GIRCS: An effective evolutionary scheme to solve SBM-RCPSP scheduling problems for industrial production planning

Abstract

Resource Constrained Project Scheduling Problem (RCPSP) is a fundamental scheduling problem that has attracted much attention from researchers for many years. Many variants of this problem have been modeled, and many different approaches have been proposed and published in journals. However, the classical mathematical models of RCPSP still have some limitations that make it not really suitable for application in practical projects. This paper introduces practical applications and classifications of the RCPSP problem. After describing some common extensions of the original RCPSP problem, we briefly introduce three approaches that have been used to solve those extensions, including exact, heuristic, and metaheuristic algorithms. We define a novel scheduling problem named SBM-RCPSP (Skill-Based Makespan-RCPSP) which overcomes the limitations of previous variants of the RCPSP problem. The Graham representation of the SBM-RCPSP problem is introduced, and then the problem is proven to be NP-Hard. To solve the SBM-RCPSP problem, we propose an evolutionary algorithm called GIRCS inspired by Cuckoo Search and improved to reduce the total project execution time. Experimental results on datasets have demonstrated that the proposed scheme finds more efficient schedules than previous solutions.