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GENETIC ALGORITHM OPTIMIZATION OF FLOW SHOP SCHEDULING PROBLEM WITH SEQUENCE DEPENDENT SETUP TIME AND LOT SPLITTING

The Flow-shop scheduling is a schedule planning for large volume systems with very less variations in requirements. In flow-shop scheduling problem (FSSP) environment, the objective of this paper is to find an optimal schedule ordering of M machines for the N jobs for Flow shop problem with sequence dependent setup time and lot splitting using genetic algorithm approach (GA). Present work considers two case studies. One case study represents the conventional flow shop whereas other case study represents the general flow shop. In conventional flow shop, all jobs require every machine in a shop whereas in general flow shop a job may not require every machine in a shop. For each case study, five simulation runs are performed for each combination of crossover and mutation probabilities in order to optimize makespan and to find job sequence.Results indicate that optimized value of makespan can be achieved through various job sequence instead of one and job splitting assist the scheduler in reducing makespan.