Scheduling of a flexible job‐shop using a multi‐objective genetic algorithm
Journal of Advances in Management Research
ISSN: 0972-7981
Article publication date: 26 October 2012
Abstract
Purpose
The purpose of this paper is to solve a flexible job shop scheduling problem where alternate machines are available to process the same job. The study considers the Flexible Job Shop Problem (FJSP) having n jobs and more than three machines for scheduling.
Design/methodology/approach
FJSP for n jobs and more than three machines is non polynomial (NP) hard in nature and hence a multi‐objective genetic algorithm (GA) based approach is presented for solving the scheduling problem. The two objective functions formulated are minimizations of the make‐span time and total machining time. The algorithm uses a unique method of generating initial populations and application of genetic operators.
Findings
The application of GA to the multi‐objective scheduling problem has given optimum solutions for allocation of jobs to the machines to achieve nearly equal utilisation of machine resources. Further, the make span as well as total machining time is also minimized.
Research limitations/implications
The model can be extended to include more machines and constraints such as machine breakdown, inspection etc., to make it more realistic.
Originality/value
The paper presents a successful implementation of a meta‐heuristic approach to solve a NP‐hard problem of FJSP scheduling and can be useful to researchers and practitioners in the domain of production planning.
Keywords
Citation
Agrawal, R., Pattanaik, L.N. and Kumar, S. (2012), "Scheduling of a flexible job‐shop using a multi‐objective genetic algorithm", Journal of Advances in Management Research, Vol. 9 No. 2, pp. 178-188. https://doi.org/10.1108/09727981211271922
Publisher
:Emerald Group Publishing Limited
Copyright © 2012, Emerald Group Publishing Limited