Hybrid Ant Colony Optimization in solving Multi-Skill Resource-Constrained Project Scheduling Problem

TL;DR

This paper introduces a hybrid Ant Colony Optimization method combined with heuristic rules to effectively solve the Multi-Skill Resource-Constrained Project Scheduling Problem, demonstrating improved stability and results over classical ACO.

Contribution

The paper presents a novel hybrid ACO approach with a new pheromone update strategy, enhancing solution quality and robustness for MS-RCPSP.

Findings

Hybrid ACO outperforms classical ACO in stability and results.

The approach is validated on benchmark instances derived from real-world data.

Experiments confirm the efficiency of the hybrid method.

Abstract

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.