A competitive genetic algorithm for resource-constrained project scheduling problem

This paper proposed a multi-objective evolutionary algorithm called the fast elitist non-dominated sorting genetic algorithm (NSGA-II) to solve resource-constrained project scheduling problem (RCPSP) with multiple activity performance modes and two objectives to minimize project makespan and resource utilization smoothness. The solution is represented by a precedence feasible activity list and a mode assignment. An agricultural example with two objectives is used to test the performance of algorithm proposed. The results show that NSGA-II is efficient for solving the multi-objective RCPSP, and find multiple approximation of the Pareto-optimal solutions in a single run of the algorithm.