Abstract
This paper presents a Markov network based multi-objective estimation distribution of algorithm (MMEDA) to solve the resource constrained scheduling problem (RCSP), which hybrid a constraint handling by Markov network based EDA and multi-objective optimization by enforced EDA. Firstly, in order to increase the searching performance while keeping the diversity of Pareto solutions, two kinds of fitness assignment functions are integrated within a novel paradigm. Secondly, Markov network, as an undirected graph model, is adopted to model interrelation between variables with constraints. Thirdly, an enforced EDA with mutation operation is proposed to handle the scheduling. Fourthly, a problem-specific local search for RCSP is applied to improve searching performance. Experiments are conducted on multi-mode resource constrained scheduling problem (MRCPSP) which is an extended RCSP including multi-mode resource constraints. The results of the proposed method highly outperformed conventional meta-heuristic based scheduling methods.
