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A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations

机译:一种混合人工神经网络,遗传算法和柱生成启发式,用于在手动拣选操作中最大限度地减少MakEspan

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摘要

At an operational level, order picking is the main activity in fulfillment centers. Motivated by and through collaboration with a third party logistic company, this study presents a novel hybrid column generation (CG), genetic algorithm (GA), and artificial neural network (ANN) heuristic for minimizing makespan in manual order picking operations. The results of column generation heuristic is compared against a mixed integer programming model solved by Gurobi, and a parallel simulated annealing and ant colony optimization (PSA-ACO) previously proposed in the literature. Through numerical experiments, the superiority of CG heuristic compared to other methods is shown, and some managerial insights regarding the relationship between makespan optimization, workload balance, picking capacity, and number of pickers in order picking operations is presented. (C) 2020 Elsevier Ltd. All rights reserved.
机译:在运营层面,订单采摘是履行中心的主要活动。本研究通过与第三方物流公司的合作,提出了一种新颖的混合柱生成(CG),遗传算法(GA)和人工神经网络(ANN)启发式,用于在手动挑选操作中最大限度地减少MAKESPHAN。将列生成启发式的结果与Gurobi解决的混合整数编程模型进行比较,以及先前在文献中提出的并行模拟退火和蚁群优化(PSA-ACO)。通过数值实验,显示了与其他方法相比的CG启发式的优越性,并提出了关于MapEspan优化,工作负载平衡,拣选能力和拾取器中的拾取器之间关系的一些管理见解。 (c)2020 elestvier有限公司保留所有权利。

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