Dynamic Facility Location and Allocation Optimization for Sustainable Product-Service Delivery Using Co-Evolutionary Adaptive Genetic Algorithms

Wei Ye and Zhitao Xu ()
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Wei Ye: Xinjiang Uygur Autonomous Region Inspection Institute of Special Equipment of China Merchants Group, Urumqi 830011, China
Zhitao Xu: College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China

Sustainability, 2025, vol. 17, issue 17, 1-18

Abstract: Product-service systems contribute to sustainable development through innovative service integration and novel customer value creation. However, the competitive advantage of sustainable product lifecycle service delivery hinges critically on the operational efficiency of service networks. This study addresses dynamic service facility location and allocation challenges in a time-varying demand environment, focusing on the strategic deployment of multiple comprehensive service centers (CSCs) and their dynamic customer allocation across planning horizons. In this study, we develop a 0–1 integer programming model and propose a novel co-evolutionary adaptive multi-objective genetic algorithm (CA-MOGA) with four key enhancements: (1) optimized chromosome representation, (2) adaptive strategy incorporation, (3) genetic operators with gene repair mechanisms, and (4) elite trans-generation migration. Through real-world case validation, CA-MOGA demonstrates significant improvements over conventional genetic algorithms in both convergence speed and solution quality. The performance and adaptability of the proposed algorithm suggest strong potential for customizable applications in solving diverse complex optimization problems.

Keywords: service facility location; product-service system; sustainability; adaptive genetic algorithms (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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