Adaptive hybrid optimization for integrated project scheduling and staffing problem with time/resource trade-offs

Abstract

The integration of project scheduling and human resource allocation is crucial in modern project management, particularly in complex and resource-constrained environments. This study addresses the Integrated Project Scheduling and Personnel Staffing Problem (IPSPSP) with time/resource trade-offs by proposing a dual-objective optimization model that minimizes both project duration and personnel cost. To solve this problem, we introduce an adaptive hybrid algorithm combining the Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO). The algorithm employs hybrid encoding for activity modes, activity priority lists and personnel allocation plans, coupled with a hypervolume-based adaptive search mechanism to improve solution quality. Experimental results demonstrate that the adaptive hybrid algorithm outperforms standalone NSGA-II and MOPSO in generating schedules and optimizing resource allocation. This study makes significant contributions by presenting a novel integrated model tailored for projects, an effective adaptive hybrid optimization algorithm and a comprehensive performance evaluation, thereby advancing the field of integrated scheduling and staffing in project management.