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

Jose Alejandro Cano, Alexander Correa-Espinal, Rodrigo Gómez-Montoya

Research output: Contribution to journalArticlepeer-review

3 Scopus citations


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.

Original languageEnglish
Pages (from-to)435-447
Number of pages13
JournalInternational Journal of Applied Decision Sciences
Issue number4
StatePublished - 2020


  • Genetic algorithms
  • Order batching
  • Picker-to-parts systems
  • Travelled distance
  • Warehouse management

Product types of Minciencias

  • B article - Q3


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