The storage location assignment problem: A literature review

Reyes, Juan José; Solano-Charris, Elyn L.; Montoya‐Torres, Jairo R.

doi:10.5267/j.ijiec.2018.8.001

Cited by 72 publications

(52 citation statements)

References 53 publications

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“…There are two fundamental strategies for storage location assignment, turnover and SKU grouping. The turnover strategy assigns the most frequently ordered SKUs (with higher turnover rate) to those slots near the I/O point [1] [8]. Although this method works efficiently, it has two important limitations.…”

Section: Figure 2 Simple Routing Policiesmentioning

confidence: 99%

“…Inherently, heuristic methods for SLAP assign one SKU to a suitable slot at a time [1] [8]. In this regard, we call such methods SKU-sequencing based heuristics.…”

Section: Figure 2 Simple Routing Policiesmentioning

confidence: 99%

“…There is a plethora of approaches that target the reduction of the order picker's travel distance, which includes generating efficient picking routes, zoning, storage location assignment and order batching. The storage location assignment has been recognized by many researchers as a factor which significantly affects the travel distance of order pickers [7] [8].…”

Section: Introductionmentioning

confidence: 99%

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Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers

2020

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This paper considers heuristic approaches that can be used to assign stock keeping units (SKU) to individual slots in distribution center. Firstly, we propose two novel strategies, slot selection and frequent itemset grouping. The former is used to find the most suitable slot for a single SKU, while the latter is for sequencing SKUs in an appropriate order. Secondly, we develop several storage location assignment heuristics by applying the two strategies. Especially, the heuristics are designed to assign frequently ordered SKU to a slot close to I/O (input/output) point and SKUs frequently ordered together to slots close to each other. Consequently, the proposed heuristics are helpful to reduce the travel distance of order picker in distribution center. In this paper, travel distance of order picker is calculated based on a routing policy that enables order picker to move along a flexible route. For illustration, we applied the heuristics to real data collected from a large distribution center. The experiment results reveal that slot selection strategy is very helpful to reduce average travel distance of order picker, especially under greedy routing policy. Also, frequent itemset grouping strategy can provide additional reduction in average travel distance if it is applied together with slot selection strategy.

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Section: Figure 2 Simple Routing Policiesmentioning

confidence: 99%

“…Inherently, heuristic methods for SLAP assign one SKU to a suitable slot at a time [1] [8]. In this regard, we call such methods SKU-sequencing based heuristics.…”

Section: Figure 2 Simple Routing Policiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers

2020

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“…The extension of this assignment problem is often called the transportation problem, which not only assigns the task to the machine, but also forms the transportation sequence when there is more than one task assigned to one machine: this is called the vehicle routing problem (VRP). There are plenty of examples of the transportation problem in the literature, such as transportation mode selection (road, waterway, railway) [10,11], connecting the transport mode by assigning the inbound and outbound gates of the product [12][13][14][15], transportation planning with time windows [14][15][16][17], and optimal location assignment [18][19][20][21]. However, the AP aims to increase efficiency and sustainability by reducing time, cost, fuel consumption, and carbon dioxide emissions, increasing customer satisfaction levels and profits, and making other improvements.…”

Section: Literature Reviewmentioning

confidence: 99%

Modified Differential Evolution Algorithm for a Transportation Software Application

Supattananon

Akararungruangkul

2019

Journal of Open Innovation: Technology, Market, and Complexity

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This research developed a solution approach that is a combination of a web application and the modified differential evolution (MDE) algorithm, aimed at solving a real-time transportation problem. A case study involving an inbound transportation problem in a company that has to plan the direct shipping of a finished product to be collected at the depot where the vehicles are located is presented. In the newly designed transportation plan, a vehicle will go to pick up the raw material required by a certain production plant from the supplier to deliver to the production plant in a manner that aims to reduce the transportation costs for the whole system. The reoptimized routing is executed when new information is found. The information that is updated is obtained from the web application and the reoptimization process is executed using the MDE algorithm developed to provide the solution to the problem. Generally, the original DE comprises of four steps: (1) randomly building the initial set of the solution, (2) executing the mutation process, (3) executing the recombination process, and (4) executing the selection process. Originally, for the selection process in DE, the algorithm accepted only the better solution, but in this paper, four new selection formulas are presented that can accept a solution that is worse than the current best solution. The formula is used to increase the possibility of escaping from the local optimal solution. The computational results show that the MDE outperformed the original DE in all tested instances. The benefit of using real-time decision-making is that it can increase the company’s profit by 5.90% to 6.42%.

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“…When there is a demand for a product, it is retrieved from its storage location and delivered to an exit point. For a recent survey on SLAP we refer to Reyes et al (2019).…”

Section: Introductionmentioning

confidence: 99%

An adaptive large neighborhood search heuristic for the planar storage location assignment problem: application to stowage planning for Roll-on Roll-off ships

et al. 2020

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This paper considers a generalized version of the planar storage location problem arising in the stowage planning for Roll-on/Roll-off ships. A ship is set to sail along a predefined voyage where given cargoes are to be transported between different port pairs along the voyage. We aim at determining the optimal stowage plan for the vehicles stored on a deck of the ship so that the time spent moving vehicles to enable loading or unloading of other vehicles (shifting), is minimized. We propose a novel mixed integer programming model for the problem, considering both the stowage and shifting aspect of the problem. An adaptive large neighborhood search (ALNS) heuristic with several new destroy and repair operators is developed. We further show how the shifting cost can be effectively evaluated using Dijkstra’s algorithm by transforming the stowage plan into a network graph. The computational results show that the ALNS heuristic provides high quality solutions to realistic test instances.

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The storage location assignment problem: A literature review

Cited by 72 publications

References 53 publications

Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers

Storage Location Assignment Heuristics Based on Slot Selection and Frequent Itemset Grouping for Large Distribution Centers

Modified Differential Evolution Algorithm for a Transportation Software Application

An adaptive large neighborhood search heuristic for the planar storage location assignment problem: application to stowage planning for Roll-on Roll-off ships

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