Journal of Intelligent Manufacturing

, Volume 25, Issue 5, pp 849–866 | Cite as

Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey

  • Mitsuo Gen
  • Lin Lin


Scheduling is an important tool for a manufacturing system, where it can have a major impact on the productivity of a production process. In order to find an optimal solution to scheduling problems it gives rise to complex combinatorial optimization problems. Unfortunately, most of them fall into the class of NP-hard combinatorial problems. In this paper, we focus on the design of multiobjective evolutionary algorithms (MOEAs) to solve a variety of scheduling problems. Firstly, we introduce fitness assignment mechanism and performance measures for solving multiple objective optimization problems, and introduce evolutionary representations and hybrid evolutionary operations especially for the scheduling problems. Then we apply these EAs to the different types of scheduling problems, included job shop scheduling problem (JSP), flexible JSP, Automatic Guided Vehicle (AGV) dispatching in flexible manufacturing system (FMS), and integrated process planning and scheduling (IPPS). Through a variety of numerical experiments, we demonstrate the effectiveness of these Hybrid EAs (HEAs) in the widely applications of manufacturing scheduling problems. This paper also summarizes a classification of scheduling problems, and illustrates the design way of EAs for the different types of scheduling problems. It is useful to guide how to design an effective EA for the practical manufacturing scheduling problems. As known, these practical scheduling problems are very complex, and almost is a combination of different typical scheduling problems.


Manufacturing scheduling Multiobjective evolutionary algorithm (MOEA) Hybrid evolutionary algorithm (HEA) Job shop scheduling (JSP) Flexible JSP (FJSP) Advanced planning and scheduling (APS) Automatic guided vehicle (AGV) 



This work is partly supported by the Japan Society of Promotion of Science (JSPS):Grant-in-Aid for Scientific Research (C) (No. 24510219.0001), National Science Council (NSC 101-2811-E-007-004, NSC 102-2811-E-007-005), the Fundamental Research Funds (Software+X) of Dalian University of Technology (No. DUT12JR05, No. DUT12JR12), and supported by New Teacher Fund of Ministry of Education of China (No. 20120041120053).


