Ranking and defuzzification methods based on area compensation
Fuzzy Sets and Systems
Future Generation Computer Systems
Ant Colony Optimization
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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.