Advertisement

Journal of Intelligent Manufacturing

, Volume 30, Issue 5, pp 2257–2272 | Cite as

Two-agent stochastic flow shop deteriorating scheduling via a hybrid multi-objective evolutionary algorithm

  • Yaping Fu
  • Hongfeng WangEmail author
  • Guangdong TianEmail author
  • Zhiwu Li
  • Hesuan Hu
Article

Abstract

Multi-agent and deteriorating scheduling has gained an increasing concern from academic and industrial communities in recent years. This study addresses a two-agent stochastic flow shop deteriorating scheduling problem with the objectives of minimizing the makespan of the first agent and the total tardiness of the second agent. In the investigated problem, the normal processing time of jobs is a random variable, and the actual processing time of jobs is a linear function of their normal processing time and starting time. To solve this problem efficiently, this study proposes a hybrid multi-objective evolutionary algorithm which is a combination of an evolutionary algorithm and a local search method. It maintains two populations and one archive. The two populations are utilized to execute the global and local searches, where one population employs an evolutionary algorithm to explore the whole solution space, and the other applies a local search method to exploit the promising regions. The archive is used to guide the computation resource allocation in the search process. Some special techniques, i.e., evolutionary methods, local search methods and information exchange strategies between two populations, are designed to enhance the exploration and exploitation ability. Comparing with the classical and popular multi-objective evolutionary algorithms on some test instances, the experimental results show that the proposed algorithm can produce satisfactory solution for the investigated problem.

Keywords

Flow shop scheduling Deteriorating scheduling Multi-objective multi-agent scheduling Multi-objective evolutionary algorithm Multipopulation 

Notes

Acknowledgements

This work is partly supported by the National Science Foundation for Distinguished Young Scholars of China under Grant Nos. 71325002, 61525302; Major International Joint Research Project of NSFC under Grant No. 71620107003; the Foundation for Innovative Research Groups of National Science Foundation of China under Grant No. 61621004; the National Nature Science Foundation of China under Grant Nos. 61703220, 71671032, 51775238; Shandong Provincial Natural Science Foundation, China under Grant No ZR2016FP02; China Postdoctoral Science Foundation Funded Project under Grant No. 2017M610407; Qingdao Postdoctoral Research Project under Grant No. 2016027.

