Solving a fuzzy flowshop problem with a self-adaptive DE algorithm

This paper studied a flowshop scheduling problem with fuzzy due dates where the objective of maximizing the sum of customer satisfaction for the completion times of jobs is concerned. A self-adaptive differential evolution (SDE) algorithm has been employed to solve the fuzzy flowshop problem (FFP). The mutation scaling factor F and crossover probability CR are self-adaptive during the evolutionary process which can balance the exploration and the exploitation effectively. In order to make the SDE algorithm more effective and efficient, a local search strategy based on insert operation is introduced to exploit more latitude of search space to anchor the global optimum. The experimental results on the problem instance from literature demonstrate the good performance of the proposed SDE, thereby proving its worth as an attractive alternative for flowshop problems in uncertain environments.