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This paper presents the use and extension of a geometrical-based algorithmic approach for determining the expected S/R machine cycle times, and therefore warehouse throughput, for class-based storage assignment layouts in an AS/RS.The approach was designed for the purpose of solving a practical storage assignment design problem for a major European...
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Context 1
... algorithm places the products that generate the largest number of movements into storage zones closest to an I/O point. As shown in Figure 3, in a warehouse with only one I/O port, the zones are formed around each other. The first zone will be square in time, the following zones have an L-shape, but, taken together with zones in previous iterations, they form either a square or rectangular zone. ...
Context 2
... Figure 3, zones 1 and 2, taken together, constitute a square-in-time zone; whereas, zones 1, 2 and 3 taken together constitute a rectangular, non-square-in-time zone. That is, beginning in the lower left corner, the time required by the crane to reach a storage location at the extreme right side of the combined zones and the time required by the crane to reach a storage location at the top of the combined zones are not equal. ...
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Citations
... Vasili et al. (2012) investigated the performance of the split-platform automated storage and retrieval system, which can be enhanced for a range of rack configurations by introducing two I/O points per aisle [11]. Ashayeri et al. (2003) determined the advantages of the class-based storage assignments in a 2D automated storage and retrieval system with two I/O points at floor level on opposite ends of each aisle [12]. The above studies show that a suitable design of the I/O point in different types of 2D automated storage and retrieval systems contributes to performance improvement. ...
The adoption of automatic warehousing systems, a type of green technology, has been an emerging trend in the logistics industry. In this study, we develop a conceptual model using a technology–organization–environment framework to investigate the factors which influence logistics firms to adopt green technology. Our model proposes that the adoption of green technology is influenced by perceived advantage, cost, technological turbulence, business partner influence, firm size, firm scope and operational performance. The objective of this study is to identify the conditions, as well as the contributing factors, for the adoption of automatic warehousing systems in logistics firms. Data were collected from 98 firms in China, and structural equation modeling with partial least squares is adopted to analyze the data. The results suggest that high perceived relative advantage, firm size, cost, firm scope, operation performance, technological turbulence and influence of business partners are important factors affecting IT adoption in small businesses. Therefore, decision support should be provided for enterprises from the three aspects of technology, organization and environment to improve the adoption of automatic warehousing systems.
... In this case the shuffling area of stacker crane for class B includes the cells in class A, too. Under the class-based storage assignment, the expected travel time for a single-command transaction in an AS/RS is the weighted sum of travel times of all possible single command transactions [14]. Hence, using the same method as in section A, the expected retrieval time for the open-rack AS/RS under single command cycle and class-based storage assignment can be expressed as, ...
... For example, the ratio 20/60% means that 20% of the products in the inventory are in class A and satisfy 60% of total demand. Classes B and C share the remaining demand in equal proportions [14]- [17]. For open-rack AS/RS, simulation results of the travel time under randomized storage assignment can be found in [10]. ...
... As an extension of previous studies, Malmborg (1996) formulates state probability models to analyse the trade-offs between space requirements and retrieval efficiency associated with dedicated and randomised storage policies. Ashayeri et al. (2001) build a geometrical-based model for AS/RSs with single, double and multiple I/O points under the class-based storage policies. Different from the above traditional single-deep single-aisle AS/RSs, Ramtin and Pazour (2014) where the storage transactions are operated by the S/R machine and the picking orders are implemented by manual pickers via multiple pick positions located in the aisle. ...
... (3) We overlook the accelerating and decelerating phases of the S/R machine since they normally take very short time. Hausman, Schwarz, and Graves (1976) S D a One S/R machine one aisle SC Heskett (1963Heskett ( , 1964 SD One S/R machine one aisle SC Bozer and White (1984) SD One S/R machine one aisle SC/DC Hwang and Ko (1988) SD One S/R machine multiple aisles SC/DC Hwang and Lee (1990) SD One S/R machine one aisle SC/DC Malmborg (1996) SD One S/R machine one aisle SC/DC Ashayeri et al. (2001) SD One S/R machine one aisle SC/DC Sari, Saygin, and Ghouali (2005) 3D Flow-rack Two S/R machines one rack SC/DC Lerher (2005Lerher ( , 2006 SD One S/R machine multiple aisles SC/DC De Koster, Le-Duc, and Yu (2008) 3D Compact One S/R machine one rack SC De Koster (2009a, 2009b) 3D Compact One S/R machine one rack SC Lerher et al. (2010a) D D a One S/R machine one aisle SC/DC Lerher et al. (2010b) SD One S/R machine multiple aisles SC/DC Yu and De Koster (2012) 3D Compact One S/R machine one rack SC/DC Ramtin and Pazour (2014) SD with multiple One S/R machine one aisle SC/DC in-the-aisle positions Ramtin and Pazour (2015) SD with multiple One S/R machine one aisle SC/DC in-the-aisle positions Xu et al. (2015) DD Dual-shuttle S/R machine SC/DC /QC b a SD: single-deep SR, DD: Double-deep SR. b SC: single-command cycle, DC: dual-command cycle, QC: quadruple command cycle. ...
