have extended traditional lighting systems to become ‘smart’. Different types of smart lighting systems are available on the market today, and researchers have concentrated on analyzing their usability and efficiency, especially for private households, office buildings and public streets. This paper presents a systematic literature review to analyze the state-of-knowledge of technologies and applications for smart lighting systems. The results of the review show that smart lighting systems have been frequently discussed in the literature, but that their potentials in industrial environments, such as production and logistics, has rarely been addressed in the literature so far. Lighting systems for industrial environments often have very different requirements depending on the working environment and operating conditions. Based on the results of the literature review, this paper contributes to closing this research gap by discussing the usage potential of smart lighting systems to improve the efficiency of warehouse order picking, which is an application that may benefit from various functions smart lighting systems provide. Several propositions are developed that emphasize research opportunities and managerial implications in this context.
... For example, Arentze et al. [18] examined the route choice behaviors of truck drivers considering various road attributes and pricing policies. Elbert et al. [19] evaluated the effects of human behavior on the routing efficiency of order picking in a warehouse in terms of the frequently observe route deviation. Moussavi et al. [20] designed a metaheuristic approach to handle the integration of worker assignment and vehicle routing problems in the context of home healthcare scheduling. ...
The order assignment in the food delivery industry is of high complexity due to the uneven distribution of order requirements and the large-scale optimization of workforce resources. The delivery performance of employees varies in different conditions, which further exacerbates the difficulty of order assignment optimization. In this research, a non-linear multi-objective optimization model is proposed with human factor considerations in terms of both deteriorating effect and learning effect, in order to acquire the optimal solutions in practice. The objectives comprised the minimization of the operational cost in multiple periods and the workload balancing among multiple employees. The proposed model is further transformed to a standardized mixed-integer linear model by the exploitation of linearization procedures and normalization operations. Numerical experiments show that the proposed model can be easily solved using commercial optimization softwares. The results indicate that the variance of employee performance can affect the entire delivery performance, and significant improvement of workload balancing can be achieved at the price of slight increase of the operational cost. The proposed model can facilitate the decision-making process of order assignment and workforce scheduling in the food delivery industry. Moreover, it can provide managerial insights for other labor-intensive service-oriented industries.
... Companies have strong interests in preventing or reducing these work flow modifications as they often negatively impact order picking operations. Elbert et al. (2017) take into account the possible consequences of maverick picking in order to check which routing policy is most efficient. The study concludes that even if pickers deviate from a given route, the optimal routing policy is still the best option in most scenarios. ...
Market trends such as globalisation, increasing customer expectations, expensive industrial land and high labour costs cause a need for efficient order picking systems in practice. However, managers often do not implement findings from academic research on order picking planning into practice because researchers hardly account for practical factors (e.g., high-level storage, human factors, pick vehicle properties) or make unrealistic assumptions in their solution algorithms. A state-of-the-art review of the scientific literature on order picking planning (1) identifies and classifies highly influential practical factors, (2) shows the impact of these practical factors on order picking performance, and (3) illustrates how existing order picking planning models should be elaborated to account for practical factors. This study contributes to close the gap between research and practice by guiding future researchers to further increase the practical applicability of their research results.
... A critical problem experienced in retail warehouses is to decide on an order picking policy aimed at pursuing this objective [2]. The development of high-quality order picking policies for retail warehouses by exact or heuristic optimisation means has therefore been among the top retail research topics in recent years [1]. ...
... Previous research showed that a considerable amount of the variability of performance resides within people (Dalal et al., 2009). Another example, Elbert et al. (2017) built a simulation model to quantify the effects of deviations from pre-specified routes in order picking on workers' performance. They confirmed that the individual attitude of workers can induce them to modify their routes, reducing their expected performance. ...
... Previous research showed that a considerable amount of the variability of performance resides within people (Dalal et al., 2009). Another example, Elbert et al. (2017) built a simulation model to quantify the effects of deviations from pre-specified routes in order picking on workers' performance. They confirmed that the individual attitude of workers can induce them to modify their routes, reducing their expected performance. ...
This work aims to explore the environmental and human factors affecting productivity of warehouse operators in material handling activities. The study was carried out in a semi-automated logistic hub and the data collection has been conducted using wearable sensors able to detect human-related variables such as heart rate and human interactions, based on a smartwatch combined with a mobile application developed by the MIT Center for Collective Intelligence. Preliminary analysis has shown that the interaction between the warehouse operators and the team leader significantly affects the productivity.
... Conventionally, performing order picking with paper lists is intuitive for human beings but laborious to handle, and the physical fatigue of workers derived from repeated picking operations may result in deviations from predefined picking performance [3]. The manual order picking becomes under increasing consideration because it has been identified as the most labor-intensive activity for almost every warehouse [4,5]. ...
In manual order picking systems, a paper list specifying the name, location, and amount of each item is often given, and this would exert mental pressure on pickers while finding target shelf bins in high-density picking environments. Augmented reality (AR) can provide a friendly alternative to improve the manual order picking performance by conveying picking information into visual instruction. Nevertheless, there is still no systematic consensus to deploy the pick-by-AR associated with actual warehouse workplaces. To establish a spatial correspondence between the visual guidance with the actual workplace, the multi-marker-based global map about the warehouse floor is established in advance. Instead of traditional single marker-based pick-by-AR methods, the warehouse floor-related map can provide an accurate and continuous navigation performance for intuitive AR guidance, allowing the picker to move freely on the warehouse floor without limiting to some certain locations. Besides, a systematic pick-by-AR solution is available by integrating the proposed method to a lightweight wearable AR device, and this easy-to-deploy pick-by-AR solution can alleviate the worker’s mental effort while doing picking action. Finally, the pick-by-AR system described in the paper is deployed in the automobile assembly line, and experimental results illustrate that the proposed method can improve the picking efficiency while reducing the picking errors compared with the current paper-based order picking.
... Previous research showed that a considerable amount of the variability of performance resides within people (Dalal et al., 2009). Another example, Elbert et al. (2017) built a simulation model to quantify the effects of deviations from pre-specified routes in order picking on workers' performance. They confirmed that the individual attitude of workers can induce them to modify their routes, reducing their expected performance. ...
... The paper at hand contributes to the further development of algorithms for optimal order picker routing, whose nonavailability for many warehouse layouts has frequently been cited as an obstacle in the improvement of order picking efficiency (Elbert et al. 2017). The paper considers a conventional warehouse with two blocks and adapts the solution procedures presented in Ratliff and Rosenthal (1983) and Roodbergen and de Koster (2001a) to this new scenario. ...
