Robust scheduling for resource constraint scheduling problem by two-stage GA and MMEDA

Inspired by the cooperative co-evolutionary paradigm, this paper presents a two-stage algorithm hybrid generic algorithm (GA) and multi-objective Markov network based EDA (MMEDA), to solve the robust scheduling problem for resource constrained scheduling problem (RCSP) with uncertainty. In the first stage, GA is used to find feasible solution for sequencing sub-problem, and in the second stage, MMEDA is adopted to model the interrelation for resource allocation and calculate the Pareto set for multi-objective optimization problems. One problem-specific local search with considering both makespan and robustness is designed to increase the solution quality. Experiment results based on a benchmark and comparisons demonstrate that our approach is highly effective and tolerant of uncertainty.