Genetic algorithm application for permutation flow shop scheduling problems

In this paper, permutation flow shop scheduling problems (PFSS) are investigated with a genetic algorithm. PFSS problem is special type of problem. problem, there n jobs to be processed on m machines in series. Each job has follow the same machine order and each must process order. The most common performance criterion literature makespan for problems. algorithm applied minimize makespan. Taillard’s instances including 20, 50, 100 5, 10, 20 used define efficiency proposed GA by considering lower bounds or optimal values instances. Furthermore, sensitivity analysis made parameters shows that crossover probability does not affect solution quality elapsed time. Supplementary parameter tuning GA, we compare our an existing experimental study reveals well-tuned outperforms when objective