A Genetic-Local Search Algorithm Approach for Resource Constrained Project Scheduling Problem

In scheduling Resource Constrained Project Scheduling Problem (RCPSP) is a well-known NP hard problem. Several metaheuristics have been applied to find near optimal solution for resource constrained project scheduling problem (RCPSP). In this paper, Genetic-Local search algorithm (GLSA) is proposed to tackle the single mode resource constrained project scheduling problem. The objective of Genetic-Local search algorithm is to minimize makespan of schedule. Genetic-Local search algorithm combines elements from evolutionary and local search procedure by using priority based crossover, neighbourhood mutation operation and neighbourhood search procedure. The algorithm treats the solution of the resource constrained project scheduling problem as activity list and serial schedule generation scheme is used to generate the solution. For solving case studies in PSLIB Library, Performance of GLSA is found out against other metaheuristics. The results show that Genetic-Local search algorithm is a high quality approach that outperforms all recent algorithms for the resource constrained project scheduling problem known by the authors of this paper for the instance sets J30, J60, J90 and J120 and it is competitive with other heuristics for the instance set J30, J60, J90 and J120.

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