References

  1. Bai, L. P., Wu, N. Q., Li, Z. W., & Zhou, M. C. (2016). Optimal one-wafer cyclic scheduling and buffer space configuration for single-arm multicluster tools with linear topology. IEEE Transactions on Systems Man & Cybernetics Systems, 46(10), 1456–1467.Google Scholar
  2. Branke, J., Su, N., Pickardt, C. W., & Zhang, M. J. (2016). Automated design of production scheduling heuristics: A review. IEEE Transactions on Evolutionary Computation, 20(1), 110–114.Google Scholar
  3. Cai, X. Y., Li, Y. X., Fan, Z., & Zhang, Q. F. (2015). An external archive guided multiobjective evolutionary algorithm based on decomposition for combinatorial optimization. IEEE Transactions on Evolutionary Computation, 19(4), 508–523.Google Scholar
  4. Chang, P. C., Chen, S. H., Zhang, Q. F., & Lin, J. L. (2008). MOEA/D for flowshop scheduling problems. In Proceeding of 2008 IEEE congress on evolutionary computation (pp. 1433–1438).Google Scholar
  5. Cheng, M., Sun, S., & He, L. (2014). Discrete optimization flow shop scheduling problems with deteriorating jobs on no-idle dominant machines. European Journal of Operational Research, 183(1), 58–62.Google Scholar
  6. Cheng, M., Tadikamalla, P. R., Shang, J., & Zhang, B. (2014a). Two-machine flow shop scheduling with deteriorating jobs: Minimizing the weighted sum of makespan and total completion time. Journal of the Operational Research Society, 66(5), 709–719.Google Scholar
  7. Cheng, M., Tadikamalla, P. R., Shang, J., & Zhang, S. Q. (2014b). Bicriteria hierarchical optimization of two-machine flow shop scheduling problem with time-dependent deteriorating jobs. European Journal of Operational Research, 234(3), 650–657.Google Scholar
  8. Deb, K. (2014). Multi-objective optimization search methodologies (pp. 403–449). US: Springer.Google Scholar
  9. Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002). A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Transactions on Evolutionary Computation, 6(2), 182–197.Google Scholar
  10. Fan, B. Q., & Cheng, T. C. E. (2016). Two-agent scheduling in a flowshop. European Journal of Operational Research, 252(2), 376–384.Google Scholar
  11. Fu, Y. P., Wang, H. F., & Huang, M. (2014). Locate multiple pareto optima using a species-based multi-objective genetic algorithm. In Proceeding of 2014 International Conference on Bio-inspired Computing: Theories and Applications (pp. 128–137).Google Scholar
  12. Fu, Y. P., Wang, H. F., Huang, M., Ding, J. L., & Tian, G. D. (2017) Multiobjective flow shop deteriorating scheduling problem via an adaptive multipopulation genetic algorithm. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture.  https://doi.org/10.1177/0954405417691553.
  13. Fu, Y. P., Wang, H. F., Huang, M., & Wang, J. W. (2016). A decomposition based multiobjective genetic algorithm with adaptive multipopulation strategy for flowshop scheduling problem. Natural Computing.  https://doi.org/10.1007/s1104.
  14. Gen, M., & Lin, L. (2014). Multiobjective evolutionary algorithm for manufacturing scheduling problems: State-of-the-art survey. Journal of Intelligent Manufacturing, 25(5), 849–866.Google Scholar
  15. Gupta, J. N. D., & Gupta, S. K. (1988). Single facility scheduling with nonlinear processing times. Computers & Industrial Engineering, 14(44), 387–393.Google Scholar
  16. Hou, Y., Wu, N. Q., Zhou, M. C., & Li, Z. W. (2017). Pareto-optimization for scheduling of crude oil operations in refinery via genetic algorithm. IEEE Transactions on Systems Man & Cybernetics Systems, 47(3), 517–530.Google Scholar
  17. Hu, H. S., Zhou, C. M., Li, Z. W., & Tang, Y. (2013). An optimization approach to improved Petri net controller design for automated manufacturing systems. IEEE Transactions on Automation Science & Engineering, 10(3), 772–782.Google Scholar
  18. Ishibuchi, H., & Murata, T. (1998). A multi-objective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems Man & Cybernetics Part C Applications & Reviews, 28(3), 392–403.Google Scholar