  1. Baker, k, & Scudder, G. (1990). Sequencing with earliness & tardiness penalties: A review. Operations Research, 38, 22–36.CrossRefGoogle Scholar
  2. Bidot, J., Vidal, T., Laborie, P., & Beck, J. C. (2009). A theoretic and practical framework for scheduling in a stochastic environment. Journal of Scheduling, 12(3), 315–344.CrossRefGoogle Scholar
  3. Cheng, R., & Gen, M. (1994). Evolution program for resource constrained project scheduling problem. In Proceedings of IEEE international conference of, evolutionary computation, pp. 736–741.Google Scholar
  4. Cheng, R., Gen, M., & Tsujimura, Y. (1996). A tutorial survey of job-shop scheduling problems using genetic algorithms, part I. Representation. Computers & Industrial Engineering, 30(4), 983–997.CrossRefGoogle Scholar
  5. Cheng, R., Gen, M., & Tsujimura, Y. (1999). A tutorial survey of job-shop scheduling problems using genetic algorithms, part II: Hybrid genetic search strategies. Computers & Industrial Engineering, 36(2), 343–364.CrossRefGoogle Scholar
  6. Choudhury, B. B., Biswal, B. B., Mishra, D., & Mahapatra, R. N. (2009). Appropriate evolutionary algorithm for scheduling in FMS. NaBIC World Congress on Nature & Biologically Inspired, Computing, pp. 1139–1144.Google Scholar
  7. Croce, F., Tadei, R., & Volta, G. (1995). A genetic algorithm for the job shop problem. Computer & Operations Research, 22, 15–24.CrossRefGoogle Scholar
  8. Dahal, K., Tan, K. C., & Cowling, P. I. (2007). Evolutionary scheduling. Berlin: Springer.CrossRefGoogle Scholar
  9. De Jong, K. (1994). Genetic algorithms: A 25 year perspective. Computational Intelligence: Imitating Life, pp. 125–134.Google Scholar
  10. Deb, K. (2001). Multiobjective optimization using evolutionary algorithms. Chichester, UK: Wiley.Google Scholar
  11. Dev, K. (1995). Optimization for engineering design: Algorithms and examples. New Delhi: Prentice-Hall.Google Scholar
  12. Dorndorf, W., & Pesch, E. (1995). Evolution based learning in a job shop scheduling environment. Computer & Operations Research, 22, 25–40.CrossRefGoogle Scholar
  13. Elyn, L. Solano-Charris, Jairo, R. Montoya-Torres, & Carlos, D. Paternina-Arboleda. (2011). Ant colony optimization algorithm for a Bi-criteria 2-stage hybrid flowshop scheduling problem. Journal of Intelligent Manufacturing, 22(5), 815–822.CrossRefGoogle Scholar
  14. Floudas, C. A., & Lin, X. (2004). Continuous-time versus discrete-time approaches for scheduling of chemical processes: a review. Computers & Chemical Engineering, 28, 2109. Google Scholar
  15. Floudas, C. A., & Lin, X. (2005). Mixed integer linear programming in process scheduling: Modeling, algorithms, and applications. Annals of Operations Research, 139, 131.CrossRefGoogle Scholar
  16. Fonseca, C., & Fleming, P. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1), 1–16.CrossRefGoogle Scholar
  17. Framinan, J. M., & Ruiz, R. (2010). Architecture of manufacturing scheduling systems: Literature review and an integrated proposal. European Journal of Operational Research, 205, 237–246.CrossRefGoogle Scholar
  18. Gao, J., Sun, L., & Gen, M. (2008). A hybrid genetic and variable neighborhood descent algorithm for flexible job shop scheduling problems. Computers & Operations Research, 35(9), 2892–2907.Google Scholar
  19. Garey, M. R., Johmson, D. S., & Sethi, R. (1976). The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research, 1, 117–129.CrossRefGoogle Scholar
  20. Geiger, M. J. (2011). Decision support for multi-objective flow shop scheduling by the Pareto iterated local search methodology. Computers & Industrial Engineering, 61, 805–812.CrossRefGoogle Scholar
  21. Gen, M., & Cheng, R. (1997). Genetic algorithms and engineering design. New York: Wiley.Google Scholar
  22. Gen, M., & Cheng, R. (2000). Genetic algorithms and engineering optimization. New York: Wiley.Google Scholar
  23. Gen, M., & Zhang, H. (2006). Effective designing chromosome for optimizing advanced planning and scheduling. Intelligent Engineering Systems Through Artificial Neural Networks, 16, 61–66.CrossRefGoogle Scholar
  24. Gen, M., Cheng, R., & Lin, L. (2008). Network models and optimization: Multiobjective genetic algorithm approach. Berlin: Springer.Google Scholar
  25. Gen, M., Lin, L., & Zhang, H. (2009). Evolutionary techniques for optimization problems in integrated manufacturing system: State-of-the-art survey. Computers & Industrial Engineering, 56(3), 779–808.CrossRefGoogle Scholar
  26. Gholami, M., & Zandieh, M. (2009). Integrating simulation and genetic algorithm to schedule a dynamic flexible job shop. Journal of Intelligent Manufacturing, 20(4), 481–498.CrossRefGoogle Scholar
  27. Guo, Y. W., Mileham, A. R., Owen, G. W., & Li, W. D. (2006). Operation sequencing optimization using a particle swarm optimization approach. Proceedings of the Institution of Mechanical Engineers, Part B: Journal of Engineering Manufacture, 220(12), 1945–1958.CrossRefGoogle Scholar
