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

• Published in: Expert systems with applications Vol. 159; p. 113566
• Main Authors: Ardjmand, Ehsan, Ghalehkhondabi, Iman, Young II, William A., Sadeghi, Azadeh, Weckman, Gary R., Shakeri, Heman
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 30.11.2020; Elsevier BV
• Subjects: Ant colony optimization; Artificial neural networks; Column generation; Genetic algorithm; Genetic algorithms; Heuristic; Heuristic methods; Integer programming; Mixed integer; Neural networks; Order batching; Order picking; Simulated annealing; Column generation; Order picking; Order batching; Genetic algorithm; Artificial neural networks
• ISSN: 0957-4174; 1873-6793
• DOI: 10.1016/j.eswa.2020.113566

•A soft computing-based column generation heuristic for order picking is proposed.•The proposed algorithm is compared against PSA-ACO and an exact method.•Based on numerical experiments some managerial insights are proposed. 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.