This paper investigates manual order picking, where workers travel through the warehouse to retrieve requested items from shelves. To minimise the completion time of orders, researchers have developed various routing procedures that guide order pickers through the warehouse. The paper at hand contributes to this stream of research and proposes an optimal order picker routing policy for a conventional warehouse with two blocks and arbitrary starting and ending points of a tour. The procedure proposed in this paper extends an earlier work of Löffler et al. (2018. Picker routing in AGV-assisted order picking systems, Working Paper, DPO-01/2018, Deutsche Post Chair-Optimization of Distribution Networks, RWTH Aachen University, 2018) by applying the concepts of Ratliff and Rosenthal (1983. “Order-picking in a Rectangular Warehouse: a Solvable Case of the Traveling Salesman Problem.” Operations Research 31 (3): 507–521) and Roodbergen and de Koster (2001a. “Routing Order Pickers in a Warehouse with a Middle Aisle.” European Journal of Operational Research 133 (1): 32–43) that used graph theory and dynamic programming for finding an optimal picker route. We also propose a routing heuristic, denoted S*-shape, for conventional two-block warehouses with arbitrary starting and ending points of a tour. In computational experiments, we compare the average order picking tour length in a conventional warehouse with a single block to the case of a conventional warehouse with two blocks to assess the impact of the middle cross aisle on the performance of the warehouse. Furthermore, we evaluate the performance of the S*-shape heuristic by comparing it to the exact algorithm proposed in this study.
... Customer orders need to be retrieved and shipped in a timely and efficient way within these limited windows. Previous studies have focussed on layout, storage, routing and batching planning problems (Elbert et al., 2017;Van Gils et al., 2018a;Žulj et al., 2018). The literature review of Van Gils et al. (2018b) concludes that there remains a need to account for more reallife issues when optimising order picking: high demand and planning uncertainties pose new challenges for planning order picking operations such as workload balancing (Boysen et al., 2018;Wruck et al., 2017). ...
A growing e-commerce market and increasing customer requirements put extra pressure on order picking operations. Collecting large quantities of relative small orders within limited time windows makes workload balancing in order picking a challenging and complicated task. Therefore, warehouse managers experience difficulties in balancing the daily workload of order pickers in every pick zone. This paper introduces the operational workload balancing problem within the domain of order picking. A mathematical model is introduced to describe the new order picking planning problem. Furthermore, an iterated local search algorithm is developed to solve the operational workload balancing problem efficiently and effectively. The benefits of daily workload balancing in order picking operations are analysed by means of a real-life case.
Drones have received growing attention in logistics recently. One possible application is deploying drones for auditing inventory in warehouses. With the use of drones, warehouses are able to increase inventory record accuracy and decrease labor costs. In this research, we introduce the stocktaking drone routing problem (STDRP), which consists of routing a fleet of drones through a warehouse for stocktaking purposes as well as deciding on the location of charging stations on the warehouse floor, which is necessary due to the limited battery capacity of the drones. Subsequently, we develop an adaptive large neighbourhood search-based heuristic (ALNS) with novel solution encoding and decoding approaches to solve the STDRP. In a numerical study, we show that ALNS can solve realistic instances in reasonable time. We also derive recommendations regarding the ideal size of the drone fleet, the charging infrastructure, and battery capacity. Finally, we investigate the interplay between the storage assignment policy (such as the popular ABC rule) and stocktaking efficiency using drones.
The order-picking process has a significant impact on both the overall logistics costs as well as the customer service level, and as a result, is characterized as one of the highest priority warehouse activities for productivity and performance improvements. Although various existing studies focus on the design of order-picking systems as well as on a series of decision-making problems, there is still a need for a holistic framework that will map and categorize the parameters (e.g. total/forward area design, order-picking equipment, storage assignment policies, level of picking locations, order picking technologies, resource planning characteristics, order picking operational policies, routing strategies, product characteristics, order profile, etc.) that should be considered by researchers and practitioners for the design, control, and evaluation of order picking systems. To this end, the aim of this paper is to provide a state-of-the-art classification and review of parameters by adopting the Systematic Literature Review (SLR) approach. Overall, 389 articles were reviewed, and the identified parameters are classified into three categories: order-picking system design, order-picking system control, and order-picking system evaluation. Furthermore, this literature review aims to present managerial implications that directly affect the successful design of order-picking systems and identify a future research agenda.
Companies’ productivity is critical in contemporary warehouse environment to maintain efficiency and competitiveness within their supply chain. Warehousing operations are well known for their impact on the overall supply chain and need to be wisely managed. Among those activities, allocation planning and layout design are some of the most important concepts in practice. Current research tends to emphasize homogeneous environment, which leaves heterogeneous and non-standard cases with less attention, even today. This paper aims to review the literature regarding warehouse allocation planning and layout design methods that could suit practical industry problems, with a focus on heterogeneous and non-standard spare parts. It also aims to describe the current trends in these fields. Following a literature review methodology, a total of fifty-seven articles were reviewed to identify the methods developed and used. The reviewed papers were also investigated in order to identify research gaps and future directions. The analysis concluded that more research is needed to better understand and optimize heterogeneous and non-standard spare parts environments in terms of allocation and layout design. More practical case applications also remain a gap to address.
Article highlights
This article evaluates 8 concepts/parameters related to warehouse allocation operation. This is to ensure a deep analysis of heterogeneous and non-standard parts presence in literature.
A research gap is identified regarding heterogeneous and non-standard parts in warehouse allocation and layout design methods in the scientific literature.
This article evaluates 3798 scientific papers over a 20-year timeframe. From this, we present 57 methods and 8 gaps in scientific literature.
This chapter details the performance evaluation of routing policies and proposes a routing heuristic to determine the minimum traveled distance for different warehouse configurations and pick-list sizes. Numerical experiments are performed considering warehouse configurations used in literature and list sizes are chosen proportional to the number of storage positions of each layout. The proposed heuristic method was shown to reduce the distance traveled by 7% for the evaluated instances. Furthermore, travel distance reductions of up to 30% were found in cases involving large warehouse and pick-list sizes. The proposed heuristic therefore is concluded to provide a more efficient solution than individual routing policies for the picker routing problem.
