An efficient hybrid algorithm for resource-constrained project scheduling

We propose an efficient hybrid algorithm, known as ACOSS, for solving resource-constrained project scheduling problems (RCPSP) in real-time. The ACOSS algorithm combines a local search strategy, ant colony optimization (ACO), and a scatter search (SS) in an iterative process. In this process, ACO first searches the solution space and generates activity lists to provide the initial population for the SS algorithm. Then, the SS algorithm builds a reference set from the pheromone trails of the ACO, and improves these to obtain better solutions. Thereafter, the ACO uses the improved solutions to update the pheromone set. Finally in this iteration, the ACO searches the solution set using the new pheromone trails after the SS has terminated. In ACOSS, ACO and the SS share the solution space for efficient exchange of the solution set. The ACOSS algorithm is compared with state-of-the-art algorithms using a set of standard problems available in the literature. The experimental results validate the efficiency of the proposed algorithm.