Computational sustainability in rural home-based healthcare: circular resource routing and community-embedded service management in China

Introduction Sustainable and equitable home-based healthcare in rural areas requires service systems that balance efficiency, fairness, and responsible resource use, particularly under constraints of limited personnel and dispersed populations. Methods This study proposes a computational sustainability framework for optimizing multi-cycle routing and scheduling of nursing staff in rural China. A bi-objective model is developed to minimize operational costs and maximize equitable service accessibility. A service-time-prioritized greedy algorithm, a customized genetic algorithm, and a tabu search procedure are applied and evaluated using publicly available datasets. Results Compared with classical and random heuristics, the proposed methods achieve approximately 10–15% reductions in operational costs and significant improvements in service equity, measured using a modified Gini coefficient. Discussion The framework integrates efficiency optimization with principles of circular resource management and community-oriented service design. It provides a scalable decision-support tool for planners and contributes to sustainable rural healthcare delivery, aligning with SDG 3 (Good Health and Well-Being) and SDG 11 (Sustainable Cities and Communities).