  28. Guo, Y. W., Li, W. D., Mileham, A. R., & Owen, G. W. (2009). Applications of particle swarm optimization in integrated process planning and scheduling. Robotics and Computer-Integrated Manufacturing, 25, 280–288.CrossRefGoogle Scholar
  29. Handa, H., Kawakami, H., & Katai, O. (2008). Recent advances in evolutionary computation. IEEJ Transactions on Electronics, Information & Systems, 128(3), 334–339.CrossRefGoogle Scholar
  30. Ho, S., Shu, L., & Chen, J. (2004). Intelligent evolutionary algorithms for large parameter optimization problems. IEEE Transactions on Evolutionary Computation, 8(6), 522–541.CrossRefGoogle Scholar
  31. Hwang, C., & Yoon, K. (1981). Multiple attribute decision making: Methods and applications. Berlin: Springer.CrossRefGoogle Scholar
  32. Ishibuchi, H., & Murata, T. (1998). A multiobjective genetic local search algorithm and its application to flowshop scheduling. IEEE Transactions on Systems, Man, & Cybernetics, 28(3), 392–403.CrossRefGoogle Scholar
  33. Kacem, I., Hammadi, S., & Borne, P. (2002a). Approach by localization and multiobjective evolutionary optimization for flexible job-shop scheduling problems. IEEE Transactions on Systems, Man, and Cybernetics-Part C, 32(1), 1–13.CrossRefGoogle Scholar
  34. Kacem, I., Hammadi, S., & Borne, P. (2002b). Pareto-optimality approach for flexible job-shop scheduling problems: Hybridization of evolutionary algorithms and fuzzy logic. Mathematics & Computers in Simulation, 60, 245–276.CrossRefGoogle Scholar
  35. Karimi-Nasab, M., & Aryanezhad, M. B. (2011). A multi-objective production smoothing model with compressible operating times. Applied Mathematical Modeling, 35, 3596–3610.CrossRefGoogle Scholar
  36. Kim, Y., Park, K., & Ko, J. (2003). A symbiotic evolutionary algorithm for the integration of process planning and job shop scheduling. Computers and Operations Research, 30, 1151–1171.Google Scholar
  37. Kim, K., Yamazaki, G., Lin, L., & Gen, M. (2004). Network-based hybrid genetic algorithm to the scheduling in FMS environments. Journal of Artificial Life and Robotics, 8(1), 67–76.Google Scholar
  38. Li, W., & McMahon, C. (2007). A simulated annealing-based optimization approach for integrated process planning and scheduling. International Journal of Computer Integrated Manufacturing, 20(1), 80–95.CrossRefGoogle Scholar
  39. Li, L., & Huo, J. (2009). Multi-objective flexible job-shop scheduling problem in steel tubes production. Systems Engineering-Theory & Practice, 29(8), 117–126.CrossRefGoogle Scholar
  40. Li, X., Zhang, C., Gao, L., Li, W., & Shao, X. (2010). An agent-based approach for integrated process planning and scheduling. Expert Systems with Applications, 37, 1256–1264.CrossRefGoogle Scholar
  41. Li, X., Gao, L., & Li, W. (2012). Application of game theory based hybrid algorithm for multi-objective integrated process planning and scheduling. Expert Systems with Applications, 39, 288–297.CrossRefGoogle Scholar
  42. Liang, Y., Lin, L., Gen, M., & Ohno, K. (2012). A hybrid evolutionary algorithm for FMS optimization with AGV dispatching. In Proceedings of the 42nd international conference on computers and industrial engineering, pp. 296.1–296.14.Google Scholar
  43. Lin, L., Shinn, S. W., Gen, M., & Hwang, H. (2006). Network model and effective evolutionary approach for AGV dispatching in manufacturing system. Journal of Intelligent Manufacturing, 17(4), 465–477.CrossRefGoogle Scholar
  44. Lin, L., Gen, M., Liang, Y., & Ohno, K. (2012). A hybrid EA for reactive flexible job-shop scheduling. Complex Adaptive Systems., 12, 110–115.Google Scholar
  45. Lopez, O., & Ramirez, M. (2005). A STEP-based manufacturing information system to share flexible manufacturing resources data. Journal of Intelligent Manufacturing, 16(3), 287–301.CrossRefGoogle Scholar
  46. Meeran, S., & Morshed, M. S. (2012). A hybrid genetic tabu search algorithm for solving job shop scheduling problems: A case study. Journal of Intelligent Manufacturing, 23(4), 1063–1078.CrossRefGoogle Scholar
  47. Michalewicz, Z. (1994). Genetic algorithm + data structures = evolution programs. Berlin: Springer.CrossRefGoogle Scholar
  48. Najid, N. M., Dauzere-Peres, S., & Zaidat, A. (2002). A modified simulated annealing method for flexible job shop scheduling problem. IEEE International Conference on Systems, Man and Cybernetics, 5, 6–9.Google Scholar
  49. Naso, D., & Turchiano, B. (2005). Multicriteria meta-heuristics for AGV dispatching control based on computational intelligence. IEEE Transactions on Systems, Man and Cybernetics-Part B, 35(2), 208–226.Google Scholar
  50. Norman, B., & Bean, J. (1995). Random keys genetic algorithm for job-shop scheduling: Unabridged version. Technical report, University of Michigan.Google Scholar
  51. Nowicki, E., & Smutnicki, C. (2005). An advanced tabu search algorithm for the job-shop problem. Journal of Scheduling, 8(2), 145–159.CrossRefGoogle Scholar
