A Genetic Algorithm Applied Heuristic to Minimize the Makespan in a Flow Shop

In this work the flowshop scheduling situation in modern manufacturing has been analyzed. In a permutation flowshop design the machines arranged in series were the jobs are processed in a same order without eliminating any machine. Thus, it resulted in a need for development of more effective alternate methodology in finding an optimal solution with newer heuristic. A new heuristic (BAT heuristic) was proposed for the flow shop problems to achieve the minimal makespan by reaching the Lower Bound (LB) through a reverse engineering method. With this sequence resulting in optimal makespan reached sooner and with reduced computational time. This heuristic has been applied with Genetic Algorithm (GA) for further minimization of makespan. The GA applied BAT heuristic was evaluated by solving Taillard benchmark problem in MATLAB environment. The results were compared with traditional heuristics like CDS and NEH heuristics and found that the GA applied BAT heuristic yielded results better with 11% & 3% more compared to CDS and NEH heuristics. (C) 2014 The Authors. Published by Elsevier Ltd.