Centralised warehouses are a widespread practice in the healthcare supply chain, as they allow for the storage of large quantities of products a short distance from hospitals and pharmacies, allowing both a reduction in warehouse costs and prompt replenishment in case of shortages. However, for this practice to lead to effective optimization, warehouses need to be equipped with efficient order fulfillment and picking strategies, as well as a storage policy that takes into account the specific procedures of the various types of items, necessary to preserve their quality. In this framework, we present a storage location assignment problem with product-cell incompatibility and isolation constraints, modeling goals and restrictions of a storage policy in a pharmaceutical warehouse. In this problem, the total distance that order pickers travel to retrieve all required items in a set of orders must be minimised. An Iterated Local Search algorithm is proposed to solve the problem, numerical experiments based on simulated data are presented, and a detailed procedure is provided on how to retrieve and structure the warehouse layout input data. The results show a dramatic improvement over a greedy full turnover procedure commonly adopted in real-life operations.KeywordsStorage allocationHealthcare supply chainIterated local searchWarehouse management
This paper presents an evaluation of the usability, functionality, and usefulness of the Warehouse Error Prevention (WEP) tool that consists of seven modules. The WEP tool is framed in a simple yes/no form, which can be used to identify human factors related to sources of pick errors in a warehouse. Thirty-three participants in 27 organisations from three different countries participated in a trial application and evaluation of the tool. The evaluation included a survey study and semi-structured interviews. Survey results show that participants agreed on the usability and functionality of the WEP tool. In the interviews, participants generally reported the WEP tool as being both accurate and functional with the potential to support engineers, ergonomists, and warehouse managers to improve order picking quality. Further quantitative field testing of the WEP tool’s potential to identify costly warehouse errors is needed.
A literature review on the order picking process in warehouses is presented for delineating the trends in time of research topics in this field. A total of 269 journal papers published between 2007 and 2022 were retrieved from Scopus. After a methodological classification, descriptive analyses were performed on authors, journals, subject area and top publishing countries. Bibliometric tools were used to map the topics covered by the reviewed studies, categorise them and determine possible relationships. Papers’ contents were evaluated in terms of eight categories, including five typical issues of order picking systems, plus three aspects dealing with the characteristics of the application. Insights about the extent to which these aspects have been covered in the literature are derived; relationships between the various aspects of the picking process are also delineated. Suggestions for future research activities are finally deducted, offering researchers and practitioners strong bases for works on order picking systems.
Many companies, despite there being opportunities for automation in production and logistics (P&L) systems, still rely on human workers due to their cognitive and motor skills. Taking Human Factor (HF) aspects into consideration when making P&L system design and management decisions is therefore important, an ignorance of HF potentially resulting in operator fatigue, discomfort, subsequent injuries and negative consequences for operator performance and the P&L system. A review of the literature shows that the majority of studies that take HF into consideration focus either on designing the workplace or on operation planning activities. There is also still a gap in the literature. Little has been published on P&L systems that incorporate HF and that combine different levels of short-term operational policy decisions (e.g. job allocation) and long-term system characteristic decisions (e.g. layout design). Current state-of-the-art frameworks that support the design and management of P&L systems and that take HF into consideration rarely consider different decision levels. This study proposes a new framework that incorporates HF into P&L systems by combining different levels of decisions to improve performance, quality, and well-being.
At the core of every high-performing warehouse is an efficient order picking (OP) system. To attain such a system, policy choices should be carefully aligned with subjects responsible for the actual picking within the established system. Despite recent advancements in automating the picking process due to Industry 4.0, human operators will continue to play a crucial role in the future of warehousing. However, unlike robots, human operators have specific skills, conduct, and perceptions, which are only partly accounted for in current planning models. This review adopts a multimethod approach to identify and analyse how these phenomena are currently integrated into OP planning problems. In addition, we assess the relevance and adequacy of human factors modelling in academic literature with practice-based insights gathered via semi-structured interviews. This leads to five major human factors integration constructs and dedicated recommendations on how to refine them. We then take the analysis one step further and make suggestions on how to integrate these constructs with leading research methodologies in the context of Industry 5.0. The results highlight the prevalent need to increasingly account for psychosocial phenomena and their impact on operational performance. Future research opportunities provide a substantiated foundation to assist in human-centric work design.
Full Eprint version available at: https://www.tandfonline.com/eprint/IYNP2GBWXFGC45CRQWDJ/full?target=10.1080/00207543.2022.2079437
Market trends such as globalisation, increasing customer expectations, expensive industrial land and high labour costs cause a need for efficient order picking systems in practice. However, managers often do not implement findings from academic research on order picking planning into practice because researchers hardly account for practical factors (e.g. high-level storage, human factors, pick vehicle properties) or make unrealistic assumptions in their solution algorithms. A state-of-the-art review of the scientific literature on order picking planning (1) identifies and classifies highly influential practical factors, (2) shows the impact of these practical factors on order picking performance, and (3) illustrates how existing order picking planning models should be elaborated to account for practical factors. This study contributes to close the gap between research and practice by guiding future researchers to further increase the practical applicability of their research results.
Order picking is a key process in supply chains and a determinant of business success in many industries. Order picking is still performed manually by human operators in most companies; however, there are also increasingly more technologies available to automate order picking processes or to support human order pickers.
One concept that has not attracted much research attention so far is hybrid order picking where automated and autonomous robots and human order pickers work together in warehouses within a shared workspace for a joint target. This study presents a simulation model that considers various system characteristics and parameters of hybrid order picking systems, such as picker blocking, to evaluate the performance of such systems. Our results show that hybrid order picking is generally capable of improving pure manual or automated order picking operations in terms of throughput and total costs. Based on the simulation results, promising future research potentials are discussed.
Order picking (OP) is a time- and labour-intensive operation in which human-system errors can lead to deficiencies in quality. This study aimed to identify human factors-related failure modes that cause human-system errors and quality deficits in OP. We conducted a systematic literature review and qualitative interviews with 38 order pickers employed by 14 different companies in four countries.
The literature review found 46 papers that identified eight different failure modes related to OP system design: physical workload, physical fatigue, mental fatigue, complexity, memory demand, vision, hearing, and motivation. The interview results confirmed many of the same factors noted in the literature review but also identified communication and supervision failure modes that had not been addressed before. The results illustrate human factors-related failure modes linked to OP system design, operation, and management that ultimately increase quality deficits and pick errors. Further research on human factors and OP system design interaction is warranted, as no assessment tool has been found to support engineers and managers seeking to improve system designs to reduce pick errors.
Companies have been trying continuously to reduce their logistics costs in the current competitive markets. Warehouses are important components of the logistics systems and they must be managed effectively and efficiently to reduce the production cost as well as maintain customer satisfaction. Order-picking is the core of warehouse operations and an order-picking system (OPS) is essential to meet customer needs and orders. Failure to perform the OPS process properly results in high costs and customer dissatisfaction. This research aims to investigate the state of the art in the adoption of OPS and provide a broad systemic analysis on main operating strategies such as simultaneous consideration of order assignment, batching, sequencing, tardiness, and routing need. This study reviews 92 articles, classifies combinations of tactical and operational OPS problems, and provides guidelines on how warehouse managers can benefit from combining planning problems, in order to design efficient OPS and improve customer service. Combining multiple order-picking planning problems results in substantial efficiency benefits, which are required to face new market developments.