  52. Okamoto, A., Gen, M., & Sugawara, M. (2005). Cooperation of scheduling agent and transportation agent in APS system. In Proceedings of the JSLS Kyushu division conference, pp. 1–11 (in Japanese).Google Scholar
  53. Pareto, V. (1906). Manuale di Economica Polittica. Milan, Italy: Societa Editrice Libraia.Google Scholar
  54. Pinedo, M. (2002). Scheduling theory, algorithms and systems. Upper Saddle River, NJ: Prentice-Hall.Google Scholar
  55. Schaffer, J. D. (1985). Multiple objective optimization with vector evaluated genetic algorithms. In Proceedings of 1st international conference on GAs, pp. 93–100.Google Scholar
  56. Shao, X., Li, X., & Gao, L. (2009). Integration of process planning and scheduling: A modified genetic algorithm-based approach. Computers & Operations Research, 36, 2082–2096.CrossRefGoogle Scholar
  57. Song, S.-G., Li, A.-p., & Xu, L.-Y. (2008). AGV dispatching strategy based on theory of constraints, automation and mechatronics. In Proceedings of IEEE conference on, robotics, pp. 922–925.Google Scholar
  58. Tavakkoli-Moghaddam, R., Jolai, F., Vaziri, F., Ahmed, P. K., & Azaron, A. (2005). A hybrid method for solving stochastic job shop scheduling problems. Applied Mathematics and Computation, 170(1), 185–206.Google Scholar
  59. Verderame, P. M., & Christodoulos, A. F. (2008). Integrated Operational Planning and Medium-Term Scheduling for Large-Scale Industrial Batch Plants. Industrial & Engineering Chemistry Research., 47(14), 4845–4860.CrossRefGoogle Scholar
  60. Vis, I. F. A. (2006). Survey of research in the design and control of automated guided vehicle systems. European Journal of Operational Research, 170(3), 677–709.CrossRefGoogle Scholar
  61. Voratas, K., & Siriwan, S. (2011). A two-stage genetic algorithm for multi-objective job shop scheduling problems. Journal of Intelligent Manufacturing, 22(3), 355–365.CrossRefGoogle Scholar
  62. Wang, S. J., Xi, L. F., & Zhou, B. H. (2008). FBS-enhanced agent-based dynamic scheduling in FMS. Engineering Applications of Artificial Intelligence, 21(4), 644–657.Google Scholar
  63. Wu, Z., & Weng, M. X. (2005). Multiagent scheduling method with earliness and tardiness objectives in flexible job shops. IEEE Transactions on System, Man, and Cybernetics-Part B, 35(2), 293–301.CrossRefGoogle Scholar
  64. Xia, W., & Wu, Z. (2005). An effective hybrid optimization approach for muti-objective flexible job-shop scheduling problem. Computers & Industrial Engineering, 48, 409–425.CrossRefGoogle Scholar
  65. Xiang, W., & Lee, H. P. (2008). Ant colony intelligence in multi-agent dynamic manufacturing scheduling. Engineering Applications of Artificial Intelligence, 21, 73–85.CrossRefGoogle Scholar
  66. Yamada, T., & Nakano, R. (1992). A genetic algorithm applicable to large-scale job-shop problems. Parallel Problem Solving from Nature: PPSN, II, 281–290.Google Scholar
  67. Yang, J. (2001). GA-based discrete dynamic programming approach for scheduling in FMS environment. IEEE Transactions on Systems, Man, Cybernetics-Part B, 31, 824–835.Google Scholar
  68. Zandieh, M., & Karimi, N. (2011). An adaptive multi-population genetic algorithm to solve the multi-objective group scheduling problem in hybrid flexible flowshop with sequence-dependent setup times. Journal of Intelligent Manufacturing, 22(6), 979–989.CrossRefGoogle Scholar
  69. Zhang, H., & Gen, M. (2006). Effective genetic approach for optimizing advanced planning and scheduling in flexible manufacturing system. In Proceedings of GECCO, pp. 1841–1848.Google Scholar
  70. Zhang, H., & Gen, M. (2005). Multistage-based genetic algorithm for flexible job-shop scheduling problem. Journal of Complexity International, 11, 223–232.Google Scholar
  71. Zhang, W., Gen, M., & Jo, J.-B. (2012a). Hybrid sampling strategy-based multiobjective evolutionary algorithm for process planning and scheduling problem. In Proceedings of international symposium on semiconductor manufacturing intelligence.Google Scholar
  72. Zhang, W., Lin, L., Gen, M., & Chien, C. F. (2012b). Hybrid sampling strategy-based multiobjective evolutionary algorithm. Complex Adaptive Systems, 12, 96–101.Google Scholar
  73. Zhao, Z.-X., Zhang, G.-S., & Bing, Z.-G. (2011). Scheduling optimization for FMS based on Petri net modeling and GA. In: Proceedings of IEEE international conference on automation and logistics, pp. 422–427.Google Scholar
  74. Zitzler, E., & Thiele, L. (2001). SPEA2: Improving the strength Pareto evolutionary algorithm, Technical report 103, Computer Engineering and Communication Networks Lab (TIK).Google Scholar
  75. Zitzler, E., & Thiele, L. (1999). Multiobjective evolutionary algorithms: A comparative case study and the strength Pareto approach. IEEE Transactions on Evolutionary Computation, 3(4), 257–271.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.Fuzzy Logic Systems InstituteIizuka CityJapan
  2. 2.National Tsing Hua UniversityHsinchu CityTaiwan
  3. 3.Dalian University of TechnologyDalianChina

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