Bayesian statistical inference-based estimation of distribution algorithm for the re-entrant job-shop scheduling problem with sequence-dependent setup times

Abstract

In this paper, a bayesian statistical inference-based estimation of distribution algorithm (BEDA) is proposed for the re-entrant job-shop scheduling problem with sequence-dependent setup times (RJSSPST) to minimize the maximum completion time (i.e., makespan), which is a typical NP hard combinatorial problem with strong engineering background. Bayesian statistical inference (BSI) is utilized to extract sub-sequence information from high quality individuals of the current population and determine the parameters of BEDA's probabilistic model (BEDA-PM). In the proposed BEDA, BEDA-PM is used to generate new population and guide the search to find promising sequences or regions in the solution space. Simulation experiments and comparisons demonstrate the effectiveness of the proposed BEDA. © 2014 Springer International Publishing Switzerland.