Order picking, the process of retrieving items from their storage locations to fulfil customer orders, ranks among the most labour- and time-intensive processes in warehousing. Prior research in this area had a strong focus on the development of operating policies that increase the efficiency of manual order picking, for example by calculating optimal routes for the order pickers or by assigning products to storage locations. One aspect that poses a major challenge to many warehouse managers in practice has, curiously enough, remained largely unexplored by academic research: modifications in workflows (i.e., workplace deviance in a positive or negative sense) in order picking, which we define as “maverick picking”.
The purpose of this paper is to characterize maverick picking and to study its causes, its forms of appearance, and its potential impact on order picking performance. To gain insights into maverick picking, we first survey the literature to illustrate the state-of-knowledge of maverick picking. Subsequently, we report the results of a multi-case study on maverick picking and deduct a related content framework. The results of our case study support the proposition that maverick picking is highly relevant in practice and that it is a major determinant of order picking performance.
Order picking (OP) is one of the most labour- and time-intensive processes in internal logistics. Over the last decades, researchers have developed various mathematical planning models that help to increase the efficiency of OP systems, for example, by optimising storage assignments or by specifying routes for the order pickers that minimise travel distance in the warehouse. Human characteristics that are often a major determinant of OP system performance have, however, widely been ignored in this stream of research. This paper systematically evaluates the literature on manual OP systems and conducts a content analysis to gain insights into how human factors (HF) have been considered and discussed in the scientific literature. The results of the analysis indicate that management-oriented efficiency criteria dominated prior research on OP, and that there is a clear lack of attention to HF in the design and management of OP systems. This poses an opportunity for research and design of manual OP systems.
Humans are at the heart of crucial processes in warehouses. Besides the common economic goal of minimising cycle times, we therefore add in this paper the human well-being goal of minimising workers’ discomfort in the context of order picking. We propose a methodology for identifying the most suitable storage location solutions with respect to both goals. The first step in our methodology is to build data-driven empirical models for estimating cycle times and workers’ discomfort. The second step of the methodology entails the use of these empirically grounded models to formulate a bi-objective assignment problem for assigning products to storage locations. The developed methodology is subsequently tested on two actual warehouses. The results of these practical tests show that clear trade-offs exist and that optimising only for discomfort can be costly in terms of cycle time. Based on the results, we provide practical guidelines for taking storage assignment decisions that simultaneously address discomfort and travel distance considerations.
The three modelling methods or paradigms (system dynamics (SD) paradigm, discrete-event (DE) modelling paradigm, and agent-based (AB) model), that exist today are essentially the three different viewpoints the modeller can take when mapping the real-world system to its image in the world of models. Depending on the simulation project goals, the available data, and the nature of the system being modelled, different problems may call for different methods. This chapter offers an overview of the most used multi-method model architectures, and discusses the technical aspects of linking different methods within one model. It also discusses three examples of multi-method models, namely: (i) consumer market and supply chain; (ii) epidemic and clinic; and (iii) product portfolio and investment policy. The example models are described at a very detailed level so that they can easily be reproduced in AnyLogic development environment.
The effect of human factors on the performance of labour-intensive order picking processes has thus far been relatively understudied in the operations and logistics management literature. This technical note offers guidance to researchers and managers regarding how qualitative methods can be used to assess human factors in order picking. The paper first discusses manual tasks in this process and highlights where human factors influence the outcomes of time, quality and worker health. This discussion is used to inform the development of a qualitative example interview guide to investigate the order picking system. The paper provides step-by-step guidance for using interviewing to assist researchers and logistics managers that emphasises considering human factors in the planning of order picking processes. Using qualitative methods to integrate human factors into order picking processes can help to avoid workers’ exposure to musculoskeletal disorders and improve the quality and efficiency of order picking systems.
Order pickers and individual differences between them could have a substantial impact on picking performance, but are largely ignored in studies on order picking. This paper explores the role of individual differences in picking performance with various picking tools (pick by voice, RF-terminal picking and pick to light) and methods (parallel, zone and dynamic zone picking). A unique realistic field experiment with 101 participants (academic students, vocational students and professional pickers) is employed to investigate the influence of individual differences, especially the Big Five personality traits, on picking performance in terms of productivity and quality. The results suggest that (PbV) performs better than RF-terminal picking, and that Neuroticism, Extraversion, Conscientiousness and the age of the picker play a significant role in predicting picking performance with voice and RF-terminals. Furthermore, achieving higher productivity appears to be possible without sacrificing quality. Managers can increase picking performance by incorporating the insights in assigning the right pickers to work with a particular picking tool or method, leading to increased picking performance and reduced warehousing costs.
The order picking process is often the single largest expense in a distribution centre (DC). The DC considered in this paper uses a picking line configuration to perform order picking. The number of pickers in a picking line, and the initial arrangement of stock-keeping units (SKUs), are two important factors that affect the total completion time of the picking lines. In this paper, the picking line configuration is simulated with an agent-based approach to describe the behaviour of an individual picker. The simulation is then used to analyse the effect of the number of pickers and the SKU arrangement. Verification and validation of this model shows that the model represents the real-world picking line to a satisfactory degree. Marginal analysis (MA) was chosen to determine a 'good' number of pickers by means of the simulation model. A look-up table is presented to provide decision support for the choice of a 'good' number of pickers to improve completion times of the picking line, for the properties of a specific picking line. The initial SKU arrangement on a picking line is shown to be a factor that can affect the level of picker congestion and the total completion time. The greedy ranking and partitioning (GRP) and organ pipe arrangement (OPA) techniques from the literature, as well as the historical SKU arrangements used by the retailer under consideration, were compared with the proposed classroom discipline heuristic (CDH) for SKU arrangement. It was found that the CDH provides an more even spread of SKUs that are picked most frequently, thus decreasing congestion and total completion time. OPSOMMING
The main purpose of this research is to provide an agenda for future warehousing research relevant for both academic development and practitioners’ needs. In order to suggest a practically relevant future research agenda, first a comprehensive literature review was performed to identify research areas covered in the literature. Then, 15 warehouse managers and senior consultants were interviewed to add empirical input to the development of potential future research areas. The literature review reveals gaps, both methodology- and topic-wise. A considerable methodological imbalance is observed. Some of the highlighted managerial concerns have been investigated in the literature extensively, but the managerial concerns emphasized mostly do not belong to the most researched categories. While most of the practitioners’ concerns relate to supportive aspects of warehousing business, a relatively high number of the reviewed studies highlight operational problems. The suggested future research agenda highlights the importance of supportive aspects of the warehousing business, employment of real data in analysis and empirical research methods. The insights from practitioners stress the expected trends of business environment such as more volatile demand, higher desire for customized services and more expansion of e-commerce.
