Multi-objective grouping genetic algorithm for the joint order batching, batch assignment, and sequencing problem

ABSTRACT

This article solves the order batching, batch assignment, and sequencing problem (JOBASP) given multiple objectives and heterogeneous picking vehicles in multi-parallel-aisle warehouse systems. A multi-objective grouping genetic algorithm (GGA) is developed to minimize total travel time and total tardiness by implementing an encoding scheme where a gene represents orders grouped in a batch and the assignment of the batch to a picking vehicle. Computer simulations show that the proposed algorithm performs 25.4% better than a first come, first served (FCFS) rule–based heuristic and 10.2% better than an earliest due date (EDD) rule–based heuristic. The proposed GGA provides significant savings of up to 46.8% and 28.4% on travel time and tardiness, respectively, for these benchmark heuristics. Therefore, this article introduces a GGA to solve the JOBASP with a reasonable computing time, making this approach interesting for warehouse operators using heterogeneous picking vehicles and addressing multiple objectives.

KEYWORDS:

• Order picking
• order batching
• batch assignment
• sequencing
• grouping genetic algorithms
• multi-objective problem
• joint order picking

DATA AVAILABILITY

The data will be accessible upon request.

Disclosure statement

The authors certify that they have NO affiliations with or involvement in any organization or entity with any financial or non-financial interest in the subject matter or materials discussed in this manuscript.