An organizational cooperative coevolutionary algorithm for multimode resource-constrained project scheduling problems

Abstract

In recent years, the resource-constrained project scheduling problem (RCPSP) with multiple execution modes is becoming more and more popular. In this paper, a new cooperative coevolutionary algorithm based on the concept of organizations, namely Organizational Cooperative Coevolutionary Algorithm for MRCPSPs (OCCA-MRCPSPs), is proposed for solving this problem. The objective is to find a schedule of activities together with their execution modes so that the makespan is minimized. In the OCCA-MRCPSPs, the population is divided into two subpopulations, for activities execution modes, respectively. The two subpopulations evolve independently, and each subpopulation is composed of organizations. During the evolutionary process, the global searching and the local searching are combined efficiently by conducting different operators. At first, each subpopulation searches the whole space of its domain through the splitting operator, the annexing operator, and the cooperation operator. Afterwards, the two subpopulations are combined to form complete solutions, and a local search operator is performed. In the experiments, the performance of OCCA-MRCPSPs is validated on benchmark problem sets J10, J12, J14, and J16 from the PLPSIB, and the experimental results show that the OCCA-MRCPSPs obtains a good performance not only in terms of the optimal solutions found but also in terms of the average deviations from optimal solutions.