Title: Using genetic algorithms for order batching in multi-parallel-aisle picker-to-parts systems

Authors: Jose Alejandro Cano; Alexander Correa-Espinal; Rodrigo Gómez-Montoya

Addresses: Universidad de Medellín, Carrera 87 # 30-65 Medellín, Colombia ' Universidad Nacional de Colombia, Av. 80 # 65 – 223 Medellín, Colombia ' ESACS – Escuela Superior en Administración de Cadena de Suministro, Calle 4 # 18-55 Medellín, Colombia

Abstract: This article aims to introduce a metaheuristic to solve the order batching problem in multi-parallel-aisle warehouse systems to minimise the travelled distance. The proposed metaheuristic is based on an item-oriented genetic algorithm (GA) using a new chromosome representation where a gen represents a customer order to guarantee feasibility in the mutation operator, decreasing the correction of chromosomes generated by the crossover operator, and avoiding the calculation of the minimum number of feasible batches. When comparing the performance of the proposed algorithm with the first-come-first-served (FCFS) rule in 360 instances, we found average savings of 11% (up to 24%) in travelled distance and 2% (up to 17%) in the number of batches. The proposed algorithm can be easily integrated into a warehouse management system (WMS) to provide significant savings in travelled distances, increasing the efficiency of order-picking operations, and reducing the consumption of energy sources required by picking devices.

Keywords: order batching; genetic algorithms; travelled distance; picker-to-parts systems; warehouse management.

DOI: 10.1504/IJADS.2020.110606

International Journal of Applied Decision Sciences, 2020 Vol.13 No.4, pp.417 - 434

Received: 07 Jun 2019
Accepted: 07 Sep 2019
Published online: 26 Oct 2020 *

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