Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems

Xie, Lin; Thieme, Nils; Krenzler, Ruslan; Li, Hanyi

doi:10.1016/j.ejor.2020.05.032

Cited by 79 publications

(31 citation statements)

References 16 publications

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“…First POA, then PPS is applied [1], [5]. Moreover, [8] performs an approach for integrating POA and PPS. Merschformann et al [5] analyze optimization performance by studying important decision rules such as POA, PPS, Replenishment Order Assignment (ROA), Replenishment Pod Selection (RPS) and Pod Storage Assignment (PSA).…”

Section: A Related Workmentioning

confidence: 99%

“…Their results imply that the provided heuristic approaches would take significant amount of time for larger instances and may not be feasible for real time applications. [8] integrate assignment of pods to station and orders to station for order picking process, instead of calculating separately. They also propose to split orders in order to improve RMFS efficiency.…”

Section: A Related Workmentioning

confidence: 99%

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Adaptive Task Planning for Multi-Robot Smart Warehouse

Bolu

Korçak

2021

IEEE Access

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Section: A Related Workmentioning

confidence: 99%

Section: A Related Workmentioning

confidence: 99%

Adaptive Task Planning for Multi-Robot Smart Warehouse

Bolu

Korçak

2021

IEEE Access

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“…Note that this model focuses on investigating the total benefit of a CDA policy, not a dynamic allocation in a rolling horizon. Valle and Beasley (2021) and Xie et al (2021) model assignment decisions on orders to pick stations and pods to pick stations in RMF systems.…”

Section: Mathematical Model Of Cdamentioning

confidence: 99%

“…Note, however, that this order can be the result of a prior batching process; (3) an order can be broken down over multiple pick stations. In this case, it is common to have a downstream order consolidation before dispatching, which this paper does not consider (See Xie et al 2021); (4) the total inventory of products is sufficient to pick all orders during the replenishment horizon; and (5) the product inventory on each pod is sufficiently large to pick any order line of the product. This assumption implies that each order line needs at most one pod.…”

Section: Mathematical Model Of Cdamentioning

confidence: 99%

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

Mirzaei

Zaerpour

Koster

2021

International Journal of Production Research

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In e-commerce fulfilment centres, storage assignment is critical to ensure short response times. To achieve this, many online retailers have moved to product dispersion in combination with product turnover-based slotting. However, commonly used policies do not fully utilise the historical customer demand information to optimise the storage assignment. This paper addresses a comprehensive approach to estimate the joint effects of 'turnover frequency', 'product correlation', and 'inventory dispersion' storage strategies on the expected order picking travel time in automated (robotic), partsto-picker systems. Additionally, it provides a thorough analysis of the impact of product correlation and turnover frequency on storage policies' performance. We develop a mixed-integer linear program for optimal product-to-cluster and cluster-to-zone allocation to minimise the robot's expected travel time. The travel time expressions are developed for different zone and station configurations. An efficient construction and improvement heuristic method is proposed and applied to a real dataset of a personal care products distributor. The analytical results show that the correlated dispersed assignment leads to a shorter expected travel time than the benchmark policies for order sets with sufficiently large order size. The demand correlation plays a major role in the performance of the models in the cases we tested.

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“…Vis [16] discussed literature related to design and control issues of AGV systems at manufacturing, distribution, transshipment, and transportation systems. Xie et al [17] concentrated on decisions about the assignment of shelves to stations and orders to stations in robotic mobile fulfillment systems, and they presented a new MIP model to integrate both decision problems. Gharehgozli and Zaerpour [18] studied the operational problem of scheduling a mobile robot fulfilling a set of customer orders from a pick station.…”

Section: Literature Reviewmentioning

confidence: 99%

AGV Scheduling Optimization for Medical Waste Sorting System

Quan

Lin

et al. 2021

Scientific Programming

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The dramatic increase in medical waste has put a severe strain on sorting operations. Traditional manual order picking is extremely susceptible to infection spread among workers and picking errors, while automated medical waste sorting systems can handle large volumes of medical waste efficiently and reliably. This paper investigates the optimization problem in the automated medical waste sorting system by considering the operational flow of medical waste. For this purpose, a mixed-integer programming model is developed to optimize the assignment among medical waste, presorting stations, and AGVs. An effective variable neighborhood search based on dynamic programming algorithm is proposed, and extensive numerical experiments are conducted. It is found that the proposed algorithm can efficiently solve the optimization problem, and the sensitivity analysis gives recommendations for the speed setting of the conveyor.

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Introducing split orders and optimizing operational policies in robotic mobile fulfillment systems

Cited by 79 publications

References 16 publications

Adaptive Task Planning for Multi-Robot Smart Warehouse

Adaptive Task Planning for Multi-Robot Smart Warehouse

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

AGV Scheduling Optimization for Medical Waste Sorting System

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