Maximum Entropy Estimation of Distribution Algorithm for JSSP under Uncertain Information Based on Rough Programming

To solve the problems of job shop scheduling under uncertain information, the paper builds a rough constrained model which overcomes the defects of traditional methods which need pre-set authorized characteristics or amount described attributes, and proposes a new maximum entropy estimation of distribution algorithm to solve these complex problems. The simulation tests of Muth and Thompson´s benchmark problems prove the effectiveness of the algorithm in the job shop scheduling problem under uncertain information.