Scheduling of a flexible job‐shop using a multi‐objective genetic algorithm

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.