non-dominated ranked genetic algorithm

This paper studies the flexible job shop scheduling problem with parallel machines by considering cleaner production criteria, dual human-machine resources, job release date, and machine speed-dependent processing time. The objective functions of this problem include minimizing the sum of earliness and tardiness and the speed increasing. Here it is assumed that the speed of the machines can be increased to reduce the completion time while the increasing the speed leads to increasing the noise pollution in the production environment, and due to the cleaner production approach which is a preventive approach, an attempt has been made to reduce the amount of noise pollution by minimizing the speed increasing. In this regard, first, a mixed integer-programming model was developed, and since the model is bi-objective and NP-hard, a NRGA is proposed and the obtained results are compared with the NSGAII considering some multi-objectives criteria. The results show that the proposed algorithm considering the MID criterion in instances with 10 and 25 jobs and considering the RAS criterion in instances with 25 and 100 jobs have better performance compare to the NSGAII. Furthermore, the TOPSIS method is also used for analysis and the results show the efficiency of the proposed algorithm.