Document Type : Original Article


1 Msc, Bu-Ali Sina University.

2 Associate Professor, Bu-Ali Sina University.


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.


Main Subjects

  1. Alto´e, W., Bissoli, D., Mauri, G., & Amaral, A. (2018). A clustering search metaheuristic for the bi-objective flexible job-shop scheduling problem, XLIV Latin American Computer Conference, 158-166.
  2. Asadizadeh, Y., Azizi, M., & Hamzeh, Y. (2018). Determination and ranking cleaner production criteria by using analy. Iranian Journal of Wood and Paper Industries, 8(4), 573-584 (In Persian).
  3. Aurich, J. C., Yang, X., Schröder, S., Hering-Bertram, M., Biedert, T., Hagen, H., & Hamann, B. (2021). Noise investigation in manufacturing systems: An acoustic simulation and virtual reality enhanced method. CIRP Journal of Manufacturing Science and Technology, 5(4), 337-347.
  4. Behnamian, J. (2016). Solving Complex Optimization Problems Methods and Algorithms. Bu-Ali Sina University Press (In Persian).
  5. Chen, c., Jiy, Z., & Wangz, Y. (2018). NSGA-II applied to dynamic flexible job shop scheduling problems with machine breakdown, Modern Physics Letters B, 32, 18401111- 18401119.
  6. Driss, E., Mallouli, R., & Hachicha, W. (2018). Mixed Integer Programming for job Shop Scheduling Problem with Seprable Sequence-Dependent Setup Times. American Journal of Mathematical and Computational Science, 3(1), 31-36.
  7. Fakhrzad, M.B., Sadeghieh, and A., & Emami, L. (2013). A New Multi-Objective Job Shop Scheduling with Setup Times Using a Hybrid Genetic Algorithm. International Journal of Engineering, 26(2), 207-218.
  8. Gholipour Kanani, Y., Tavakkoli Moghaddam, R., Tabari, M., Jafari Zarandini, Y., & Aryanezhad, M.B. (2011). Solving a Novel Multi-Objective Scheduing Problem in a Cellular Manufacturing System by a Hybrid Algorithm. Journal of Production and Operations Management, 2(2), 1-18 (In Persian).
  9. Golmohammadi, R. (2017). Oel Assessment Guideline for Noise and Vibration, Ministry of Health and Medical Education Environmental and Occupational Health Center (In Persian).
  10. Gonzalez, M., Vela, C., & Varela. R. (2015). Scatter Search with Path Relinking for the Flexible Job Shop Scheduling Problem. European Journal of Operational Research, 245(1), 1-31.
  11. Heydari, M., & Aazami, A. (2018). 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), 134-150.
  12. Huang, R., & Yu, S. (2016). Two-stage multiprocessor flow shop scheduling with deteriorating maintenance in cleaner production. Journal of Cleaner Production, 135, 276-283.
  13. Jamshidi, B., Asabati, M., & Ghanian, I. (2012). Common methods of noise reduction in the textile industry. Journal of Mechanical Engineering and Vibration, 3(1), 11-15 (In Persian).
  14. Li,, Huang, W., Wu, R., & Guo, K. (2020). An improved artificial bee colony algorithm for solving multi-objective low-carbon flexible job shop scheduling problem, Applied Soft Computing, 95, 1-14.
  15. Liu, Q., Tian, Y., Wang, C., Chekem, F., & Sutherland, J. (2017). Flexible job shop scheduling for reduced manufacturing carbon footprint. Journal of Manufacturing Science and Engineering, 140(6), 1-9.
  16. Lu, Y., & Jiang, T. (2019). Bi-Population Based Discrete Bat Algorithm for the Low-Carbon Job Shop Scheduling Problem, IEEE Access, 7, 14513-14522.
  17. Min, D., Dunbingb, T., Adrianac, G., & Miguel, S. (2019). Multi-objective optimization for energy-efficient flexible job-shop scheduling problem with transportation constraints, Robotics and Computer Integrated Manufacturing, 59, 143-157.
  18. Molavi, B, Esmaelian, M., Ansari, R. (2012). Identifying and Prioritizing Agility Drivers Using FTOPSISAnd Fractional Programming Approach, Journal of Industrial Management Perspective, 2(1), 91-114. (In Persian)