Order picking (OP) activities, essential to logistics operations, are laborious and time-intensive. Humans are central actors in the OP process and determine both OP effectiveness and efficiency. Many researchers have developed models for planning OP activities and increasing the efficiencies of such systems by suggesting different warehouse layouts, OP routes or storage assignments. These studies have, however, ignored workers’ characteristics, or human factors, suggesting that they cannot be substantiated, which led to only partially realistic results. This paper proposes a conceptual framework for integrating human factors into planning models of OP activities and hypothesises that doing so improves the performance of an OP system and workers’ welfare. The framework is based on a systematic literature review that synthesises findings documented in the OP and human factors literature. The results of the paper may assist researchers and practitioners in designing OP systems by developing planning models that help in enhancing performance and reducing long-term costs caused by work-related inefficiencies.
The paper presents an analytical approach to layout design of the picking area in low-level, picker-to-part systems using COI (cube per order index)-based and random storage policies. The layout of the picking area is one of the major issues in increasing picking system productivity, i.e. in reducing the time required to complete a given set of orders, and must take account of the inter-relationship between the main operating policies, i.e. storage, routing and batching. The main system parameters affecting the layout design are the total length of the picking aisles, the number of pick stops per tour and the shape of the COI-based ABC curve. A formula that relates the optimal number of aisles to the above parameters will be presented, together with the increase in the expected tour distance stemming from the adoption of a non-optimal number of aisles. The study thus provides a comprehensive framework for layout design.
The paper evaluates and compares the expected travel distance for different routing strategies- namely traversal and return policies in low-level pickerto-part systems. Items are assigned to storage locations on the basis of the ratio of the required space to the order frequency (cube-per-order index or COI). The focus is on narrow-aisle systems, in which the distance travelled crossing the aisle from one side to the other is negligible compared to the distance travelled along the centreline of the aisle. For both routing policies, an efficient COI-based stock location assignment strategy is first developed. Second, analytical models are derived which relate the expected travel distance required to fill an order to the main system parameters (i.e. the COI-based ABC curve; the number of picks in a tour; the number, length and width of aisles). Simulation results confirming the accuracy of the analytical models are presented. Finally, preference regions as a function of the number of picks in a tour and differently skewed COI-based ABC curves are given for traversal and return policies.
In manual order picking systems, order pickers walk or drive through a distribution warehouse in
order to collect items which are requested by (internal or external) customers. In order to perform these
operations efficiently, it is usually required that customer orders are combined into (more substantial)
picking orders of limited size. The Order Batching Problem considered in this paper deals with the
question of how a given set of customer orders should be combined such that the total length of all
tours is minimized which are necessary to collect all items. The authors introduce two metaheuristic
approaches for the solution of this problem: the first one is based on Iterated Local Search; the second
on Ant Colony Optimization. In a series of extensive numerical experiments, the newly developed
approaches are benchmarked against classic solution methods. It is demonstrated that the proposed
methods are not only superior to existing methods but provide solutions which may allow distribution
warehouses to be operated significantly more efficiently.
Order picking is the warehousing process by which products are retrieved from their storage locations in response to customers' orders. Its efficiency can be influenced through the layout of the area and the operating policies. We present a model that minimizes travel distances in the picking area by identifying an appropriate layout structure consisting of one or more blocks of parallel aisles. The model has been developed for one commonly used routing policy, but it is shown to be fairly accurate for some other routing policies as well.
This paper describes an approach to determine a layout for the order picking area in warehouses, so that the average travel distance for the order pickers is minimized. We give analytical formulas that can be used to calculate the average length of an order picking route under two different routing policies. The optimal layout can be determined by using these formulas as the objective function in a nonlinear programming model. The optimal number of aisles in an order picking area appears to depend strongly on the required storage space and the pick list size.
Operations management (OM) and human resources management (HRM) historically have been very separate fields. In practice, operations managers and human resource managers interact primarily on administrative issues regarding payroll and other matters. In academia, the two subjects are studied by separate communities of scholars publishing in disjoint sets of journals, drawing on mostly separate disciplinary foundations. Yet, operations and human resources are intimately related at a fundamental level. Operations are the context that often explains or moderates the effects of human resource activities such as pay, training, communications, and staffing. Human responses to OM systems often explain variations or anomalies that would otherwise be treated as randomness or error variance in traditional operations research models. In this paper, we probe the interface between operations and human resources by examining how human considerations affect classical OM results and how operational considerations affect classical HRM results. We then propose a unifying framework for identifying new research opportunities at the intersection of the two fields.
This work addresses the problem of order-picking in a rectangular warehouse that contains crossovers only at the ends of aisles. An algorithm is presented for picking an order in minimum time. The computational effort required is linear in the number of aisles. The procedure has been implemented on a microcomputer. A 50-aisle problem requires only about 1 minute to solve.
This paper considers routing and layout issues for parallel aisle warehouses. In such ware- houses order pickers walk or drive along the aisles to pick products from storage. They can change aisles at a number of cross aisles. These cross aisles are usually located at the front and back of the warehouse, but there can also be one or more cross aisles at positions in between. We describe a number of heuristics to determine order picking routes in a warehouse with two or more cross aisles. To analyse the performance of the heuristics, a branch-and-bound algorithm is used that generates shortest order picking routes. Performance comparisons between heuristics and the branch-and-bound algorithm are given for various warehouse layouts and order sizes. For the majority of the instances with more than two cross aisles, a newly developed heuristic appears to perform better than the existing heuristics. Furthermore, some consequences for layout are discussed. From the results it appears that the addition of cross aisles to the warehouse layout can decrease handling time of the orders by lowering average travel times. However, adding a large number of cross aisles may increase average travel times because the space occupied by the cross aisles has to be traversed as well.
Purpose
This paper aims to develop a scheduler for multiple picking agents in a warehouse that takes into account distance and loading queue delay minimization within the context of minimizing makespan (i.e. picking time).
Design/methodology/approach
The paper uses tabu search to solve the scheduling problem in a more global sense. Each search iteration is enhanced by a custom local search (LS) procedure that hastens convergence by driving a given schedule configuration quickly to a local minimum. In particular, basic operators transfer demand among agents to balance load and minimize makespan. The new load distribution is further improved by considering a vehicle‐routing problem on the picking assignments of the agents with relocated demands. Loading queue delays that may arise from the reassignments are systematically minimized using a fast scheduling heuristic.
Findings
The proposed tabu scheduler greatly improves over a widely practiced scheduling procedure for the given problem. Variants of the tabu scheduler produce solutions that are roughly of the same quality but exhibit considerable differences in computational time.
Research limitations/implications
The proposed methodology is applicable only to the static scheduling problem where all inputs are known beforehand. Furthermore, of the possible delays during picking, only loading queues are explicitly addressed (although this is justifiable, given that these delays are dominant in the problem).
