A genetic algorithm‐based approach for job shop scheduling
Journal of Manufacturing Technology Management
ISSN: 1741-038X
Article publication date: 7 September 2012
Abstract
Purpose
The purpose of this paper is to optimise the job shop scheduling problem using simulation and genetic algorithm.
Design/methodology/approach
The paper presents a simulation‐based genetic algorithm approach for the job shop scheduling problem. In total, three cases have been considered to access the performance of the job shop, with an objective to minimise mean tardiness and makespan. A restart scheme is embedded into regular genetic algorithm in order to avoid premature convergence.
Findings
Simulation‐based genetic algorithm can be used for job shop scheduling problems. Moreover, a restart scheme embedded into a regular genetic algorithm results in improvement in the fitness value. Single process plans selected on the basis of minimum production time criterion results in improved shop performance, as compared to single process plans selected randomly. Moreover, availability of multiple process plans during scheduling improves system performance measures.
Originality/value
The paper presents a simulation‐based genetic algorithm approach for job shop scheduling problem, with and without restart scheme. In this paper the effect of multiple process plans over single process plans, as well as criterion for selection of single process plans, are studied. The findings should be taken into account while designing scheduling systems for job shop environments.
Keywords
Citation
Phanden, R.K., Jain, A. and Verma, R. (2012), "A genetic algorithm‐based approach for job shop scheduling", Journal of Manufacturing Technology Management, Vol. 23 No. 7, pp. 937-946. https://doi.org/10.1108/17410381211267745
Publisher
:Emerald Group Publishing Limited
Copyright © 2012, Emerald Group Publishing Limited