Markov Random Fields based Estimation of Distribution Algorithm and Application of Sharable Resource Constrained Scheduling Problem

During the past several years, a large number of studies have been conducted in the area of sharable resource constrained scheduling problems. Intelligent manufacturing planning and scheduling based on meta-heuristic methods, such as the simulated annealing and particle swarm optimization, have become common tools for finding satisfactory solutions within reasonable computational times in real settings. However, only a few studies have analyzed the effects of interdependent relations during group decision-making activities. This paper proposes a Markov network based estimation of distribution algorithm (MNEDA) to overcome these challenges. We present an empirical validation of MNEDA on the semiconductor final test scheduling problem as an example of resource constraint scheduling problems.