Practical implications
The proposed approach can significantly increase through‐put and productivity in picking systems that utilize multiple intelligent agents (human pickers included), e.g. in warehouses/distribution centers.
Originality/value
The paper addresses a practical scheduling problem with a high degree of complexity, i.e. scheduler explicitly deals with delays while trying to minimize makespan (generally, delays are ignored in the literature to simplify things). In the tabu implementation, an LS procedure is introduced in the metaheuristic loop that enhances the search process by minimizing non‐productive time of picking agents (travel time and delays).
Unlabelled:
This paper presents a conceptual framework that can support efforts to integrate human factors (HF) into the work system design process, where improved and cost-effective application of HF is possible. The framework advocates strategies of broad stakeholder participation, linking of performance and health goals, and process focussed change tools that can help practitioners engage in improvements to embed HF into a firm's work system design process. Recommended tools include business process mapping of the design process, implementing design criteria, using cognitive mapping to connect to managers' strategic goals, tactical use of training and adopting virtual HF (VHF) tools to support the integration effort. Consistent with organisational change research, the framework provides guidance but does not suggest a strict set of steps. This allows more adaptability for the practitioner who must navigate within a particular organisational context to secure support for embedding HF into the design process for improved operator wellbeing and system performance.
Practitioner summary:
There has been little scientific literature about how a practitioner might integrate HF into a company's work system design process. This paper proposes a framework for this effort by presenting a coherent conceptual framework, process tools, design tools and procedural advice that can be adapted for a target organisation.
Order picking, the assembly of a customer's order from items in storage, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. This study departs from the limited prior research that focused on either routing of workers or storage of warehoused items. The main objectives are to (1) evaluate various routing heuristics versus an optimal routine in a volume-based storage environment, (2) propose several methods of implementing volume-based storage, and (3) examine the interaction of the routing and storage policies under different operating conditions of pick list size and demand skewness. The experimental results show statistically significant differences in the mean route distance for the routing policies, storage policies, and their interactions. Further testing indicates that the choice of certain routing and storage policies in combination can result in increased picking efficiency.
Manual order picking ranks among the most time- and cost-intensive activities in warehouses, and it has frequently been studied in the past. The aim of existing studies was to improve the operational efficiency of order picking processes mainly by developing planning models that help to reduce the time that is needed for order picking. As order picking is still performed manually with technical support in most warehouses, human workers play an important role for order picking performance. Although it is recognized that manual material handling activities in warehouses expose workers to a high risk of developing musculoskeletal disorders, integrated planning approaches that consider both economic and ergonomic objectives in order picking design are still rare. This paper contributes to closing this research gap by developing economic and ergonomic performance measures for the case where orders are picked from pallets, half-pallets and half-pallets equipped with a pull-out system. The comprehensive analysis of the different rack layouts shows that there are opportunities to replace the traditional pallet storage system by half-pallets with a pull-out system on the lower rank to improve both ergonomics and economic performance.
Order picking, the activity by which a number of goods are retrieved from a warehousing system to satisfy a number of customer orders, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. The main objectives of this paper are: evaluate various routing heuristics and an optimal routine in a volume-based and random storage environment; compare the performance of volume-based storage to random storage; and examine the impact of travel speed and picking rates on routing and storage policy performance. The experimental results show the solution gap between routing heuristics and optimal routing is highly dependent on the travel speed and picking rate, the storage policy, and the size of the pick list. In addition, volume-based storage produced significant savings over random storage, but again these savings are dependent on the travel speed and picking rate.
Order picking is the most time-consuming and labor-intensive activity in warehousing. Due to the need to frequently handle items, order picking requires high human energy expenditure and poses a risk environment for workers to develop musculoskeletal disorders. The storage assignment policy in use has a significant impact on human energy expenditure and fatigue during the picking process, but this impact is usually not considered in (management-oriented) decision support models for storage assignment.
This paper models and analyzes the integration of human energy expenditure as one dimension of ergonomics into the storage assignment problem using a bi-objective approach that considers both total order picking time and human energy expenditure. Time and energy expenditure depend on the main features of the order picking system, such as item characteristics, item popularity, order profiles, and physical dimensions of the shelf and locations. Pareto frontiers are constructed to understand the impact of the storage assignment policy on the objective functions. Subsequently, a quantitative approach is developed to integrate the energy expenditure rate into the time estimation for a general order picking system based on the introduction of rest allowance. Finally, the results of the model are analyzed and suggestions for the practical application of the model are presented.
This paper studies the joint order batching and order picker routing problem in conventional multi-parallel-aisle picker-to-part order picking systems. It complements prior publications by considering a capacity constraint that is formulated as a function of total item weight, instead of item count. A mathematical model is formulated and a simulated annealing algorithm is developed to batch orders and to determine pick tours. The intention of the paper is to provide a more realistic model and to improve classical batching and routing heuristics. It thereby pays special attention to the practical applicability of the model. The proposed methods are compared and evaluated in an extensive numerical study, and it is shown that the developed approach leads to an improved solution for the joint order batching and order picker routing problem, as compared to classical heuristics for this problem.
Purpose
– Warehouse picking is often referred to as the most labour-intensive, expensive and time consuming operation in manual warehouses. These factors are becoming even more crucial due to recent trends in manufacturing and warehousing requiring the processing of orders that are always smaller and needed in a shorter time. For this reason, in recent years more efficient and better performing systems have been developed, employing various technological solutions that can support pickers during their work. The purpose of this paper is to introduce a comparison of five paperless picking systems (i.e. barcodes handheld, RFID tags handheld, voice picking, traditional pick-to-light, RFID pick-to-light).
Design/methodology/approach
– Warehouse picking is often referred to as the most labour-intensive, expensive and time consuming operation in manual warehouses. These factors are becoming even more crucial due to recent trends in manufacturing and warehousing requiring the processing of orders that are always smaller and needed in a shorter time. For this reason, in recent years more efficient and better performing systems have been developed, employing various technological solutions that can support pickers during their work. The present paper introduces a comparison of five paperless picking systems (i.e. barcodes handheld, RFID tags handheld, voice picking, traditional pick-to-light, RFID pick-to-light.
Findings
– The proposed approach contributes to the understanding of the performance of different technologies in different application fields; some solutions are more suitable for a low-level warehouse, others bring greater benefits in the case of picking from multilevel shelving.
Originality/value
– The study concerns an issue that until now has received very little attention in the literature. It compares some traditional solutions with some innovative ones by an economic evaluation. The presented hourly cost function also takes into account the different errors arising and their probability of occurrence.
