A hybrid artificial neural network, genetic algorithm and column generation heuristic for minimizing makespan in manual order picking operations. (30th November 2020)

• Record Type: Journal Article
• Authors: Ardjmand, Ehsan; Ghalehkhondabi, Iman; Young II, William A.; Sadeghi, Azadeh; Weckman, Gary R.; Shakeri, Heman
• Is Part Of: Expert systems with applications. Volume 159(2020)
• Publication Date: 2020-11-30
• Subjects: Order picking -- Order batching -- Column generation -- Artificial neural networks -- Genetic algorithm; Expert systems (Computer science) -- Periodicals; Systèmes experts (Informatique) -- Périodiques; Electronic journals; 006.33
• DOI: 10.1016/j.eswa.2020.113566
• Languages: English
• ISSNs: 0957-4174

Abstract

Highlights: 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. Abstract: 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.