%0 Journal Article %T Pareto-based Multi-criteria Evolutionary Algorithm for Parallel Machines Scheduling Problem with Sequence-dependent Setup Times %J International Journal of Engineering %I Materials and Energy Research Center %Z 1025-2495 %A Rezaeian Zeidi, J. %A Zarei, M. %A Shokoufi, K. %D 2017 %\ 12/01/2017 %V 30 %N 12 %P 1863-1869 %! Pareto-based Multi-criteria Evolutionary Algorithm for Parallel Machines Scheduling Problem with Sequence-dependent Setup Times %K multi %K objective optimization %K Unrelated parallel machine %K just %K In %K time Scheduling %K Controlled elitism non %K dominated sorting genetic algorithm %K mixed integer programming %K Sequence %K dependent setup time %R %X This paper addresses an unrelated multi-machine scheduling problem with sequence-dependent setup time, release date and processing set restriction to minimize the sum of weighted earliness/tardiness penalties and the sum of completion times, which is known to be NP-hard. A Mixed Integer Programming (MIP) model is proposed to formulate the considered multi-criteria problem. Also, to solve the model for real-sized applications, a Pareto-based algorithm, namely controlled elitism non-dominated sorting genetic algorithm (CENSGA), is proposed. To validate its performance, the algorithm is examined under six performance metric measures, and compared with a Pareto-based algorithm, namely NSGA-II. The results are statistically evaluated by the Mann–Whitney test and t-test methods. From the obtained results based on the t-test, the proposed CENSGA significantly outperforms the NSGA-II in four out of six terms. Additionally, the statistical results from Mann–Whitney test show that the performance of the proposed CENSGA is better than the NSGA- II in two out of six terms. Finally, the experimental results indicate the effectiveness of the proposed algorithm for different problems. %U https://www.ije.ir/article_73076_0206e099bd9ad1d54af1a53028ae7e18.pdf