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

被引:30
作者
Ardjmand, Ehsan [1 ]
Ghalehkhondabi, Iman [2 ]
Young, William A., II [1 ]
Sadeghi, Azadeh [3 ]
Weckman, Gary R. [3 ]
Shakeri, Heman [4 ]
机构
[1] Ohio Univ, Coll Business, Dept Analyt & Informat Syst, Athens, OH 45701 USA
[2] Our Lady Lake Univ, Dept Business, San Antonio, TX 78207 USA
[3] Ohio Univ, Russ Coll Engn & Technol, Dept Ind & Syst Engn, Athens, OH 45701 USA
[4] Univ Virginia, Sch Data Sci, Charlottesville, VA 22904 USA
关键词
Order picking; Order batching; Column generation; Artificial neural networks; Genetic algorithm; VARIABLE NEIGHBORHOOD SEARCH; TABU SEARCH; DISTRIBUTION CENTERS; MULTIPLE PICKERS; ROUTING PROBLEM; WAVE PICKING; WAREHOUSE; OPTIMIZATION; SYSTEM; SOLVE;
D O I
10.1016/j.eswa.2020.113566
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
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.
引用
收藏
页数:13
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