  19. Momeni, M. (2014). New Topics in Operations Research, Tehran: Management School Publications, (In Persian).
  20. Mohtashami, A., & Sagharichiha, M.N. (2018). Prioritizing Order Picking from Storage and Transferring to Production Department Based on a New Mathematical Model and Multi-objective Meta-heuristic Algorithms. International Journal of Industrial Engineering & Production Management, 28(1), 69-86 (In Persian).
  21. Naser Sadrabadi, A., & Sattarkhan, M.H. (2014). A binary programming model for parallel machines scheduling in a multi-product system. The Journal of Industrial Management Perspective, 4(2), 139-156. (In Persian)
  22. Nouri, H., Driss, O., & Ghedira, K. (2018). Solving the flexible job shop problem by hybrid Metaheuristics-based multi agent model. Journal of Engineering International, 14, 1-14.
  23. Rahimi, H., Azar, A., & Rezaei Pandari. (2015). Designing a Multi Objective Job Shop Scheduling Model and Solving itby Simulated Annealing. The Journal of Industrial Management Perspective, 5(3), 39-63. (In Persian)
  24. Rohaninejad, M., Kheirhkah, A., Fattahi, P., & Vahid-Nouri, B. (2015). A hybrid multi-objective genetic algorithm based on the ELECTRE method for a capacitated flexible job shop scheduling problem. The International Journal of Advanced Manufacturing Technology, 77, 51–66.
  25. Seng, D.W., Li, J.W., Fang, X.J., Zhang, X.F., & Chen, J. (2018). Low carbon flexible job shop scheduling based on improved non-dominated sorting genetic algorithm-II. International Journal of Simulation Modelling, 17(4), 712-723.
  26. Shen, L. (2014). A tabu search algorithm for the job shop problem with sequence dependent setup times.Computer & Industrial Engineering, 78, 95-106.
  27. Shen, L. Dauzere Peres, S., & Neufeld, J. (2018). Solving the flexible job shop scheduling problem with sequence-dependent setup times. European Journal of Operational Research, 265, 503-516.
  28. Wang, Y., & Zhu, Q. (2021). A Hybrid Genetic Algorithm for Flexible Job Shop Scheduling Problem with Sequence-Dependent Setup Times and Job Lag Times. IEEE Access, 9, 104864 – 104873.
  29. Wu, X., Peng, J., Xiao, X., &Wu, S. (2021). An effective approach for the dual-resource flexible job shop-scheduling problem considering loading and unloading, Journal of Intelligent Manufacturing, 32,707–728.
  30. Xiang, X., Liu. C., & Miao1, L. (2018). Minimizing the Maximum Flow Time for Flexible Job Shop Problem with Parallel Machines Considering Release Time. Advances in Intelligent Systems Research, 159, 208-211.
  31. Yang, X., Zeng, Z., Wang, R., & Sun, X. (2016). Bi-Objective Flexible Job-Shop Scheduling Problem Considering Energy Consumption under Stochastic Processing Times. PLOS ONE, 11(12), 1-13.
  32. Yazdani, M., Aleti, A., Khalili, S.M, & Jolai, F. (2017). Optimising the sum of maximum earliness and tardiness of the job shop scheduling problem. Computers & Industrial Engineering, 107, 12-24.
  33. Zambrano Rey, G., Bekrar, A.,Trentesaux, D., & Zhou, B. (2015). Solving the flexible job-shop just-in-time scheduling problem with quadratic earliness and tardiness costs. International Journal of Advansed, 81(9), 1-2.
  34. Z‌e‌g‌o‌r‌d‌i, S.H, Rahimi& Ghashghaie, M. (2010). F‌l‌e‌x‌i‌b‌l‌e j‌o‌b s‌h‌o‌p s‌c‌h‌e‌d‌u‌l‌i‌n‌g p‌r‌o‌b‌l‌e‌m; c‌o‌n‌s‌i‌d‌e‌r‌i‌n‌g m‌a‌i‌n‌t‌e‌n‌a‌n‌c‌e c‌o‌n‌s‌t‌r‌a‌i‌n‌t, Industrial Engineering & Management Sharif, 26(1), 101-115 (In Persian).
  35. Zhang, R., & Chiong, R. (2016). Solving the energy-efficient job shop scheduling problem: A multi-objective genetic algorithm with enhanced local search for minimizing the total Weighted tardiness and total energy consumption, Journal of Cleaner Production, 112, 3361-3375