Solving the Multi-Skill Resource-Constrained Project Scheduling Problem Considering Skill Depth and Breadth Using ACO Algorithm and LINGO

Abstract

This study addresses the Multi-Skill Resource-Constrained Project Scheduling Problem (MSRCPSP) by investigating the simultaneous effects of workforce skill proficiency (depth) and skill versatility (breadth) on project makespan reduction. Two state-of-the-art techniques, the ACO algorithm integrated with the parallel scheduling approach (PSS) and LINGO, were implemented to solve a known dataset. The results indicate that LINGO’s performance improves compared to the ACO algorithm with increasing resource levels and provides higher solution quality at higher resource availability. On the other hand, with limited resource availability, the ACO algorithm performs better at lower SP levels (0.1 and 0.3). However, as the SP level increases, LINGO outperforms the ACO algorithm, especially for SP values of 0.5, 0.7, and 0.9. Furthermore, it is also observed that there is a positive correlation between resource level and optimality percentage. The results indicate that ACO may be more competitive for scenarios with limited SP and few resources, while LINGO is more effective in instances with greater resource amounts and larger SP values.