Double-deep multi-aisle automated storage/retrieval systems are increasingly applied for storing and retrieving unit loads, with advantages of increased space utilisation, reduced number of aisles and improved efficiency of storage rack (S/R) machines. In such systems, the retrieval process may consist of the rearrangement of blocking loads, based on the assumptions of uniformly distributed storage locations and random storage policy. We formulate analytical travel-time models of both single- and dual-command cycles under three rearrangement rules. We validate the analytical travel-time models by simulation and conduct numerical experiments to analyse the effect of the number of aisles an S/R machine serves, the fill-grade factor and the command cycles on the expected travel time of the S/R machine. The results show that the expected travel time of the S/R machine is increasing with the increase in the number of aisles an S/R machine serves and the increase in the fill-grade factor, and dual command cycle outperforms single-command cycle in terms of cycle time. To deal with the trade-off between the storage space cost and the operational cost of the S/R machine, we develop a decision model for finding an optimal fill-grade factor to minimise the total cost. We find the condition when an optimal fill-grade factor exists and show how to calculate it. Based on the decision model, we compare the performance of double-deep multi-aisle automated storage/retrieval system (AS/RSs) and single-deep single-aisle AS/RSs. The results show that double-deep multi-aisle AS/RSs outperform single-deep single-aisle AS/RSs in terms of total cost, although double-deep multi-aisle AS/RSs need more storage locations.
... We devote Chapter 3 of this thesis to investigate this problem in-depth. Besides the publications above-mentioned, the class-based storage assignment method is also considered in: Hausman et al. (1976), Graves et al. (1977), Schwarz et al. (1978), Park and Webster (1989), Rosenblatt and Eynan (1989), Goetschalckx and Ratliff (1990), Guenov and Raeside (1992), Eynan and Rosenblatt (1994), Kouvelis and Papanicolaou (1995), Malmborg (1996), Larson et al. (1997), Ashayeri et al. ( , 2003, and Park et al. (2005) for parts-to-picker OP systems, and in Lee (1992), Jarvis and McDowell (1991), Tang and Chew (1997), Chew and Tang (1999), De Koster (2005a, b, 2004a), and for picker-to-parts OP systems. ...
Binnen een logistieke keten dienen producten fysiek te worden verplaatst van de ene locatie naar de andere, van producenten naar eindgebruikers. Tijdens dit proces worden producten gewoonlijk opgeslagen op bepaalde plaatsen (magazijnen) voor een bepaalde periode. Orderverzameling – het ophalen van producten uit de opslaglocatie in het magazijn naar aanleiding van een specifieke klantorder – is het meest kritieke magazijnproces. Het is een arbeidsintensieve operatie in handmatig bestuurde systemen, en een kapitaalintensieve operatie in geautomatiseerde systemen. Een niet optimaal functionerend orderverzamelingsproces kan leiden tot onbevredigende service en hoge operationele kosten voor het magazijn, en dientengevolge voor de hele keten. Om efficiënt te kunnen functioneren dient het orderverzamelingsproces robuust te zijn ontworpen en optimaal te worden bestuurd.
Dit proefschrift heeft als doel analytische modellen te ontwerpen die het ontwerp en de besturing van efficiënte orderverzamelingsprocessen ondersteunen. Verschillende methoden worden voorgesteld voor het schatten van de route langs de locaties van de te verzamelen producten, het bepalen van de optimale grenzen van zones in het magazijn die bestemd zijn voor opslag, de indeling van het magazijn, het aantal producten die tegelijk (in één ronde) worden verzameld (de batch size) en het aantal zones in het magazijn die worden ingericht voor het verzamelen en gereedmaken van orders. De methoden worden getest middels simulatie experimenten en worden inzichtelijk gemaakt met behulp van rekenexperimenten.
... It is routinely accepted that there are three ways of assigning products to storage locations in warehouses: randomized storage, class-based storage and dedicated storage [13]. Class-based storage is effective when there are many products having different residence times in the storage system. ...
Automated Storage and Retrieval Systems (AS/RSs) are warehousing systems that are used for the storage and retrieval of products in both distribution and production environments. This paper presents an open-rack structure with unidirectional-upward mobile loads within the rack, for miniload AS/RS, in which the stacker crane is only used for the retrieval operations, and the storage operations are carried out by separate devices namely, storage platforms. Heuristics algorithms and models are developed for load shuffling and travel time of the storage platform, respectively. The well-known ABC approach is used to classify inventory items for determination of class-based storage assignments. Then the expected travel time of the proposed AS/RS is derived. The travel time model and the performance of proposed AS/RS are validated using Monte Carlo simulation and are compared with a conventional one. The results show that the open-rack AS/RS represents a higher performance and the proposed models are reliable for the design and analysis of this kind of AS/RS. Keywords: Automated storage and retrieval systems (AS/RS), Open-rack structure, ABC approach, Travel time, Monte Carlo simulation.
