Integrating storage location and order picking problems in warehouse planning

Silva, Allyson; Coelho, Leandro C.; Darvish, Maryam

doi:10.1016/j.tre.2020.102003

Cited by 50 publications

(23 citation statements)

References 51 publications

(109 reference statements)

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“…The simulation results show that this discrete modeling method can effectively evaluate the system throughput and other indicators [1]. Silva takes the storage location of goods as a constraint condition when modeling, and proposes a variable neighborhood search algorithm, which further improves the operation efficiency [2], Kaiwen Liu et al considered the problem of time cut-off when establishing the model, established a model with the minimum energy consumption as the goal, and used the gray wolf optimization algorithm to obtain the minimum energy consumption order sequence [3]. Xiangnan Zhan adopts improved particle swarm optimization algorithm to effectively reduce the delivery time of goods [4].…”

Section: Related Workmentioning

confidence: 99%

Research on Optimization Strategy of Stacker Scheduling in Intelligent Storage System Based on Intelligent Improved Algorithm

Shang-Cai¹,

Shang-Cai²

2022

Journal of Compurters

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With the vigorous development of e-commerce, higher requirements are put forward for the storage capacity and operation efficiency of the warehousing system to ensure the performance of the entire supply chain in the business process. The factors that affect the operation efficiency and storage capacity of the warehousing system mainly include: storage space planning, shelf design, goods access strategy, stacker scheduling strategy, goods picking efficiency, etc. The main research object of this paper is the optimization of the scheduling strategy of the stacker in the storage system, which can solve the problem of the storage space detention caused by the irregular storage and the empty running time in the scheduling process of the stacker, so as to improve the operating efficiency of the storage system.

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Section: Related Workmentioning

confidence: 99%

Research on Optimization Strategy of Stacker Scheduling in Intelligent Storage System Based on Intelligent Improved Algorithm

Shang-Cai¹,

Shang-Cai²

2022

Journal of Compurters

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show abstract

“…Kofler, et al studied the SLAP in a logistics center of an Austrian company in the automotive e-sector; where they employed simulated annealing to decrease picking effort [15]. Silva, et al presented a General Variable Neighborhood Search metaheuristic, which is observed to be efficient for both small and large instances in WMS [7]. Syafrudin proposed a model that utilizes IoT-based sensors, big data processing, and a hybrid prediction using Random Forest classification.…”

Section: Storage Location Assignmentmentioning

confidence: 99%

“…The objective function is a weighted average of two generic grouping scores. The storage location assignment problem has been shown to be NP-Hard [7]. Thus the objective function is formulated to solve this combinatorial optimization problem.…”

Section: Iiiiiv Data Analysismentioning

confidence: 99%

“…There are larger order fulfillment operational challenges when receiving, storing, order picking, and shipping products; showing an increased uncertainty in storage time, higher turnover velocities, and more types of product to fulfill. Due to labor intensity in such systems, the order picking process alone costs 50-75% of the total operating costs [7]. This shows urgent need for smart and intelligent solutions to enhance the effectiveness of storage policies, and guarantee more efficient order picking within a shorter timeframe [8] [9].…”

Section: Introductionmentioning

confidence: 99%

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Smart Solution For Enhancing Storage Location Assignments In WMS Using Genetic Algorithm

Saleet¹

2020

International Journal of Engineering Research and Technology

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“…The picking time of stacker takes up most of the time of goods entering and leaving the warehouse. The picking path problem of order is generally abstracted into traveling salesman problem (TSP) [29,22,10], or the capacitated vehicle routing problem (CVRP) [30,14,11,31], with capacity constraints, needs to be handled in batches. Compared with TSP, we regard it as CVRP, which is more in line with the actual picking characteristics of the warehouse.…”

mentioning

confidence: 99%

Picker routing optimization of storage stacker based on improved multi-objective iterative local search algorithm

Wei

Feng

Liu

2023

JIMO

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In the process of warehouse automation, the efficient picker routing strategy is one of the key factors to improve warehouse efficiency. According to the order and the picking characteristics of the warehouse stacker, the picker routing of the stacker is regarded as the prize-collecting vehicle routing problem (PCVRP). According to the path and order for stacker picking, we designed a picker routing optimization strategy based on improved multi-objective iterative local search (IMOILS) algorithm. An elite pool strategy was set to save the high-quality solutions in the iterative process, and the nondominated solutions in the iterative evolution were stored and updated. Adding the adaptive enhanced perturbation strategy was beneficial to the current solution to escape from local optimum. In the local search procedure, to limit the excessively large search space, the epsilon constraint was adopted to limit the significant deterioration in another objective direction. The computational results showed that the proposed algorithm is superior to the comparison algorithms in the number of nondominated solutions, the hypervolume, and the spacing metric, and the stability of the algorithm is also good, which not only verifies the effectiveness of the proposed algorithm, but also provides a feasible optimization scheme for the storage stacker's picker routing problem.

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Integrating storage location and order picking problems in warehouse planning

Cited by 50 publications

References 51 publications

Research on Optimization Strategy of Stacker Scheduling in Intelligent Storage System Based on Intelligent Improved Algorithm

Research on Optimization Strategy of Stacker Scheduling in Intelligent Storage System Based on Intelligent Improved Algorithm

Smart Solution For Enhancing Storage Location Assignments In WMS Using Genetic Algorithm

Picker routing optimization of storage stacker based on improved multi-objective iterative local search algorithm

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