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Warehouse Management Systems (WMS) play a major role in optimizing warehouse logistic processes, archiving merchant trends of supply and demand, and also facilitate treating of goods which are close to expiring deadline, out of stock, broken or deposited by customers. Since warehouse management systems may be integrated with business intelligence,...
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... In linear structural analysis, the applied force and displacement have linear relations. We considered linear analysis because the applied force lies in the elastic region [6,7]. Static structural analysis has been carried out in ANSYS Workbench 18.2. ...
... In this case, the minimum life cycle for a tooling system is 10 6 . It means that the tool can bear an infinite life cycle with this amount of load. ...
A flexible manufacturing system (FMS) is an integral part of a smart factory of Industry 4.0 in which every machine is interconnected and works autonomously. Robots are in the process of replacing humans in every industrial sector. As the cyber-physical system (CPS) and artificial intelligence (AI) are advancing, the manufacturing industry is getting more dependent on computers than human brains. This modernization has boosted production with high quality and accuracy and shifted from classic production to smart manufacturing systems. However, workpiece handling for such an automated production system is a challenge and needs to be addressed with the best possible solution. Conventional clamping systems are designed for manual work and are not suitable for highly automated production lines. Researchers and engineers are trying to find the most economical solution for loading/unloading and transportation of workpieces from a warehouse to a machine shop for machining operations and back to the warehouse without human involvement. This work aims to propose an advanced multi-shape tooling solution for highly automated manufacturing systems. The currently obtained result shows that it could function well with automated guided vehicles (AGVs) and modern conveyor belts. The proposed solution is following requirements to be automation-friendly, and universal for different part geometry and production operations. We used a bottom-up approach in this work, starting with studying different case scenarios and their limitations and finishing with the general solution.
... The research conducted by Zunic et al. [78] serves as a sanity check for the design of warehouse order picking optimization systems. Their work focuses on developing a system that enhances the efficiency of order picking processes for warehouse workers, with the basis rooted in a practical, real-world challenge. ...
... While simulation-based evaluations give an understanding of the models' potential, they may not fully represent real-world complexities. Conversely, Zunic et al. [78] employed an empirical approach, testing their method with data from an actual warehouse. This real-world evaluation provides a direct assessment of the method's practical applicability. ...
... Simulation Theys et al. [76], Ratliff and Rosenthal [77] Real-world case Zunic et al. [78] ...
Background: In the context of Warehouse Management Systems, knowledge related to motion trajectory prediction methods utilizing machine learning techniques seems to be scattered and fragmented. Objective: This study seeks to fill this research gap by using a systematic literature review approach. Methods: Based on the data collected from Google Scholar, a systematic literature review was performed, covering the period from 2016 to 2023. The review was driven by a protocol that comprehends inclusion and exclusion criteria to identify relevant papers. Results: Considering the Warehouse Management Systems, five categories of motion trajectory prediction methods have been identified: Deep Learning methods, probabilistic methods, methods for solving the Travelling-Salesman problem (TSP), algorithmic methods, and others. Specifically, the performed analysis also provides the research community with an overview of the state-of-the-art methods, which can further stimulate researchers and practitioners to enhance existing and develop new ones in this field.
... Significant efforts have been made throughout the past 30 years to improve the orderpicking process [4,13]. Based on [14][15][16][17], the weight of the products is a barely considered feature, which may be a key consideration when retrieving the products from the orders since it avoids impacts or damages on the items. The main motivation to consider the weight of the products is that planning the retrieval routes of the orders does not consider this factor. ...
... However, even though several authors have widely studied these problems in the literature, such as [5][6][7]14,20,21], most of their methodologies focus on developing solution methods in the warehouse with rectangular or block layouts. Nevertheless, other non-traditional layouts exist, such as the fishbone used in [22][23][24], U-shape in [25], an unusual layout presented in [17], an irregular layout used in [26], a layout of the robotic mobile fulfillment system (RMFS) in [27,28], or the general warehouse studied by [18], which is used in this work. On the other hand, although our case study company does not use a radio-frequency identification system (RFID), in the literature [29], the authors developed an interesting contribution for the optimized RFID system for different warehouses with an L-shape and U-shape. ...
