Evolving Priority Rules to Optimize Multi-Objective Criteria for the Resource-Constrained Project Scheduling Problem

The resource-constrained project scheduling problem (RCPSP) is a combinatorial optimization problem that entails allocating a set of tasks to limited resources, such as budget, workers, or equipment while aiming to optimize a defined objective function. Since this problem belongs to the class of NP-hard problems, heuristic approaches such as priority rules (PRs) are often used to solve them. In literature, genetic programming (GP) was used to evolve PRs for RCPSP while optimizing a single criterion, but optimizing only a single criterion is rare in real-world problems. Usually, realworld problems require the optimization of several criteria simultaneously. In this paper, new PRs are evolved using GP for multi-objective RCPSP. More precisely, sets of Pareto optimal solutions were obtained using non-dominated sorting genetic algorithm II (NSGA-II) and non-dominated sorting genetic algorithm III (NSGA-III). Through experiments, these two algorithms were compared using multi-objective performance measures. Additionally, a comparison between multi-objective PRs and single-objective PR was conducted.