Reducing noise pollution by flexible job-shop scheduling with worker flexibility: Multi-subpopulation evolutionary algorithm

Document Type : Article


Department of Industrial Engineering, Faculty of Engineering, Bu-Ali Sina University, Hamedan, Iran


Flexible job-shop scheduling is one of the most critical production management topics. In this paper, it is also assumed job interruption due to the machine breakdown is allowed, and the processing time depends on the speed of the machines and requires both human and machine resources to process the jobs. Although, as the speed of the machine increases, the time of job’ completion reduces, an increase in speed results in an increase in noise pollution in the production environment, and with the aim of applying a cleaner production that is a preventative approach, it has been tried to reduce noise pollution by minimizing the increase in speed. After modeling the problem using the mixed-integer programming and solving it using the ε-constraint method, since the problem is NP-hard, a multi-subpopulation evolutionary algorithm is proposed to solve it. Finally, the proposed algorithm was compared with the NSGAII and the obtained results showed that it performs better than the NSGAII.


[1] Behnamian, J., Fatemi Ghomi, S.M.T., Jolai, F., et al. “Realistic two-stage flowshop batch scheduling problems with transportation capacity and time” Applied Mathematical Modelling, 36, pp. 723–735 (2012).
[2] Hillary, R. Environmental management systems and cleaner production: Wiley Toronto (1997).
[3] Scarazzato, T., Panossian, Z., Tenório, J., et al. “A review of cleaner production in electroplating industries using electrodialysis” Journal of Cleaner Production, 168, pp. 1590-1602 (2017).
[4] Mokhtari, H., & Hasani, A. “A multi-objective model for cleaner production-transportation planning in manufacturing plants via fuzzy goal programming” Journal of Manufacturing Systems, 44, pp. 230-242 (2017).
[5] Jariwala, H. J., Syed, H. S., Pandya, M. J., et al. “Noise Pollution & Human Health: A Review” Noise and Air Pollutions: Challenges and Opportunities. Ahmedabad: LD College of Engineering (2017).
[6] Zhang, J., Ding, G., Zou, Y., et al. “Review of job shop scheduling research and its new perspectives under Industry 4.0” Journal of Intelligent Manufacturing, 30(4), pp. 1809-1830 (2019).
[7] Yazdani, M., Zandieh, M., Tavakkoli-Moghaddam, R. “Evolutionary algorithms for multi-objective dual-resource constrained flexible job-shop scheduling problem” OPSEARCH, 56(3), pp. 983-1006 (2019).
[8] Shen, L., Dauzère-Pérès, S., Neufeld, J. S. “Solving the flexible job shop scheduling problem with sequence-dependent setup times” European Journal of Operational Research, 265(2), pp. 503-516 (2018).
[9] de Oliveira Neto, G. C., Santana, J. C. C., Godinho Filho, M., et al. “Assessment of the environmental impact and economic benefits of the adoption of cleaner production in a Brazilian metal finishing industry” Environmental technology, pp. 1-15 (2018).
[10] Rajaram, R., Jawahar, N., Ponnambalam, S., et al. “Multi-Objective Optimization of Economic and Environmental Aspects of a Three-Echelon Supply Chain” In Industry 4.0 and Hyper-Customized Smart Manufacturing Supply Chains (pp. 127-158): IGI Global (2019).
[11] Zarrouk, R., Bennour, I. E., Jemai, A. “A two-level particle swarm optimization algorithm for the flexible job shop scheduling problem” Swarm Intelligence, 13(2), pp. 145-168 (2019).
[12] Amjad, M. K., Butt, S. I., Kousar, R., et al. “Recent research trends in genetic algorithm based flexible job shop scheduling problems” Mathematical Problems in Engineering, (2018).
[13] Dai, M., Tang, D., Giret, A., et al. “Multi-objective optimization for energy-efficient flexible job shop scheduling problem with transportation constraints” Robotics and Computer-Integrated Manufacturing, 59, pp. 143-157 (2019).
[14] Abedi, M., Chiong, R., Noman, N., et al. “A multi-population, multi-objective memetic algorithm for energy-efficient job-shop scheduling with deteriorating machines” Expert Systems with Applications, 157, pp. 1-33 (2020).
[15] Li, X., Gao, L. “An effective hybrid genetic algorithm and tabu search for flexible job shop scheduling problem” International Journal of Production Economics, 174, pp. 93-110 (2016).
[16] Kundakcı, N., Kulak, O. “Hybrid genetic algorithms for minimizing makespan in dynamic job shop scheduling problem” Computers & Industrial Engineering, 96, pp. 31-51 (2016).
[17] AitZai, A., Benmedjdoub, B., Boudhar, M. “Branch-and-bound and PSO algorithms for no-wait job shop scheduling” Journal of Intelligent Manufacturing, 27(3), pp. 679-688 (2016).
[18] Wu, J., Wu, G., Wang, J. “Flexible job-shop scheduling problem based on hybrid ACO algorithm” International Journal of Simulation Modelling, 16(3), pp. 497-505 (2017).
[19] Wang, L., Cai, J., Li, M., et al. “Flexible job-shop scheduling problem using an improved ant colony optimization” Scientific Programming, 2017, pp. 1-11, (2017).
