fa A Scheduling Model for the Re-entrant Manufacturing System and Its Optimization by NSGA-II Research Paper مقاله پژوهشی In this study, a two-objective mixed-integer linear programming model (MILP) for multi-product re-entrant flow shop scheduling problem has been designed. As a result, two objectives are considered. One of them is maximization of the production rate and the other is the minimization of processing time. The system has m stations and can process several products in a moment. The re-entrant flow shop scheduling problem is well known as NP-hard problem and its complexity has been discussed by several researchers. Given that NSGA-II algorithm is one of the strongest and most applicable algorithm in solving multi-objective optimization problems, it is used to solve this problem. To increase algorithm performance, Taguchi technique is used to design experiments for algorithm’s parameters. Numerical experiments are proposed to show the efficiency and effectiveness of the model. Finally, the results of NSGA-II are compared with SPEA2 algorithm (Strength Pareto Evolutionary Algorithm 2). The experimental results show that the proposed algorithm performs significantly better than the SPEA2. In this study, a two-objective mixed-integer linear programming model (MILP) for multi-product re-entrant flow shop scheduling problem has been designed. As a result, two objectives are considered. One of them is maximization of the production rate and the other is the minimization of processing time. The system has m stations and can process several products in a moment. The re-entrant flow shop scheduling problem is well known as NP-hard problem and its complexity has been discussed by several researchers. Given that NSGA-II algorithm is one of the strongest and most applicable algorithm in solving multi-objective optimization problems, it is used to solve this problem. To increase algorithm performance, Taguchi technique is used to design experiments for algorithm’s parameters. Numerical experiments are proposed to show the efficiency and effectiveness of the model. Finally, the results of NSGA-II are compared with SPEA2 algorithm (Strength Pareto Evolutionary Algorithm 2). The experimental results show that the proposed algorithm performs significantly better than the SPEA2. Re-entrant Manufacturing System , Non-dominated Sorting Genetic Algorithm (NSGA-II) , Taguchi Parameter Setting , Re-entrant Manufacturing System , Non-dominated Sorting Genetic Algorithm (NSGA-II) , Taguchi Parameter Setting , 1413 1428 http://system.khu.ac.ir/ijsom/browse.php?a_code=A-10-392-4&slc_lang=fa&sid=1 Masoud Rabbani Masoud Rabbani mrabani@ut.ac.ir 1003194753284600145 1003194753284600145 Yes Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Safoura Famil Alamdar Safoura Famil Alamdar safouraalamdar@ut.ac.ir 1003194753284600146 1003194753284600146 No Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Department of Industrial Engineering, College of Engineering, University of Tehran, Tehran, Iran Parisa Famil Alamdar Parisa Famil Alamdar safouralamdar@ut.ac.ir 1003194753284600147 1003194753284600147 No Department of Industrial Engineering, Amir Kabir University, Tehran, Iran Department of Industrial Engineering, Amir Kabir University, Tehran, Iran