Abstract
Most of the work about flowshop scheduling problems assume that the problem data are known exactly at the advance or the common approach to the treatment of the uncertainties in the problem is use of probabilistic models. However, the evaluation and optimization of the probabilistic model is computationally expensive and rational only when the descriptions of the uncertain parameters are available from the historical data. In addition, a certain amount of delay on due dates may be tolerated in most real-world situations although they are handled as crisp dates in most of the previous papers. In this paper we deal with a flowshop scheduling problem with fuzzy processing times and flexible due dates. Schedules are generated by a proposed algorithm in the context of ant colony optimization metaheuristic approach.
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Kilic, S. (2007). Scheduling a Fuzzy Flowshop Problem with Flexible Due Dates Using Ant Colony Optimization. In: Giacobini, M. (eds) Applications of Evolutionary Computing. EvoWorkshops 2007. Lecture Notes in Computer Science, vol 4448. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-71805-5_80
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DOI: https://doi.org/10.1007/978-3-540-71805-5_80
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