  19. Ishibuchi, H., Yoshida, T., & Murata, T. (2003). Balance between genetic search and local search in memetic algorithms for multiobjective permutation flowshop scheduling. IEEE Transactions on Evolutionary Computation, 7(2), 204–223.Google Scholar
  20. Jiang, Z. G., Zhou, T. T., Zhang, H., Wang, Y., Cao, H. J., & Tian, G. D. (2016). Reliability and cost optimization for remanufacturing process planning. Journal of Cleaner Production, 135, 1602–1610.Google Scholar
  21. Jin, L. L., Zhang, C. Y., & Shao, X. Y. (2015). An effective hybrid honey bee mating optimization algorithm for integrated process planning and scheduling problems. The International Journal of Advanced Manufacturing Technology, 80(5), 1253–1264.Google Scholar
  22. Lee, W. C., Chen, S. K., Chen, C. W., & Wu, C. C. (2011). A two-machine flowshop problem with two agents. Computers & Operations Research, 38(1), 98–104.Google Scholar
  23. Lee, W. C., Yen, W. C., & Chung, Y. H. (2014). Total tardiness minimization in permutation flowshop with deterioration consideration. Applied Mathematical Modelling, 38(13), 3081–3092.Google Scholar
  24. Lei, D. (2015). Variable neighborhood search for two-agent flow shop scheduling problem. Computers & Industrial Engineering, 80, 125–131.Google Scholar
  25. Li, J. Q., Pan, Q. K., & Mao, K. (2016). A hybrid fruit fly optimization algorithm for the realistic hybrid flowshop rescheduling problem in steelmaking systems. IEEE Transactions on Automation Science & Engineering, 13(2), 932–949.Google Scholar
  26. Lin, S. W., & Ying, K. C. (2013). Minimizing makespan and total flowtime in permutation flowshops by a bi-objective multi-start simulated-annealing algorithm. Computers & Operations Research, 40(6), 1625–1647.Google Scholar
  27. Liu, H. L., Chen, L., Deb, K., & Goodman, E. (2016). Investigating the effect of imbalance between convergence and diversity in evolutionary multi-objective algorithms. IEEE Transactions on Evolutionary Computation.  https://doi.org/10.1109/TEVC.2016.2606577.
  28. Liu, L. L., Wang, D. W., & Ip, W. H. (2009). A permutation-based dual genetic algorithm for dynamic optimization problems. Soft Computing, 13(7), 725–738.Google Scholar
  29. Liu, Y. F., Pan, Q. K., & Chai, T. Y. (2015). Magnetic material group furnace problem modeling and the specialization of the genetic algorithm. IEEE Transactions on Engineering Management, 62(1), 51–64.Google Scholar
  30. Long, J. Y., Zheng, Z., Gao, X. Q., & Pardalos, P. M. (2016). A hybrid multi-objective evolutionary algorithm based on NSGA-II for practical scheduling with release times in steel plants. Journal of the Operational Research Society.  https://doi.org/10.1057/jors.2016.17.
  31. Luo, W. C., Chen, L., & Zhang, G. C. (2012). Approximation schemes for two-machine flow shop scheduling with two agents. Journal of Combinatorial Optimization, 24, 229–239.Google Scholar
  32. Ma, X. L., Liu, F., Qi, Y. T., Wang, X. D., Li, L. L., Jiao, L. C., et al. (2016). A multiobjective evolutionary algorithm based on decision variable analyses for multiobjective optimization problems with large-scale variables. IEEE Transactions on Evolutionary Computation, 20(2), 275–298.Google Scholar
  33. Miettinen, K. (2012). Nonlinear multiobjective optimization. Berlin: Springer.Google Scholar
  34. Mor, B., & Mosheiov, G. (2014). Polynomial time solutions for scheduling problems on a proportionate flowshop with two competing agents. Journal of Operational Research Society, 65, 151–157.Google Scholar
  35. Pinedo, M. (2012). Scheduling, theory, algorithms and systems. New Jersey: Prentice Hall.Google Scholar
  36. Tian, G. D., Zhang, H., Feng, Y., Wang, D., Peng, Y., & Jia, H. (2018). Green decoration materials selection under interior environment characteristics: A grey-correlation based hybrid MCDM method. Renewable and Sustainable Energy Reviews, 81(1), 682–692.Google Scholar
  37. Tian, G. D., Zhou, M. C., & Chu, J. W. (2013). A chance constrained programming approach to determine the optimal disassembly sequence. IEEE Transactions on Automation Science & Engineering, 10(4), 1004–1013.Google Scholar