One of the critical warehousing processes is the order-picking process. This activity consists of retrieving items from their storage locations to fulfill the demand specified in the pick lists. Therefore, the storage location assignment affects the picking time and, consequently, reduces the operating costs of the warehouse. This work presents two alternative mixed-integer linear models and an adaptive multi-start heuristic (AMH) for solving the integrated storage location and picker-routing problem. The problem considers a warehouse with a general layout and precedence constraints for picking according to the products weight. Experimental work confirms the efficiency of the proposed reformulations since we found out a total of 334 tested instances and optimal solutions for 51 new cases and 62 new feasible solutions. The proposed AMH improved more than 29% of the best-known solutions and required an average execution time of 117 s. Consequently, our proposed algorithm is an attractive decision-making tool to achieve efficiency when solving practical situations in a warehouse.
... The problem asks: given a set of cities and the distances between each pair, what is the shortest possible path such that a salesman (or agent) visits each city exactly once and returns to the starting point? Variants of this problem exist in many applications such as vehicular routing (Robust, Daganzo, and Souleyrette II 1990), warehouse management (Zunic et al. 2017). Traditionally, the TSP has been tackled using two classes of algorithms; exact algorithms and approximate algorithms (Anbuudayasankar, Ganesh, and Mohapatra 2014). ...
The traveling salesman problem is a fundamental combinatorial optimization problem with strong exact algorithms. However, as problems scale up, these exact algorithms fail to provide a solution in a reasonable time. To resolve this, current works look at utilizing deep learning to construct reasonable solutions. Such efforts have been very successful, but tend to be slow and compute intensive. This paper exemplifies the integration of entropic regularized optimal transport techniques as a layer in a deep reinforcement learning network. We show that we can construct a model capable of learning without supervision and inferences significantly faster than current autoregressive approaches. We also empirically evaluate the benefits of including optimal transport algorithms within deep learning models to enforce assignment constraints during end-to-end training.
... This is an important constraint that needs to be catered to while designing a warehouse, but in situations where the optimization needs to be performed on a fixed layout, different techniques need to be applied. This aspect of the optimization is described through the research from Zunic et al. [20] and Zunic et al. [21] where order picking optimization is designed mainly to be robust against the constraints that come with different layout approaches. This is a very important aspect of the optimization of the processes inside a warehouse because of the effect that the layouts can have on the overall performance like it is described in the research from Gu et al. [22] and Gue and Meller [23]. ...
... Defined differently, there are priority classes for items in the collection process. Orders with fewer locations were obtained, so that the optimal picking route problem can be solved with simpler approaches such as 2 As stated in [20], 2-opt and 3-opt algorithms give results with less than 5% and 3% error, respectively. The algorithm was tested on 5000 orders in the real warehouse. ...
... However, it was discovered that Flying-V Layouts (Figure 5a, 5b), and Fishbone Layouts (Figure 5c) can reduce up to 20% the traveled distance during the execution of batch-picking and 15% when it is necessary to carry out retrieval and storage in the same run (dual-command ) (Zunic et al., 2017) compared to traditional designs. ...
With the advancement of the autonomous mobile robots applied to Warehouses and the creation of the Robotic Mobile Fulfillment System after the market implementation of the Kiva Robots, it is necessary to carry out a deeper approach of the researches carried out to this date. The objective of this survey is to provide a unified and accessible presentation of the basic concepts of a warehouse system, such as its types, layouts, systems, and methodologies already applied to improve the activities, thus going to the latest research and methodologies focused on the development of new architectures and algorithms in Robotic Mobile Fulfillment Systems (RMFS). The main contribution of this work is an attempt to present a comprehensive review of recent breakthroughs in the goods-to-person RMFS field, providing links to the most interesting and successful works from the state-of-the-art, but also to provide a presentation and summary of how a Warehouse systems works, in a way that allows future researchers to understand his taxonomies and principles of operation.
... The Fig 7 show the 2-opt moves from [7]. [8] compared several heuristic strategies for the TSP problem such as Greedy, Insertion, SA, GA, etc. ...
