Effective Estimation of Distribution Algorithm for Stochastic Job Shop Scheduling Problem

This paper propose an effective estimation of distribution algorithm (EDA), which solves the stochastic job-shop scheduling problem (S-JSP) with the uncertainty of processing time, to minimize the expected average makespan within a reasonable amount of calculation time. With the framework of proposed EDA, the probability model of operation sequence is estimated firstly. For sampling the processing time of each operation with the Monte Carlo methods, we use allocation method to decide the operation sequence then the expected makespan of each sampling is evaluated. Subsequently, updating mechanism of the probability models is proposed with the best solutions to obtain. Finally, for comparing with some existing algorithms by numerical experiments on the benchmark problems, we demonstrate the proposed effective estimation of distribution algorithm can obtain acceptable solution in the aspects of schedule quality and computational efficiency.