Order picking is a time-intensive and costly logistics process as it involves a high amount of manual human work. Since order picking operations are repetitive by nature, it can be observed that human workers gain familiarity with the job over time, which implies that learning takes place. Even though learning may be an important source of efficiency improvements in companies, it has largely been neglected in planning order picking operations. Mathematical planning models of order picking that have been published earlier thus provide an incomplete picture of real-world order picking, which affects the quality of the planning outcome. To contribute to closing this research gap, this paper presents an approach to model worker learning in order picking. First, the results of a case study are presented that emphasize the importance of learning in manual order picking. Subsequently, an analytical model is developed to describe learning in order picking, which is then evaluated with the help of numerical examples. The results show that learning impacts order picking efficiency. In particular, the results imply that worker learning should be considered when planning order picking operations as it leads to a better predictability of order throughput times. In addition, the effects of learning are relevant for the allocation of available resources, such as the allocation of workers to different zones of the warehouse. The results of the numerical analysis indicate that it is beneficial to assign workers with the lowest learning rate in the workforce to the fast moving zone to gain experience.
In manually operated warehouses, the travel distance of the order picker has profound effects on the warehouse cost and efficiency. Estimating this distance is difficult because the warehouse environment is a stochastic one, affected by a great number of parameters. Therefore, we present a comprehensive statistical study to assess how the different warehouse parameters and their interactions affect the travel distance. To estimate the travel distance, we simulate the different designs using agent-based modeling (ABM). Having 324 different designs, ABM has enabled us to build one computer model to simulate all the cases. The study shows that having one cross aisle only and using a class-based storage policy decreases the travel distance. Moreover, the results obtained show that choosing the best routing policy depends on the warehouse layout, which proves the importance of considering the interactions among the different parameters.
The present paper introduces an innovative full-body system for the real-time ergonomics evaluations of manual material handling in warehouse environments, where all parts of the body are interested during the activities execution. The system is based on inertial sensors with integrated compensation of magnetic interference and long wireless connection that permit its use also in heavy industrial applications. A specific set of tools has been developed in order to elaborate the collected motion data and give real-time evaluation and feedback of ergonomics based on the most used methodologies and extended with others advanced ad hoc tools, such as hands positions analysis, travel distance, time and methods collection calculations. The system has been applied to two different warehouses both for the re-design of the storage area and successively management of the typical warehousing activities, such as picking, packing and others, reducing the risk of musculoskeletal disorders and simultaneous increasing of productivity of systems.
Order picking is a time-intensive and costly logistics activity as it involves a high amount of manual work. Prior research has mostly neglected the influence of human factors on the efficiency of order picking systems. This paper develops a mathematical model that investigates the impact of learning and forgetting of a heterogeneous workforce on order picking time and, consequently, on storage assignment decisions. In particular, the paper investigates when to change a storage assignment and when to keep it if learning and forgetting occur among the members of an order picking workforce. The results show that learning and forgetting should be considered in order to achieve a proper planning of storage assignment strategies.
Mainly deal with queueing models, but give the properties of many useful statistical distributions and algorithms for generating them.
We show how to configure a cross aisle in a unit-load warehouse to facilitate travel between storage locations and multiple pickup and deposit points on one side. We use our models to investigate designs having two types of cross aisles—those that form a “Flying-V” and those that form an “Inverted-V.” Our numerical results suggest that there is a benefit to using a Flying-V aisle design, but the benefit is more modest than in the case of a single P&D point. Thus, to the extent practicable, pickup and deposit points should be concentrated toward the middle of the warehouse.
This article discusses a novel agent-based modeling (ABM) approach to analyze the impact of warehouse congestion and presents results indicating the significant effect of congestion on cost and performance in various scenarios. In particular, the simulation represents the behaviors of the order pickers in a picker-to-part, low picking warehouse and focuses on representing the traffic and movements of the pickers. The key motivation for simulating this system is the lack of literature discussing models or simulations capable of representing the congestion component of order pickers, a component important in actual warehouse operations. The conceptual model of the simulation is described and justified using the Conceptual Model for Simulation Diagram™ and the simulation is constructed using the simulation software AnyLogic®. The simulation is operationally validated via a series of experiments performed to test the simulation’s results against the expected dynamics of the system as described in (Tompkins et al. 2003). After operationally validating the simulation, key results are discussed and it is shown that the ABM simulation paradigm is capable of quantitatively capturing new and traditionally difficult to explore dynamics in warehouse operations, including components of congestion not considered in literature.
Order-picking, or the process of retrieving items from storage locations in response to a specific customer request, is the most laborious and the most costly activity in a typical warehouse. This is especially true in the case of the conventional warehouses, with almost 90% of total time spent on order-picking activities and 55% of all operating costs attributed to order-picking. As 50% of total order-picking time is spent on travelling, organisational changes and application of various order-picking methods to reduce travel distances could lead to significant improvements. In this paper, three groups of order-picking methods are analysed: routing, storage and order batching. In order to determine the potential order-picker travel distance savings using a particular method or a combination of various methods, an extensive simulation is done. The presented results showed up to 80% possible reduction of travel distances using the appropriate combination of order-picking methods.
This paper evaluates the performance of three routing policies in the order-picking process, i.e. return, traversal, and midpoint policy. It is assumed that items are assigned to storage locations on the basis of the cube-per-order index (COI) rule in a low-level picker-to-part warehousing system. First, for the three policies, analytical models are developed for the total expected travel distance of the order picker considering the number of the stocking aisles is even or odd. Then the developed models are compared with simulation results to show the validity. Finally, the performance of the three policies is examined by varying the parameter value of the COI-based ABC curve, number of picks in the pick list, and ratio of the length to the width of the warehouse.
Class-based storage (CBS) partitions stock-keeping units (SKUs) into storage classes by demand and randomly assigns storage locations within each storage class area. This study compares the performance implications of CBS to both random and volume-based storage (VBS) for a manual order picking warehouse. In addition, this study considers the effect of the number of storage classes, the partition of storage classes, and the storage implementation strategy applied in the warehouse. The simulation results show that CBS provides savings in picker travel over random storage and offers performance that approaches VBS. Other operational issues having an impact on warehouse performance are examined. The results offer managers insight for improving distribution center operations.
Presents a methodology useful when analysing the efficiency of order
picking systems. The main feature of the analysis is the ability to
compare different system designs. The methodology has earlier been
applied mainly to assembly production systems, and has in these cases
proved to be an effective management tool in discussions concerning the
choice of production system.
Prior research on storage policies and order-picking strategies in order-picking systems has concentrated on studying warehouses with parallel shelves that are separated by horizontal and/or vertical aisles. This paper analyses a special case of an order-picking system where the warehouse is divided into zones with shelves being arranged in the shape of a U in each of the zones. The paper assumes that the shelves of the order-picking system are made up of two rows of stillages that can be flexibly exchanged and that the base of the order-picking process can be moved within the aisle prior to the beginning of the order-picking process. We describe the order-picking system in a formal model and propose different storage location assignment policies whose efficiency is compared in a numerical study. The paper pays special attention to the practical applicability of the model and proposes heuristics that can be easily implemented in practice.
