New Effective Differential Evolution Algorithm for the Project Scheduling Problem

Abstract

The Resource-Constrained Project Scheduling Problem (RCPSP) is a classical combinational optimization problem that has many practical applications. In this paper, we consider an extension of the RCPSP which called Multi-Skill Resource-Constrained Project Scheduling Problem (MS-RCPSP). In the past few years, various approaches have been proposed to solve this problem such as Genetic Algorithm and Ant Colony Optimization. However, reducing the likelihood of premature convergence is the challenge facing researchers. In this paper, we present a novel algorithm called DEM in order to solve the MS-RCPSP problem. Besides using the differential evolution metaheuristic, we also develop the Reassignment function to improve the solution quality at the end of each iteration, so that proposed algorithm converges rapidly to global extremum. Moreover, proposed algorithm also avoids getting trapped in a local extremum. The experiments were conducted to evaluate the performance of the proposed algorithm, as well as to compare the DEM with previous algorithms such as GreedyDO, HAntCO, and GA. Experimental results show that the proposed algorithm can effectively enhance searching efficiently.