Extraction of Decision Alternatives in Project Management: Application of Hybrid PSO-SFLA

Abstract

Resource-constrained project-scheduling problem (RCPSP) management is a process of scheduling activities based on time and resources to determine an appropriate decision alternative that minimizes the time duration of a project by considering resource limitations and precedence of activities. The critical path method (CPM) is a management tool for project scheduling that considers the longest path through the activity network of an entire project. By using the CPM tool in RCPSPs, the complex and discrete nature of the solution domain for such problems causes failing of traditional and gradient-based methods in determining an optimal or even feasible solution in some problems. Thus, evolutionary algorithms are extensively employed and adapted to extract decision alternatives in the RCPSP. Hybrid algorithms focus on a more efficient search in the decision space. This paper proposes a hybrid algorithm based on particle swarm optimization (PSO) and shuffled frog leaping algorithm (SFLA) to solve simple and complex RCPSPs. Convergence speed and number of critical paths are two factors that show the capabilities of the PSO-SFLA algorithm in solving RCPSPs. Results show that the hybrid algorithm is more capable to determine an optimal solution in all problems, even with fewer number of iterations, as well as more feasible and optimal solutions compared with the individual application of PSO and SFLA. Moreover, the hybrid PSO-SFLA showed an improvement compared with other algorithms employed to determine more paths, especially in a simple network.