A classical order picking problem is the case where items have to be picked from both sides of an aisle and the picker cannot reach items on both sides without changing position. Hence the picker must cross the aisle one or more times. An efficient optimal algorithm is developed and shown to yield policies with up to 30% savings in travel time over commonly used policies. It is also shown that, for most practical aisle widths, it is significantly more efficient to pick both sides of the aisle in the same pass (a traversal policy) rather than pick one side and then pick the other side (a return policy) unless the pick densities are greater than 50%. All the algorithms presented here can be implemented in real time on a microcomputer.
Purpose
The purpose of this paper is to present an eight‐step simulation model development process (SMDP) for the design, implementation, and evaluation of logistics and supply chain simulation models, and to identify rigor criteria for each step.
Design/methodology/approach
An extensive review of literature is undertaken to identify logistics and supply chain studies that employ discrete‐event simulation modeling. From this pool, studies that report in detail on the steps taken during the simulation model development and model more than one echelon in logistics, supply chain, or distribution systems are included to illustrate rigor in developing such simulation models.
Findings
Literature review reveals that there are no preset rigor criteria for publication of logistics and supply chain simulation research, which is reflected in the fact that studies published in leading journals do not satisfactorily address and/or report the efforts taken to maintain the rigor of simulation studies. Although there has been a gradual improvement in rigor, more emphasis on the methodology required to ensure quality simulation research is warranted.
Research limitations/implications
The SMDP may be used by researchers to design and execute rigorous simulation research, and by reviewers for academic journals to establish the level of rigor when reviewing simulation research. It is expected that such prescriptive guidance will stimulate high quality simulation modeling research and ensure that only the highest quality studies are published.
Practical implications
The SMDP provides a checklist for assessment of the validity of simulation models prior to their use in practical decision making. It assists in making practitioners better informed about rigorous simulation design so that, when answering logistics and supply chain system questions, the practitioner can decide to what extent they should trust the results of published research.
Originality/value
This paper develops a framework based on some of the most rigorous studies published in leading journals, provides rigor evaluation criteria for each step, provides examples for each step from published studies, and illustrates the SMDP using a supply‐chain risk management study.
Order picking, the assembly of a customer’s order from items in storage, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. The main objectives are to: evaluate various routeing policies in a random storage environment; evaluate the impact of warehouse shape and pick-up/drop-off location; and examine the interaction of the routeing policies, warehouse shape, and pick-up/drop-off location under different pick list sizes. The experimental results clearly indicate that the optimal routeing procedure generates significantly shorter routes than heuristic methods. The composite and largest gap routeing policies are, however, significantly better than simpler heuristic procedures. Further testing, in addition, indicates that the shape of the warehouse and the location of the pick-up/drop-off point can affect the picking efficiency.
Order picking, the activity by which a number of goods are retrieved from a warehousing system to satisfy a number of customer orders, is an essential link in the supply chain and is the major cost component of warehousing. The critical issue is to simultaneously reduce the cost and increase the speed of the order picking activity. The main objectives of this paper are: evaluate various routing heuristics and an optimal routine in a volume-based and random storage environment; compare the performance of volume-based storage to random storage; and examine the impact of travel speed and picking rates on routing and storage policy performance. The experimental results show the solution gap between routing heuristics and optimal routing is highly dependent on the travel speed and picking rate, the storage policy, and the size of the pick list. In addition, volume-based storage produced significant savings over random storage, but again these savings are dependent on the travel speed and picking rate.
This paper may be considered as a practical reference for those who wish to add (now sufficiently matured) Agent Based modeling to their analysis toolkit and may or may not have some System Dynamics or Discrete Event modeling background. We focus on systems that contain large numbers of active objects (people, business units, animals, vehicles, or even things like projects, stocks, products, etc. that have timing, event ordering or other kind of individual behavior associated with them). We compare the three major paradigms in simulation modeling: System Dynamics, Discrete Event and Agent Based Modeling with respect to how they approach such systems. We show in detail how an Agent Based model can be built from an existing System Dynamics or a Discrete Event model and then show how easily it can be further enhanced to capture much more complicated behavior, dependencies and interactions thus providing for deeper insight in the system being modeled. Commonly understood examples are used throughout the paper; all models are specified in the visual language supported by AnyLogicTM tool. We view and present Agent Based modeling not as a substitution to older modeling paradigms but as a useful add-on that can be efficiently combined with System Dynamics and Discrete Event modeling. Several multi-paradigm model architectures are suggested.
Although the picking of items may make up as much as 60%of all labor activities in a warehouse and may account for as much as 65%of all operating expenses, many order picking problems are still not well understood. Indeed, usually simple rules of thumb or straightforward constructive heuristics are used in practice, even in state-of-the-art warehouse management systems, however, it might well be that more attractive algorithmic alternatives could be developed. We address one such a fundamental materials handling problem: the batching of orders in a parallel-aisle warehouse so as to minimize the total traveling time needed to pick all items. Many heuristics have been proposed for this problem, however, a fundamental analysis of the problem is still lacking. In this paper, we first address the computational complexity of the problem. We prove that this problem is NP-hard in the strong sense but that it is solvable in polynomial time if no batch contains more than two orders. This result is not really surprising but it justifies the development of approximation and/or enumerative optimization algorithms for the general case. Our primary goal is to develop a branch-and-price optimization algorithm for the problem. To this end, we model the problem as a generalized set partitioning problem and present a column generation algorithm to solve its linear programming relaxation. Furthermore, we develop a new approximation algorithm for the problem. Finally, we test the performance of the branch-and-price algorithm and the approximation algorithm on a comprehensive set of instances. The computational experiments show the compelling performance of both algorithms.
This paper evaluates and compares strategies for routing a manual picker through a simple warehouse. It expands on previous work, in which optimization algorithms were developed, by deriving equations which relate route length to warehouse attributes. Several rules of thumb are derived for selection of order picking strategies and optimization of warehouse shape.
RFID technology provides in-depth, real-time visibility into the status of assets throughout the supply chain. However, the deployment of RFID technology may have collateral value in the high-quality data generated by these assets. This study explores the potential value of RFID data for tactical and strategic purposes and the redesign of processes within supply chain through the deployment of simulation modeling and analysis. We present a simulation study conducted at a regional hospital for which data related to trauma patient movement was collected with an RFID-based system. We find that not only does this data serve as the basis for successful simulation modeling, but that RFID technology may address several data-related challenges previously identified in the simulation literature.