A late-mover genetic algorithm for resource-constrained project-scheduling problems

The Resource-Constrained Project Scheduling Problem (RCPSP) plays a critical role in various management applications. Despite its importance, research efforts are still ongoing to improve lower bounds and reduce deviation values. This study aims to develop an innovative and straightforward algorithm for RCPSPs by integrating the 1+1 evolution strategy into a genetic algorithm framework. Unlike most existing studies, the proposed algorithm eliminates the need for parameter tuning and utilizes real-valued numbers and path representation as chromosomes. Consequently, it does not require priority rules to construct a feasible schedule. The algorithm's performance is evaluated using the RCPSP benchmark and compared to alternative algorithms, such as cWSA, Hybrid PSO, and EESHHO. The experimental results demonstrate that the proposed algorithm is competitive, while the exploration capability remains a challenge for further investigation.

Automated summary

This study aims to develop an innovative and straightforward algorithm for the Resource-Constrained Project Scheduling Problem (RCPSP) by integrating the 1+1 evolution strategy into a genetic algorithm framework. Unlike most existing studies, the proposed algorithm eliminates the need for parameter tuning and utilizes real-valued numbers and path representation as chromosomes, resulting in no requirement for priority rules to construct a feasible schedule. The algorithm's performance is evaluated using the RCPSP benchmark and compared to alternative algorithms, demonstrating competitiveness but highlighting the challenge of exploration capability for further investigation.