Solution Representation for Job Shop Scheduling Problems in Ant Colony Optimisation

  • James Montgomery
  • Carole Fayad
  • Sanja Petrovic
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4150)


Production scheduling problems such as the job shop consist of a collection of operations (grouped into jobs) that must be scheduled for processing on different machines. Typical ant colony optimisation applications for these problems generate solutions by constructing a permutation of the operations, from which a deterministic algorithm can generate the actual schedule. This paper considers an alternative approach in which each machine is assigned a dispatching rule, which heuristically determines the order of operations on that machine. This representation creates a substantially smaller search space that likely contains good solutions. The performance of both approaches is compared on a real-world job shop scheduling problem in which processing times and job due dates are modelled with fuzzy sets. Results indicate that the new approach produces better solutions more quickly than the traditional approach.


Solution Representation Production Schedule Problem Fuzzy Processing Time Satisfaction Grade Average Tardiness 
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.
    Blum, C., Sampels, M.: An ant colony optimization algorithm for shop scheduling problems. J. Math. Model. Algorithms 3, 285–308 (2004)zbMATHCrossRefMathSciNetGoogle Scholar
  2. 2.
    Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M.: Ant system for job-shop scheduling. JORBEL 34, 39–53 (1994)zbMATHGoogle Scholar
  3. 3.
    Dorndorf, U., Pesch, E.: Evolution based learning in a job shop scheduling environment. Comput. Oper. Res. 22, 25–44 (1995)zbMATHCrossRefGoogle Scholar
  4. 4.
    Montgomery, J., Fayad, C., Petrovic, S.: Solution representation for job shop scheduling problems in ant colony optimisation. Technical Report SUTICT-TR2006.05, Faculty of Information & Communication Technologies, Swinburne University of Technology, Melbourne, Australia (2006)Google Scholar
  5. 5.
    Montgomery, J., Randall, M., Hendtlass, T.: Structural advantages for ant colony optimisation inherent in permutation scheduling problems. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS, vol. 3533, pp. 218–228. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  6. 6.
    Fayad, C., Petrovic, S.: A fuzzy genetic algorithm for real-world job shop scheduling. In: Ali, M., Esposito, F. (eds.) IEA/AIE 2005. LNCS (LNAI), vol. 3533, pp. 524–533. Springer, Heidelberg (2005)CrossRefGoogle Scholar
  7. 7.
    Klir, G., Folger, T.: Fuzzy Sets, Uncertainty and Information. Prentice Hall, New Jersey (1988)zbMATHGoogle Scholar
  8. 8.
    Itoh, T., Ishii, H.: Fuzzy due-date scheduling problem with fuzzy processing time. Int. Trans. Oper. Res. 6, 639–647 (1999)CrossRefGoogle Scholar
  9. 9.
    Sakawa, M., Kubota, R.: Fuzzy programming for multiobjective job shop scheduling with fuzzy processing time and fuzzy duedate through genetic algorithms. Eur. J. Oper. Res. 120, 393–407 (2000)zbMATHCrossRefMathSciNetGoogle Scholar
  10. 10.
    Stützle, T., Hoos, H.: \(\mathcal{MAX-MIN}\) ant system. Future Gen. Comp. Sys. 16, 889–914 (2000)CrossRefGoogle Scholar
  11. 11.
    Montgomery, E.J.: Solution Biases and Pheromone Representation Selection in Ant Colony Optimisation. Ph.D thesis, Bond University (2005)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • James Montgomery
    • 1
  • Carole Fayad
    • 2
  • Sanja Petrovic
    • 2
  1. 1.Faculty of Information & Communication TechnologiesSwinburne University of TechnologyMelbourneAustralia
  2. 2.School of Computer Science & ITUniversity of NottinghamNottinghamUnited Kingdom

Personalised recommendations