How to benefit from order data: correlated dispersed storage assignment in robotic warehouses

Mirzaei, Masoud; Zaerpour, Nima; Koster, René de

doi:10.1080/00207543.2021.1971787

Cited by 25 publications

(13 citation statements)

References 30 publications

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“…Using information from historical customer orders, Xiang et al [31] studied the small-scale item storage assignment problem by considering item scattered storage and solve it with the CPLEX solver. Mirzaei et al [32] considered item correlation, turnover frequency, and inventory dispersion concurrently and proposed a correlated dispersion storage assignment policy. Ma et al [33] further considered item classification, namely, best-selling and general-selling items.…”

Section: Literature Reviewmentioning

confidence: 99%

“…A high correlation degree can reduce the number of pod visits during order picking operations [33]. As a result, the travel distance of robots and the order retrieval time can be reduced accordingly [23,25,32]. The high performance of the ALNS heuristic in the RISA problem has a considerable positive effect on the travel distance of robots and order retrieval time.…”

Section: ) Comparison With Existing Heuristicsmentioning

confidence: 99%

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Item Storage Assignment Problem in Robotic Mobile Fulfillment Systems With Nonempty Pods

Li,

Deng,

2024

IEEE Access

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This study examines the item storage assignment problem in robotic mobile fulfillment systems (RMFSs), that determines the assignment of items to pods. Unlike previous studies on this problem that generally considered an empty warehouse, the current study addresses the problem in RMFSs with nonempty pods. The problem is formulated as a mixed-integer program with the goal of maximizing the correlation degree among items. On the basis of the characteristics of the problem, an adaptive large neighborhood search (ALNS) heuristic is designed for the solution. Numerical results show that the gap between the ALNS heuristic and the Gurobi solver is less than 0.32% in small-scale instances. In medium-and large-scale instances, the ALNS heuristic improves the objective value by at least 10% compared with the methods in literature, and it outperforms commonly used storage assignment policies for traditional warehouses (e.g., random, dedicated, and class-based storage) by more than 30%. The ALNS heuristic can also be applied to the special case where the initial state of the warehouse is empty. The computational study of the ALNS heuristic in all instances shows that it can effectively solve this problem by providing high-quality solutions and has good scalability.INDEX TERMS Robotic mobile fulfillment system, storage assignment, adaptive large neighborhood search, warehouse.

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Section: Literature Reviewmentioning

confidence: 99%

Section: ) Comparison With Existing Heuristicsmentioning

confidence: 99%

Item Storage Assignment Problem in Robotic Mobile Fulfillment Systems With Nonempty Pods

Li,

Deng,

2024

IEEE Access

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“…Some research on correlated storage assignment regards assigning products with a high correlated relationship to the same storage location. Mirzaei et al [49] adopt correlated dispersed storage assignment in RMF systems and suggest that it can improve picking performance compared with random and turnover-based storage policies. With the definition of correlation in correlated storage assignment, products that are frequently ordered together have high degrees of correlation.…”

Section: Storage Location Assignmentmentioning

confidence: 99%

Storage Location Assignment for Improving Human–Robot Collaborative Order-Picking Efficiency in Robotic Mobile Fulfillment Systems

Chen,

2024

Sustainability

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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.

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“…Simulation was used to compare traditional and high-density storage systems. Mirzaei et al [2021] considered the problem of product storage in a RMFS, with the aim being to store product so as to minimise expected robot travel time. A mixed-integer linear program for storage allocation was presented, as well as a construction and improvement heuristic.…”

Section: Literature Reviewmentioning

confidence: 99%

A two-stage approach for order and rack allocation in a mobile rack environment

Valle¹,

Beasley²

2021

Preprint

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In this paper we investigate a problem associated with operating a robotic mobile fulfilment system (RMFS). This is the problem of allocating orders and mobile storage racks to pickers.We present a two-stage formulation of the problem. In our two-stage approach we, in the first-stage, deal with the orders which must be definitely fulfilled (picked), where the racks chosen to fulfil these first-stage orders are chosen so as to (collectively) contain sufficient product to satisfy all orders. In the second-stage we restrict attention to those racks chosen in the first-stage solution in terms of allocating second-stage orders.We present three different strategies for first-stage order selection; one of these strategies minimises the requirement to make decisions as to the rack sequence (i.e. the sequence in which racks are presented to each picker).We present a heuristic procedure to reduce the number of racks that need to be considered and too present a heuristic for order and rack allocation based on partial integer optimisation that makes direct use of our two-stage formulation.Extensive computational results are presented for test problems that are made publicly available.

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How to benefit from order data: correlated dispersed storage assignment in robotic warehouses

Cited by 25 publications

References 30 publications

Item Storage Assignment Problem in Robotic Mobile Fulfillment Systems With Nonempty Pods

Item Storage Assignment Problem in Robotic Mobile Fulfillment Systems With Nonempty Pods

Storage Location Assignment for Improving Human–Robot Collaborative Order-Picking Efficiency in Robotic Mobile Fulfillment Systems

A two-stage approach for order and rack allocation in a mobile rack environment

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