Genetic Algorithm for Scattered Storage Assignment in Kiva Mobile Fulfillment System

Guan, Mengcheng; Li, Zhenping

doi:10.4236/ajor.2018.86027

Cited by 14 publications

(7 citation statements)

References 15 publications

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“…Kim et al [23] addressed the ISAP to maximize the relevance of items on the pods, and the movement distance of the pods is reduced by the re-optimization algorithm. Guan and Li [24] studied the problem of decentralized storage allocation of items based on association rules in RMFS, aiming to determine the pods on which each item is placed to minimize the number of pods that need to be moved when picking a batch of orders. Tey proposed an integer programming model and developed a genetic algorithm to solve the problem.…”

Section: Te Study Of Isap In Rmfsmentioning

confidence: 99%

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Joint Optimization of Item and Pod Storage Assignment Problems with Picking Aisles’ Workload Balance in Robotic Mobile Fulfillment Systems

Zhang,

Tian,

Zhou

2024

Complexity

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The item and pod storage assignment problems, two critical issues at the strategic level in robotic mobile fulfillment systems, have a strong correlation and should be studied together. Moreover, the workload balance in each picking aisle needs to be considered in the storage assignment problems to avoid robots’ congestion within picking aisles. Motivated by these, the joint optimization of item and pod storage assignment problems (J-IPSAP) with picking aisles’ workload balance is studied. The mixed integer programming model of the J-IPSAP with the workload balance constraint is formulated to minimize the robots’ movement distance. The improved genetic algorithm (IGA) with the decentralized pod storage assignment strategy is designed to solve the J-IPSAP model. The experimental results show that the IGA can obtain high-quality solutions when compared with Gurobi and the two-stage heuristic algorithms. The robots’ movement distance is smallest when the width-to-length ratio of the storage area is close to 1, and the robots’ movement distance will increase with more stringent workload balance constraints.

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Section: Te Study Of Isap In Rmfsmentioning

confidence: 99%

“…Terefore, Pc/Pm � {0.8/0.2} is applicable in this paper. Te hyperparameter settings are also the same with Guan and Li [24]. Te optimization solver Gurobi is used to verify the correctness of the J-IPSAP model and the efectiveness of IGA.…”

Section: Comparative Analysis With Gurobi Optimization Solutions Inmentioning

confidence: 99%

Joint Optimization of Item and Pod Storage Assignment Problems with Picking Aisles’ Workload Balance in Robotic Mobile Fulfillment Systems

Zhang,

Tian,

Zhou

2024

Complexity

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“…They also find that carefully determining the number of picking stations is essential to maintain a high picker utilisation while avoiding congestion. Guan and Li (2018) derive association rules from historical demand data to decide which items to store together on a shelf, to maximise item similarity (the probability that items from the same shelf are ordered together) within the context of scattered storage. They propose a non-linear MIP and a genetic algorithm to solve Robotic Mobile Fulfillment Systems: a Mathematical Modelling Framework for E-commerce Applications CIRRELT-2020-42 it, and they obtain a higher item similarity of about 35%, but their results are not converted to actual productivity gains through simulations.…”

Section: Literature Reviewmentioning

confidence: 99%

Robotic mobile fulfillment systems: a mathematical modelling framework for e-commerce applications

Rimélé

Gamache

Gendreau

et al. 2021

International Journal of Production Research

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Robotic Mobile Fulfillment Systems (RMFSs) are a new type of automated warehouse deployed in e-commerce. In this parts-to-picker system, a fleet of small robots is tasked with retrieving and storing shelves of items in the warehouse. Due to the nature of the e-commerce market, and the high flexibility of RMFSs, there are many opportunities to improve the productivity of the warehouse by optimising operational decisions. Online retailers promise extremely fast deliveries, which requires that new orders be included in the set of requests to fulfil as soon as they are revealed. For this reason, and because of the very dynamic nature of the robots' cycles, decision-making needs to be done in real time, in an uncertain environment. Because such a problem often lacks a formal description, we propose a mathematical framework that models the operational decisions taking place in an RMFS as a stochastic dynamic program. Our objective is to formalise optimisation opportunities, to allow researchers to develop more advanced methods in a well-defined environment. Embedded in a discrete event simulator, this model is illustrated by simulations to compare against standard storage decision rules.

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“…Numerous studies have shown that the correlation between goods, the probability that they appear in the same order at the same time, makes it possible to improve the efficiency of picking through storage planning (Chen HP and Huang Y2021;Guan M and Li Z 2018). In addition, the frequency of selection of goods is affected by the heat of sale.…”

Section: Introductionmentioning

confidence: 99%

Optimization of Storage Allocation of Mobile Racks Based on Demand Forecast

wang,

2023

Preprint

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The complexity of a single multi-product order structure of retail e-commerce enterprises. There is a difficult situation of time-consuming step-by-step picking and a heavy load of rack handling. The product fails to take advantage of the flexibility of the mobile racks. Matching with the shelves according to the sales rules results in a long pick-and-walk distance. The general classification storage strategy in the warehouse makes it difficult to deal with fragmented picking. Given this, the optimization of the storage position of mobile racks in the warehouse of retail e-commerce enterprises is studied. A combination optimization model is established to minimize the length of the pick road. The first stage is the introduction of complex network abstraction of each commodity for the nodes to establish the correlation network, mining within the order of the commodity demand correlation for clustering; In the second stage, a subset of (1, N) Grey-Prediction model related factor series is used to distribute racks for the first stage of clustered commodity portfolio using single parent evolutionary genetic algorithm under elite strategy based on short-term sales prediction. The evaluation of the experimental data shows that compared to the current storage strategy of the enterprise, the optimized model yields a better-quality solution. Robustness perturbed by order size. At least a 2.85% reduction in pick length at large parameters. Rack handling is reduced by at least 5.62%. The result of a short distance and a small number of picks is realized.

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Genetic Algorithm for Scattered Storage Assignment in Kiva Mobile Fulfillment System

Cited by 14 publications

References 15 publications

Joint Optimization of Item and Pod Storage Assignment Problems with Picking Aisles’ Workload Balance in Robotic Mobile Fulfillment Systems

Joint Optimization of Item and Pod Storage Assignment Problems with Picking Aisles’ Workload Balance in Robotic Mobile Fulfillment Systems

Robotic mobile fulfillment systems: a mathematical modelling framework for e-commerce applications

Optimization of Storage Allocation of Mobile Racks Based on Demand Forecast

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