An Evolutionary Approach to Practical Constraints in Scheduling: A Case-Study of the Wine Bottling Problem

  • Arvind Mohais
  • Sven Schellenberg
  • Maksud Ibrahimov
  • Neal Wagner
  • Zbigniew Michalewicz


Practical constraints associated with real-world problems are a key differentiator with respect to more artificially formulated problems. They create challenging variations on what might otherwise be considered as straightforward optimization problems from an evolutionary computation perspective. Through solving various commercial and industrial problems using evolutionary algorithms, we have gathered experience in dealing with practical dynamic constraints. Here, we present proven methods for dealing with these issues for scheduling problems. For use in real-world situations, an evolutionary algorithm must be designed to drive a software application that needs to be robust enough to deal with practical constraints in order to meet the demands and expectations of everyday use by domain specialists who are not necessarily optimization experts. In such situations, addressing these issues becomes critical to success. We show how these challenges can be dealt with by making adjustments to genotypic representation, phenotypic decoding, or the evaluation function itself. The ideas presented in this chapter are exemplified by the means of a case study of a real-world commercial problem, namely that of bottling wine in a mass-production environment. The methods described have the benefit of having been proven by a full-fledged implementation into a software application that undergoes continual and vigorous use in a live environment in which time-varying constraints, arising in multiple different combinations, are a routine occurrence.