[20] Jamrus, T., Chien, C.-F., Gen, M., et al. “Hybrid particle swarm optimization combined with genetic operators for flexible job-shop scheduling under uncertain processing time for semiconductor manufacturing” IEEE Transactions on Semiconductor Manufacturing, 31(1), pp. 32-41 (2017).
[21] Gong, X., Deng, Q., Gong, G., et al. “A memetic algorithm for multi-objective flexible job-shop problem with worker flexibility” International Journal of Production Research, 7(56), pp. 2506-2522 (2017).
[22] Peng, C., Fang, Y., Lou, P., et al. “Analysis of double resource flexible job-shop scheduling problem based on genetic algorithm” 15th International Conference on Networking, Sensing and Control, pp. 1-6 (2018).
[23] Tamssaouet, K., Dauzère-Pérès, S., Yugma, C.” Metaheuristics for the Job-Shop Scheduling Problem with Machine Availability Constraints” Computers & Industrial Engineering, 125, pp. 1-16 (2018).
[24] Gong, G., Deng, Q., Chiong, R., et al. “An effective memetic algorithm for multi-objective job-shop scheduling” Knowledge-Based Systems, 182, pp. 1-14 (2019).
[25] Zhang, G., Hu, Y., Sun, J., et al. “An improved genetic algorithm for the flexible job shop scheduling problem with multiple time constraints” Swarm and Evolutionary Computation, 54, pp. 1-15 (2020).
[26] Ding, H., Gu, X. "Improved particle swarm optimization algorithm based novel encoding and decoding schemes for flexible job shop scheduling problem" Computers and Operations Research, 121, pp. 1-15 (2020).
[27] Yang, D., Zhou, X., Yang, Zh., et al. "Multi-objective optimization Model for Flexible Job Shop Scheduling Problem Considering Transportation Constraints: A Comparative Study" IEEE Congress on Evolutionary Computation (CEC), pp. 1-18 (2020).
[28] Heydari, M., Aazami, A. "Minimizing the maximum tardiness and makespan criteria in a job shop scheduling problem with sequence dependent setup times" Journal of Industrial and Systems Engineering, 11(2), pp. 134-150 (2018).
[29] Gao, K.-Z., Suganthan, P. N., Pan, Q.-K., et al. "Discrete harmony search algorithm for flexible job shop scheduling problem with multiple objectives" Journal of Intelligent Manufacturing, 27(2), pp. 363-374 (2016).
[30] Ebrahimi, A., Jeon, H. W., Lee, S., et al. "Minimizing total energy cost and tardiness penalty for a scheduling-layout problem in a flexible job shop system: A comparison of four metaheuristic algorithms" Computers & Industrial Engineering, 141, pp. 106295 (2020).
[31] Yazdani, M., Aleti, A., Khalili, S. M., et al. "Optimizing the sum of maximum earliness and tardiness of the job shop scheduling problem" Computers & Industrial Engineering, 107, pp. 12-24 (2017).
[32] Yu, J.-M., Lee, D.-H. "Solution algorithms to minimise the total family tardiness for job shop scheduling with job families" European Journal of Industrial Engineering, 12(1), pp. 1-23 (2018).
[33] Mendoza, A.M., Acosta, R.H., Reyes, J.C. "Production scheduling for a job shop using a mathematical model" Methodology, 11(12), pp. 13 (2018).
[34] Sadaghiani, J., Boroujerdi, S., Mirhabibi, M., et al. "A Pareto archive floating search procedure for solving multi-objective flexible job shop scheduling problem" Decision Science Letters, 3(2), pp. 157-168 (2014).
[35] Dalfard, V. M., Mohammadi, G. "Two meta-heuristic algorithms for solving multi-objective flexible job-shop scheduling with parallel machine and maintenance constraints" Computers & Mathematics with Applications, 64(6), pp. 2111-2117 (2012).
[36] Huang, S., Tian, N., Wang, Y., et al. "Multi-objective flexible job-shop scheduling problem using modified discrete particle swarm optimization" SpringerPlus, 5(1), pp. 1432 (2016).
[38] Behnamian, J., Zandieh, M., Fatemi Ghomi, S.M.T. "A multi-phase covering Pareto-optimal front method to multi-objective parallel machine scheduling" International Journal of Production Research, 48(17), pp. 4949–4976 (2010).
[37] Aurich, J. C., Yang, X., Schröder, S., et al. "Noise investigation in manufacturing systems: An acoustic simulation and virtual reality enhanced method" CIRP Journal of Manufacturing Science and Technology, 5(4), pp. 337-347 (2012).
[39] Behnamian, J., Fatemi Ghomi, S.M.T., Jolai, F. et al. "Minimizing makespan on a three-machine flowshop batch scheduling problem with transportation using genetic algorithm" Applied soft computing, 12, pp. pp. 768-777 (2012).
[40] Ahmadi, E., Zandieh, M., Farrokh, M., et al. "A multi objective optimization approach for flexible job shop scheduling problem under random machine breakdown by evolutionary algorithms" Computers & Operations Research, 73, pp. 56-66 (2016).
[41] Cuiyu, W., Yang, L., Xinyu, L. "Solving flexible job shop scheduling problem by a multi-swarm collaborative genetic algorithm" Journal of Systems Engineering and Electronics, 32, 2, pp. 261-271 (2021).
[42] Ehtesham Rasi, R." Optimization of the multi-objective flexible job shop scheduling model by applying NSGAII and NRGA algorithms" Journal of Industrial Engineering and Management Studies, 8, 1, pp. 45-71 (2021).