Hybrid Ant Colony Optimization in solving Multi-Skill Resource-Constrained Project Scheduling Problem
This work addresses scheduling optimization for project management with multi-skill resources, presenting an incremental improvement over existing methods.
The paper tackled the Multi-Skill Resource-Constrained Project Scheduling Problem by proposing a hybrid method combining Ant Colony Optimization with heuristic priority rules, resulting in a more stable and often better-performing approach than classical ACO.
In this paper Hybrid Ant Colony Optimization (HAntCO) approach in solving Multi--Skill Resource Constrained Project Scheduling Problem (MS--RCPSP) has been presented. We have proposed hybrid approach that links classical heuristic priority rules for project scheduling with Ant Colony Optimization (ACO). Furthermore, a novel approach for updating pheromone value has been proposed, based on both the best and worst solutions stored by ants. The objective of this paper is to research the usability and robustness of ACO and its hybrids with priority rules in solving MS--RCPSP. Experiments have been performed using artificially created dataset instances, based on real--world ones. We published those instances that can be used as a benchmark. Presented results show that ACO--based hybrid method is an efficient approach. More directed search process by hybrids makes this approach more stable and provides mostly better results than classical ACO.