Abstract

PurposeThe 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/approachFJSP 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.FindingsThe 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/implicationsThe model can be extended to include more machines and constraints such as machine breakdown, inspection etc., to make it more realistic.Originality/valueThe 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.

Talk to us

Join us for a 30 min session where you can share your feedback and ask us any queries you have

Schedule a call

Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.