Abstract
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.
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References
Blum, C., Sampels, M.: An ant colony optimization algorithm for shop scheduling problems. J. Math. Model. Algorithms 3, 285–308 (2004)
Colorni, A., Dorigo, M., Maniezzo, V., Trubian, M.: Ant system for job-shop scheduling. JORBEL 34, 39–53 (1994)
Dorndorf, U., Pesch, E.: Evolution based learning in a job shop scheduling environment. Comput. Oper. Res. 22, 25–44 (1995)
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)
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)
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)
Klir, G., Folger, T.: Fuzzy Sets, Uncertainty and Information. Prentice Hall, New Jersey (1988)
Itoh, T., Ishii, H.: Fuzzy due-date scheduling problem with fuzzy processing time. Int. Trans. Oper. Res. 6, 639–647 (1999)
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)
Stützle, T., Hoos, H.: \(\mathcal{MAX-MIN}\) ant system. Future Gen. Comp. Sys. 16, 889–914 (2000)
Montgomery, E.J.: Solution Biases and Pheromone Representation Selection in Ant Colony Optimisation. Ph.D thesis, Bond University (2005)
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Montgomery, J., Fayad, C., Petrovic, S. (2006). Solution Representation for Job Shop Scheduling Problems in Ant Colony Optimisation. In: Dorigo, M., Gambardella, L.M., Birattari, M., Martinoli, A., Poli, R., Stützle, T. (eds) Ant Colony Optimization and Swarm Intelligence. ANTS 2006. Lecture Notes in Computer Science, vol 4150. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11839088_49
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DOI: https://doi.org/10.1007/11839088_49
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