Purpose
This paper aims to propose a simulation model integrated with an empirical regression analysis to provide a new mathematical formulation for automated storage and retrieval system (AS/RS) travel time estimation under class-based storage and different input/output (I/O) point vertical levels.
Design/methodology/approach
A simulation approach is adopted to compute the travel time under different warehouse scenarios. Simulation runs with several I/O point levels and multiple shape factor values.
Findings
The proposed model is extremely precise for both single command (SC) and dual command (DC) cycles and very well fitted for a reliable computation of travel times.
Research limitations/implications
The proposed mathematical formulation for estimating the AS/RS travel time advances widely applied methodologies existing in literature. As well as, it provides a practical implication by supporting faster and more accurate travel time computations for both SC and DC cycles. However, the regression analysis is conducted based on simulated data and can be refined by numerical values coming from real warehouses.
Originality/value
This work provides a new simulation model and a refined mathematical equation to estimate AS/RS travel time.
This paper presents a methodology to find optimal zones in automated storage/ retrieval systems (AS/RS) and derive the performance gain for different operating strategies. One central feature of the described method is the computation of precise results in very short time (less than one second in most cases on standard hardware) for any given rack shape independent of interface alignment. These short runtimes lead to new openings in warehouse planning and control: Warehouse planning is broadly driven by the usage of experience and analytical models without respect to influences through operating strategies in early design stages. Embedding the presented method in specially tailored software enables the planner to evaluate the possible system performance achieved under a class-based storage strategy. Additionally the computed results are utilized to assess performance gains using multi-shuttle storage and retrieval systems with advanced routing and can be easily extended for double-deep systems. Furthermore, warehouse control systems can adopt the introduced models to calculate optimal article to zone associations continuously and improve overall performance in existing systems. Short runtimes are realized by a new procedure to determine optimal zones in the rack and thereon a highly parallelized algorithm calculates the average performance for a given system on usual graphic processors.
Automated storage and retrieval system (AS/RS) is one of the major material handling systems, which is widely used in distribution centers and automated production environments. AS/RSs have been utilized not only as alternatives to traditional warehouses but also as a part of advanced manufacturing systems. AS/RSs can play an essential role in modern factories for work-in-process storage and offer the advantages of improved inventory control and cost-effective utilization of time, space and equipment. Many issues and approaches related to the efficiency improvement of AS/RSs have been addressed in the literature. This chapter presents an overview of this literature from the past 40 years. It presents a comprehensive description of the current state-of-the-art in AS/RSs and discusses future prospects. The focus is principally on travel time estimates and different control policies such as dwell-point of the stacker crane, storage assignment, request sequencing and so on. In particular, this chapter will provide researchers and decision makers with an understanding of how to apply existing approaches effectively.
In conventional automated storage and retrieval systems (AS/RS), storage and retrieval (S/R) machine travels simultaneously in the horizontal and vertical directions. However, S/R machine cannot support overly heavy loads, such as sea containers, so a new AS/RS, called split-platform AS/RS (SP-AS/RS), was introduced and studied in recent years. The SP-AS/RS employs vertical and horizontal platforms, which move independently, and are capable of handling heavy loads. The vertical platform which represents an elevator (or lift) with the elevator’s lifting table carries the load up and down among different tiers and the horizontal platform which represents the shuttle carrier or the shuttle vehicle can access all cells of the tier in which it belongs to. Single command cycle (SC) and dual command cycle (DC) are two main operating modes in AS/RSs. However, travel time models in all previous articles related to the SP-AS/RS are only for the SC. In this study, we first present a continuous travel time model for the DC in the SP-AS/RS under input and output (I/O) dwell point policy and validate its accuracy by computer simulations. Our model and simulation results both show that the square-in-time rack incurs the smallest expected travel time. After comparing with the existing model for the SC, we find that the DC is better than the SC in terms of the expected travel time.
The concept of service was quick to arise and has taken the world by storm in the past decade. It has gradually redefined the way warehouses are operated and has nurtured the emergence of a new kind of warehouse, namely the public warehouse (PW). Against this backdrop, a warehouse product service system (wPSS) based on the PW is established in this paper. Furthermore, in order to evaluate the service capability and optimise the resource configuration of a PW, a wPSS service capability maturity model (wPSS–SCMM) based on the analytical target cascading (ATC) algorithm is put forward. Firstly, the generation of wPSS service capability is clarified and the framework of wPSS–SCMM is proposed. After disassembling the wPSS service capability into a tree structure, its four main sub-capabilities, namely storage service capability, transportation service capability, cost control capability and time control capability, are modelled and quantised, respectively. Then the results of the four sub-capabilities are adopted in the ATC algorithm to search for the maximal service capability maturity and the optimal resource configuration solution. Finally, a numerical example of a PW with eight customers in an industrial park is analysed to demonstrate the feasibility and applicability of the proposed new approach. This research is expected to provide guidance for both investors who want to build a PW and managers who want to continuously improve an existing one.
