A Multi-objective Resource Scheduling Optimization Method Based on Multi-factor Satisfaction Assessment

To address the challenge of balancing individual satisfaction with overall efficiency in resource reallocation, this study proposes a multi-objective optimization approach grounded in multi-factor satisfaction evaluation. First, a satisfaction assessment model is constructed by comprehensively considering key factors such as plot area, functional performance, and refurbishment investment. The Analytic Hierarchy Process (AHP) is employed to determine the weights of each factor, ensuring the model’s rationality and scientific validity. Subsequently, a multi-objective integer programming model is formulated with the dual goals of maximizing resource integration and minimizing scheduling costs. A multi-objective genetic algorithm (MOGA) is introduced to improve solution efficiency and the quality of the Pareto front. Experimental results demonstrate that, while satisfying individual satisfaction constraints, the proposed method significantly enhances the rate of resource integration and the system’s overall benefits, outperforming traditional single-objective optimization methods. Designed for dense, resource-constrained environments with diverse preferences, the proposed framework exhibits strong adaptability and coordination capability. It is scalable and applicable to urban renewal, public service planning, logistics networks, and industrial scheduling, offering a practical solution for complex, multi-agent decision-making scenarios.