Efficient genetic algorithm for resource-constrained project scheduling problem

Abstract

This paper presents a new genetic algorithm for the resource-constrained project scheduling problem (RCPSP). The algorithm employs a standardized random key (SRK) vector representation with an additional gene that determines whether the serial or parallel schedule generation scheme (SGS) is to be used as the decoding procedure. The iterative forward-backward improvement as the local search procedure is applied upon all generated solutions to schedule the project three times and obtain an SRK vector, which is reserved into population. Several evolutionary strategies are implemented including the elitist selection (the high quality solution set), and the selection of parents used in crossover operator. The computational experiments on 1 560 standard instances show that the proposed algorithm outperforms the current state-of-the-art heuristic algorithms for J30 and J60, and ranks the third for J120 with 50 000 schedules; it ranks the second for J30 and J60, and ranks the fifth for J120 with 5 000 schedules; it ranks the third, second, and fifth for J30, J60 and J120 with 1 000 schedules, respectively. It is demonstrated that the proposed algorithm is competitive for RCPSP, especially for larger number of schedules.