Solving the Multi-objective Flexible Job-Shop Scheduling Problem with Alternative Recipes for a Chemical Production Process

  • Piotr DziurzanskiEmail author
  • Shuai Zhao
  • Jerry Swan
  • Leandro Soares Indrusiak
  • Sebastian Scholze
  • Karl Krone
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11454)


This paper considers a new variant of a multi-objective flexible job-shop scheduling problem, featuring multisubset selection of manufactured recipes. We propose a novel associated chromosome encoding and customise the classic MOEA/D multi-objective genetic algorithm with new genetic operators. The applicability of the proposed approach is evaluated experimentally and showed to outperform typical multi-objective genetic algorithms. The problem variant is motivated by real-world manufacturing in a chemical plant and is applicable to other plants that manufacture goods using alternative recipes.


Multi-objective job-shop scheduling Process manufacturing optimisation Multi-objective genetic algorithms 



The authors acknowledge the support of the EU H2020 SAFIRE project (Ref. 723634).


  1. 1.
    Ali, C.I., Ali, K.A.: A research survey: review of flexible job shop scheduling techniques. Int. Trans. Oper. Res. 23(3), 551–591 (2015)MathSciNetzbMATHGoogle Scholar
  2. 2.
    Ham, A.: Flexible job shop scheduling problem for parallel batch processing machine with compatible job families. Appl. Math. Model. 45, 551–562 (2017)MathSciNetCrossRefGoogle Scholar
  3. 3.
    Wang, K., Choi, S.: A holonic approach to flexible flow shop scheduling under stochastic processing times. Comput. Oper. Res. 43, 157–168 (2014)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Gen, M., Lin, L.: Multiobjective evolutionary algorithm for manufacturing scheduling problems: state-of-the-art survey. J. Intell. Manufact. 25(5), 849–866 (2014)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Ozguven, C., Ozbakir, L., Yavuz, Y.: Mathematical models for job-shop scheduling problems with routing and process plan flexibility. Appl. Math. Model. 34(6), 1539–1548 (2010)MathSciNetCrossRefGoogle Scholar
  6. 6.
    Thomalla, C.: Job shop scheduling with alternative process plans. Int. J. Prod. Econ. 74(1–3), 125–134 (2001)CrossRefGoogle Scholar
  7. 7.
    Ishibuchi, H., Murata, T.: A multi-objective genetic local search algorithm and its application to flowshop scheduling. Trans. Sys. Man Cyber Part C 28(3), 392–403 (1998)CrossRefGoogle Scholar
  8. 8.
    Zhang, Q., Li, H.: MOEA/D: a multiobjective evolutionary algorithm based on decomposition. IEEE Trans. Evol. Comput. 11(6), 712–731 (2007)CrossRefGoogle Scholar
  9. 9.
    Li, L., Huo, J.Z.: Multi-objective flexible job-shop scheduling problem in steel tubes production. Syst. Eng. Theor. Pract. 29(8), 117–126 (2009)CrossRefGoogle Scholar
  10. 10.
    Méndez, C.A., et al.: State-of-the-art review of optimization methods for short-term scheduling of batch processes. Comput. Chem. Eng. 30(6–7), 913–946 (2006)CrossRefGoogle Scholar
  11. 11.
    Huynh, N.T., Chien, C.F.: A hybrid multi-subpopulation genetic algorithm for textile batch dyeing scheduling and an empirical study. Comput. Ind. Eng. 125, 615–627 (2018)CrossRefGoogle Scholar
  12. 12.
    Amjad, K.M., et al.: Recent research trends in genetic algorithm based flexible job shop scheduling problems. Math. Probl. Eng. 1–32 (2018)Google Scholar
  13. 13.
    Michalewicz, Z.: Genetic Algorithms + Data Structures = Evolution Programs, 3rd edn. Springer, Heidelberg (1996). Scholar
  14. 14.
    Indrusiak, L.S., Dziurzanski, P.: An interval algebra for multiprocessor resource allocation. In: International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS), pp. 165–172, July 2015Google Scholar
  15. 15.
    Miettinen, K.: Nonlinear Multiobjective Optimization, vol. 12. Springer, New York (2012).
  16. 16.
    Deb, K., et al.: A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans. Evol. Comput. 6(2), 182–197 (2002)CrossRefGoogle Scholar
  17. 17.
    Li, M., Yang, S., Liu, X.: Diversity comparison of pareto front approximations in many-objective optimization. IEEE Trans. Cybern. 44(12), 2568–2584 (2014)CrossRefGoogle Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Piotr Dziurzanski
    • 1
    Email author
  • Shuai Zhao
    • 1
  • Jerry Swan
    • 1
  • Leandro Soares Indrusiak
    • 1
  • Sebastian Scholze
    • 2
  • Karl Krone
    • 3
  1. 1.Department of Computer ScienceUniversity of YorkYorkUK
  2. 2.Institut fur Angewandte Systemtechnik Bremen GmbHBremenGermany
  3. 3.OAS AGBremenGermany

Personalised recommendations