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Multi-objective Job Shop Rescheduling with Estimation of Distribution Algorithm

  • Xinchang HaoEmail author
  • Lu Sun
  • Mitsuo Gen
Conference paper
Part of the Lecture Notes on Multidisciplinary Industrial Engineering book series (LNMUINEN)

Abstract

To solve the moJSRP model, with the framework of proposed MoEDA, the probability model of the operation sequence is estimated firstly. For sampling the processing time of each operation with the Monte Carlo methods, allocation method is used to decide the operation sequence, and then the expected makespan and total tardiness of each sampling are evaluated. Subsequently, updating mechanism of the probability models is proposed according to the best solutions to obtain. Finally, for comparing with some existing algorithms by numerical experiments on the benchmark problems, we demonstrate the proposed effective estimation of distribution algorithm can obtain an acceptable solution in the aspects of schedule quality and computational efficiency.

Keywords

Job shop rescheduling Multi-objective optimization Estimation of distribution algorithms 

Notes

Acknowledgement

This work is partly supported by the National Natural Science Foundation of China under Grant 61572100, and in part by the Grant-in-Aid for Scientific Research (C) of Japan Society of Promotion of Science (JSPS) No. 15K00357.

References

  1. 1.
    Cauty R (2000) Genetic algorithms and engineering optimization. Wiley, New YorkGoogle Scholar
  2. 2.
    Deb K, Pratap A, Agarwal S et al (2002) A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput 6(2):182–197CrossRefGoogle Scholar
  3. 3.
    Gen M, Cheng R, Lin L (2008) Network models and optimization: multiobjective genetic algorithm approach. Springer, LondonzbMATHGoogle Scholar
  4. 4.
    Giffler B, Thompson GL (1960) Algorithms for solving production-scheduling problems. Oper Res 8(4):487–503MathSciNetCrossRefzbMATHGoogle Scholar
  5. 5.
    Kempf K (1994) Intelligently scheduling semiconductor wafer fabrication. In: Intelligent Scheduling, pp 517–544Google Scholar
  6. 6.
    Michiels W, Aarts E, Korst J (2007) Theoretical aspects of local search. Springer, BerlinzbMATHGoogle Scholar
  7. 7.
    Sha DY, Liu CH (2005) Using data mining for due date assignment in a dynamic job shop environment. Int J Adv Manufact Technol 25(11):1164–1174CrossRefGoogle Scholar
  8. 8.
    Vieira GE, Herrmann JW, Lin E (2003) Rescheduling manufacturing systems: a framework of strategies, policies, and methods. J Sched 6(1):39–62MathSciNetCrossRefzbMATHGoogle Scholar
  9. 9.
    Vinod V, Sridharan R (2008) Scheduling a dynamic job shop production system with sequence-dependent setups: an experimental study. Rob Comput-Integr Manufact 24(3):435–449CrossRefGoogle Scholar
  10. 10.
    Zitzler E, Laumanns M, Thiele L (2001) SPEA 2: improving the strength pareto evolutionary algorithm. In: Evolutionary methods for design, optimization and control. CIMNE, BarcelonaGoogle Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  1. 1.Waseda UniversityTokyoJapan
  2. 2.Dalian University of TechnologyDalianPeople’s Republic of China
  3. 3.Fuzzy Logic Systems InstituteTokyo University of ScienceTokyoJapan

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