Multi skill project scheduling optimization based on quality transmission and rework network reconstruction

Quality deficiencies are widely acknowledged as a primary driver of project rework, with personnel skill levels serving as a critical determinant of activity quality. This study presents a scheduling model that integrates quality transmission mechanisms and dynamic rework subnet reconstruction within the Multi-Skill Resource-Constrained Project Scheduling Problem (MSRCPSP) framework. The proposed model aims to optimize project duration while mitigating rework risks. To address the computational complexity of the model, an Improved Gazelle Optimization Algorithm (GOAIP) was developed, incorporating dynamic operators, shuffle crossover, and Gaussian mutation strategies to balance global and local optimization. Experimental validation across diverse case scales demonstrates that the proposed model and algorithm outperform mainstream optimization techniques in solution accuracy and convergence efficiency, highlighting their robust applicability and practical significance.