  38. Tian, G. D., Zhou, M. C., Li, P. G., Zhang, C. Y., & Jia, H. F. (2016). Multiobjective optimization models for locating vehicle inspection stations subject to stochastic demand, varying velocity and regional constraints. IEEE Transactions on Intelligent Transportation Systems, 17(7), 1978–1986.Google Scholar
  39. Trivedi, A., Srinivasan, D., Sanyal, K., & Ghosh, A. (2016). A survey of multiobjective evolutionary algorithms based on decomposition. IEEE Transactions on Evolutionary Computation.  https://doi.org/10.1109/TEVC.2016.2608507.
  40. Wang, H. F., Fu, Y. P., Huang, M., & Wang, J. W. (2015). Multiobjective optimisation design for enterprise system operation in the case of scheduling problem with deteriorating jobs. Enterprise Information Systems, 10(3), 1–18.Google Scholar
  41. Wang, J. B., & Wang, M. Z. (2013). Solution algorithms for the total weighted completion time minimization flow shop scheduling with decreasing linear deterioration. International Journal of Advanced Manufacturing Technology, 67(1–4), 243–253.Google Scholar
  42. Wang, S. Y., Wang, L., Liu, M., & Xu, Y. (2013). An enhanced estimation of distribution algorithm for solving hybrid flow-shop scheduling problem with identical parallel machines. International Journal of Advanced Manufacturing Technology, 68(9–12), 2043–2056.Google Scholar
  43. Wang, X., Khemaissia, I., Khalgui, M., Li, Z. W., Mosbahi, O., & Zhou, M. C. (2015). Dynamic low-power reconfiguration of real-time systems with periodic and probabilistic tasks. IEEE Transactions on Automation Science and Engineering, 12(1), 258–271.Google Scholar
  44. Wang, X., Li, Z. W., & Wonham, W. M. (2016). Dynamic multiple-period reconfiguration of real-time scheduling based on timed DES supervisory control. IEEE Transactions on Industrial Informatics, 12(1), 101–111.Google Scholar
  45. Wang, X. Y., & Wang, J. J. (2014). Scheduling deteriorating jobs with a learning effect on unrelated parallel machines. Applied Mathematical Modelling, 38(21), 5231–5238.Google Scholar
  46. Yin, Y., Wu, W. H., Cheng, T. C. E., & Wu, C. C. (2014). Due-date assignment and single-machine scheduling with generalised position-dependent deteriorating jobs and deteriorating multi-maintenance activities. International Journal of Production Research, 52(8), 2311–2326.Google Scholar
  47. Zhang, Q. F., & Li, H. (2007). MOEA/D: A multiobjective evolutionary algorithm based on decomposition. IEEE Transactions on Evolutionary Computation, 11(6), 712–731.Google Scholar
  48. Zhang, Q. F., Zhou, A. M., & Jin, Y. (2008). RM-MEDA: A regularity model-based multiobjective estimation of distribution algorithm. IEEE Transactions on Evolutionary Computation, 12(1), 41–63.Google Scholar
  49. Zhang, J. R., Tang, Q. H., Li, P., Deng, D. X., & Che, Y. L. (2016). A modified MOEA/D approach to the solution of multi-objective optimal power flow problem. Applied Soft Computing, 47, 494–514.Google Scholar
  50. Zhou, A. M., Qu, B. Y., Li, H., Zhao, S. Z., Suganthan, P. N., & Zhang, Q. F. (2011). Multiobjective evolutionary algorithms: A survey of the state of the art. Swarm & Evolutionary Computation, 1(1), 32–49.Google Scholar
  51. Zhou, A. M., & Zhang, Q. F. (2016). Are all the subproblems equally important? Resource allocation in decomposition-based multiobjective evolutionary algorithms. IEEE Transactions on Evolutionary Computation, 20(1), 52–64.Google Scholar
  52. Zitzler, E., Thiele, L., Laumanns, M., Fonseca, C. M., & Fonseca, V. G. D. (2003). Performance assessment of multiobjective optimizers: An analysis and review. IEEE transactions on evolutionary computation, 7(2), 117–132.Google Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Institute of Complexity ScienceQingdao UniversityQingdaoChina
  2. 2.College of Information Science and EngineeringNortheastern UniversityShenyangChina
  3. 3.College of TransportationJilin UniversityChangchunChina
  4. 4.Institute of Systems EngineeringMacau University of Science and TechnologyMacauChina
  5. 5.School of Electro-Mechanical EngineeringXidian UniversityXi’anChina

Personalised recommendations