Sustainable management of institutional water infrastructure is essential to maintaining reliable campus operations while supporting resource-efficient, technologically advanced teaching environments. However, infrastructure investment planning involves complex trade-offs among system reliability, environmental sustainability, and lifecycle cost, which are often evaluated using fragmented or subjective decision frameworks. This study develops a data-driven decision-support model that integrates entropy-based criterion weighting with the Non-dominated Sorting Genetic Algorithm II (NSGA-II) to optimize investment allocation across institutional water infrastructure programs. Performance indicators representing technical reliability, environmental benefits, and economic efficiency are normalized and objectively weighted using the entropy method to reflect their respective informational contributions. These weights are then embedded in a multi-objective optimization model that identifies Pareto-efficient investment strategies subject to realistic budget and feasibility constraints. The results reveal clear non-linear trade-offs between performance improvements and financial burden, demonstrating that a diversified allocation across efficiency enhancement, reuse expansion, treatment upgrades, and monitoring systems yields the most balanced outcomes. A compromise solution identified in the Pareto knee region achieves high system performance and environmental benefits at a moderate cost, indicating the practical feasibility of integrated investment strategies. Sensitivity analysis confirms the model’s robustness to parameter uncertainty. The proposed framework provides a transparent, scalable methodology for infrastructure prioritization in institutional water systems and supports long-term operational stability on smart teaching campuses.
Wang et al. (Sat,) studied this question.
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