Schedule Problem White Wine Time Block Practical Constraint Machine Breakdown 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Davis, L.: Job shop scheduling with genetic algorithms. In: Proceedings of the 1st International Conference on Genetic Algorithms, pp. 136–140. L. Erlbaum Associates Inc., Mahwah (1985)Google Scholar
  2. 2.
    Van Laarhoven, P.J.M.: Job shop scheduling by simulated annealing. Operations Research 40(1), 113–125 (1992)MathSciNetzbMATHCrossRefGoogle Scholar
  3. 3.
    Wang, L., Zheng, D.-Z.: A modified genetic algorithm for job-shop scheduling. International Journal of Advanced Manufacturing Technology 20(1), 72–76 (2002)CrossRefGoogle Scholar
  4. 4.
    Garey, M.R., Johnson, D.S., Sethi, R.: The complexity of flowshop and jobshop scheduling. Mathematics of Operations Research 1(2), 117–129 (1976)MathSciNetzbMATHCrossRefGoogle Scholar
  5. 5.
    Branke, J.: Memory enhanced evolutionary algorithms for changing optimization problems. In: Proc. of the 1999 Congress on Evolutionary Computation, CEC 1999, pp. 1875–1882 (1999)Google Scholar
  6. 6.
    Branke, J.: Evolutionary approaches to dynamic optimization problems - a survey. In: GECCO Workshop on Evolutionary Algorithms for Dynamic Optimization Problems, pp. 134–137 (1999)Google Scholar
  7. 7.
    Morrison, R.W., De Jong, K.A.: A test problem generator for non-stationary environments. In: Proceedings of the 1999 Congress on Evolutionary Computation, CEC 1999, pp. 2047–2053 (1999)Google Scholar
  8. 8.
    Carlisle, A., Dozier, G.: Adapting particle swarm optimization to dynamic environments. In: International Conference on Artificial Intelligence, Las Vegas, NV, USA, pp. 429–434 (2000)Google Scholar
  9. 9.
    Yu, X., Tang, K., Yao, X.: An immigrants scheme based on environmental information for genetic algorithms in changing environments. In: Proceedings of the 2008 Congress on Evolutionary Computation, CEC 2008, pp. 1141–1147 (2008)Google Scholar
  10. 10.
    Pettit, E., Swigger, K.M.: An analysis of genetic-based pattern tracking and cognitive-based component tracking models of adaptation. In: Proceedings of the National Conference on Artificial Intelligence, pp. 327–332. AAAI Press, Menlo Park (1983)Google Scholar
  11. 11.
    Krishnakumar, K.: Micro-genetic algorithms for stationary and non-stationary function optimization. In: Proc. of the SPIE, Intelligent Control and Adaptive Systems, pp. 289–296 (1989)Google Scholar
  12. 12.
    John, J.: Grefenstette. Genetic algorithms for changing environments. In: Parallel Problem Solving from Nature, vol. 2, pp. 137–144. Elsevier, Amsterdam (1992)Google Scholar
  13. 13.
    Branke, J.: The moving peaks benchmark,
  14. 14.
    Johnston, M.D., Adorf, H.-M.: Scheduling with neural networks – the case of the hubble space telescope. Computers & Operations Research 19(3-4), 209–240 (1992)zbMATHCrossRefGoogle Scholar
  15. 15.
    Chryssolouris, G., Subramaniam, V.: Dynamic scheduling of manufacturing job shops using genetic algorithms. Journal of Intelligent Manufacturing 12(3), 281–293 (2001)CrossRefGoogle Scholar
  16. 16.
    Madureira, A.M., Ramos, C., Silva, S.C.: Madureira, Carlos Ramos, and Slvio C. Silva. Using genetic algorithms for dynamic scheduling. In: 14th Annual Production and Operations Management Society Conference, POMS 2003 (2003)Google Scholar
  17. 17.
    Emperador, J.M., González, B., Winter, G., Galván, B.: Minimum-cost planning of the multimodal transport of pipes with evolutionary computation. Int. J. Simul. Multidisci. Des. Optim. 3(3), 401–405 (2009)CrossRefGoogle Scholar
  18. 18.
    Jain, A.K., Elmaraghy, H.A.: Production scheduling/rescheduling in flexible manufacturing. International Journal of Production Research 35(1), 281–309 (1997)zbMATHCrossRefGoogle Scholar
  19. 19.
    Petrovic, D., Alejandra, D.: A fuzzy logic based production scheduling/rescheduling in the presence of uncertain disruptions. Fuzzy sets and systems 157(16), 2273–2285 (2006)MathSciNetzbMATHCrossRefGoogle Scholar
  20. 20.
    Kutanoglu, E., Sabuncuoglu, I.: Routing-based reactive scheduling policies for machine failures in dynamic job shops. International Journal of Production Research 39(14), 3141–3158 (2001)zbMATHCrossRefGoogle Scholar
  21. 21.
    Holthaus, O.: Scheduling in job shops with machine breakdowns: an experimental study. Computers & Industrial Engineering 36(1), 137–162 (1999)CrossRefGoogle Scholar
  22. 22.
    Cheng, R., Gen, M., Tsujimura, Y.: A tutorial survey of job-shop scheduling problems using genetic algorithms—i: representation. Computers & Industrial Engineering 30(4), 983–997 (1996)CrossRefGoogle Scholar
  23. 23.
    Yamada, T., Reeves, C.R.: Solving the c sum permutation flowshop scheduling problem by genetic local search. In: Proceedings of the 1998 Congress on Evolutionary Computation, CEC 1998, pp. 230–234 (1998)Google Scholar
  24. 24.
    Nowicki, E., Smutnicki, C.: A fast tabu search algorithm for the permutation flow-shop problem. European Journal of Operational Research 91(1), 160–175 (1996)zbMATHCrossRefGoogle Scholar
  25. 25.
    Marchiori, E., Steenbeek, A.: An evolutionary algorithm for large scale set covering problems with application to airline crew scheduling. In: Oates, M.J., Lanzi, P.L., Li, Y., Cagnoni, S., Corne, D.W., Fogarty, T.C., Poli, R., Smith, G.D. (eds.) EvoIASP 2000, EvoWorkshops 2000, EvoFlight 2000, EvoSCONDI 2000, EvoSTIM 2000, EvoTEL 2000, and EvoROB/EvoRobot 2000. LNCS, vol. 1803, pp. 367–381. Springer, Heidelberg (2000)CrossRefGoogle Scholar
  26. 26.
    Ponnambalam, S.G., Mohan Reddy, M.: A ga-sa multiobjective hybrid search algorithm for integrating lot sizing and sequencing in flow-line scheduling. International Journal of Advanced Manufacturing Technology 21(2), 126–137 (2003)Google Scholar
  27. 27.
    Ray, T., Sarker, R.A.: Optimum oil production planning using an evolutionary approach. In: Evolutionary Scheduling, pp. 273–292. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  28. 28.
    Burke, E.K., Smith, A.J.: A memetic algorithm to schedule planned maintenance for the national grid. Journal of Experimental Algorithmics 4, 1 (1999)CrossRefGoogle Scholar
  29. 29.
    Martinelli, F.: Stochastic comparison algorithm for discrete optimization with estimation of time-varying objective functions. Journal of Optimization Theory and Applications 103(1), 137–159 (1999)MathSciNetzbMATHCrossRefGoogle Scholar
  30. 30.
    Tinós, R., Yang, S.: Genetic algorithms with self-organizing behaviour in dynamic environments. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 105–127. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  31. 31.
    Yang, S.: Explicit memory schemes for evolutionary algorithms in dynamic environments. In: Evolutionary Computation in Dynamic and Uncertain Environments, pp. 3–28. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  32. 32.
    Schönemann, L.: Evolution strategies in dynamic environments. Evolutionary Computation in Dynamic and Uncertain Environments, pp. 51–77. Springer, Heidelberg (2007)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Arvind Mohais
    • 1
  • Sven Schellenberg
    • 2
  • Maksud Ibrahimov
    • 3
  • Neal Wagner
    • 1
  • Zbigniew Michalewicz
    • 3
    • 4
    • 5
  1. 1.SolveIT Software Pty. Ltd.Australia
  2. 2.SolveIT Software Pty. Ltd.DocklandsAustralia
  3. 3.School of Computer ScienceUniversity of AdelaideAustralia
  4. 4.Institute of Computer SciencePolish Academy of SciencesWarsawPoland
  5. 5.Polish-Japanese Institute of Information TechnologyWarsawPoland

Personalised recommendations