Distributed Systems architectures are becoming the standard computational model for processing and transportation of information, especially for Cloud Computing environments. The increase in demand for application processing and data management from enterprise and end-user workloads continues to move from a single-node client-server architecture to a distributed multitier design where data processing and transmission are segregated. Software development must considerer the orchestration required to provision its core components in order to deploy the services efficiently in many independent, loosely coupled—physically and virtually interconnected—data centers spread geographically, across the globe. This network routing challenge can be modeled as a variation of the Travelling Salesman Problem (TSP). This paper proposes a new optimization algorithm for optimum route selection using Algorithmic Information Theory. The Kelly criterion for a Shannon-Bernoulli process is used to generate a reliable quantitative algorithm to find a near optimal solution tour. The algorithm is then verified by comparing the results with benchmark heuristic solutions in 3 test cases. A statistical analysis is designed to measure the significance of the results between the algorithms and the entropy function can be derived from the distribution. The tested results shown an improvement in the solution quality by producing routes with smaller length and time requirements. The quality of the results proves the flexibility of the proposed algorithm for problems with different complexities without relying in nature-inspired models such as Genetic Algorithms, Ant Colony, Cross Entropy, Neural Networks, 2opt and Simulated Annealing. The proposed algorithm can be used by applications to deploy services across large cluster of nodes by making better decision in the route design. The findings in this paper unifies critical areas in Computer Science, Mathematics and Statistics that many researchers have not explored and provided a new interpretation that advances the understanding of the role of entropy in decision problems encoded in Turing Machines.
... The first such task was the determination of the optimal path for the collection of items during order picking. This developed solution [17] included attributes such as the expiry date, weight and the priority of the items. Through a detailed analysis of the sales data collected over this period when the initial WMS version was operational, associative rules were established among articles with firm correlation links. ...
One of the frequently occurring tasks during the development of warehouse management systems is the implementation of routing algorithms of some kind. Whether it is for routing workers during order picking, delivery vehicles or company representatives, this task has proven to be challenging in the technical as well as the social sense. In other words, the task is heavily dependent on various general and company-specific constraints and it directly dictates the way employees should do their job. This paper describes a strategic approach to the development and gradual integration of such algorithms which makes sure that all constraints are satisfied and, more importantly, ensures that route suggestions are viewed by the employees as a helpful tool rather than a threat to their job. In the first part of this paper, the approach is described and evaluated on a warehouse representative routing problem through a real-world case study in a medium-to-large warehouse. In the second part, the same approach is adapted to a delivery vehicle routing problem for a smaller retailer company. In both cases, routing efficiency almost doubled in comparison to previous approaches used by the companies. The most important factors of the implementation and integration stages as well as the impact of the changes on employee satisfaction are aggregated, analysed in detail, and discussed throughout different stages of development.
... The book of Davarzani [9] discussed warehouse planning, technology, equipment, human resource management, connections to other department and companies. Zunic [10] considered various warehouse designs, especially the V-shape isles and calculated the order picking routes for these designs. Dharmapriya [11] discussed the use of simulated annealing for the warehouse layout optimization taking into account the total demand and traveling cost, but without considering the co-existence of various products in the same orders. ...
We present a complete, fully automatic solution based on genetic algorithms for theoptimization of discrete product placement and of order picking routes in a warehouse. The solutiontakes as input the warehouse structure and the list of orders and returns the optimized productplacement, which minimizes the sum of the order picking times. The order picking routes areoptimized mostly by genetic algorithms with multi-parent crossover operator, but for some casesalso permutations and local search methods can be used. The product placement is optimized byanother genetic algorithm, where the sum of the lengths of the optimized order picking routes isused as the cost of the given product placement. We present several ideas, which improve andaccelerate the optimization, as the proper number of parents in crossover, the caching procedure,multiple restart and order grouping. In the presented experiments, in comparison with the randomproduct placement and random product picking order, the optimization of order picking routesallowed the decrease of the total order picking times to 54%, optimization of product placement withthe basic version of the method allowed to reduce that time to 26% and optimization of productplacement with the methods with the improvements, as multiple restart and multi-parent crossoverto 21%.
... The use of smart warehouse management tools has become a requirement [2]. To support the working process, many algorithms that are dependent on spatial relations of objects are used, like optimal warehouse order picking [3], optimal product placement [4], and picking zone capacity and content prediction [5]. These approaches usually require intervention in the real world warehouse, where employees need to know which pallet places require attention and their exact position in the warehouse. ...
This paper presents a data visualization method in 3D space that includes actual positions, volumes and space relations of the chunks of data that are being visualized. Data that is being visualized is real-time information provided by the smart warehouse management system about packages distributed on pallet places within a warehouse. Three different visualizations are shown: qualitative, quantitative and cumulative. The method is graded for the time needed to determine the location of all pallet places that fulfill searched criteria and getting the exact value of searched information for each pallet place. Challenges in presenting this data and interacting with resulting visualizations are discussed. It is concluded that showing actual positions of chunks of data greatly increases the speed of acquiring searched values and positions at the same time for outliers but has issues with clusters and multiple types of queried data.
