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Robust Optimization for Age-friendly Facility Location Problem
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AI
DOI：10.1007/s11518-026-5741-9.png)
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Abstract
En 中文
Optimizing age-friendly community facilities has become crucial for enhancing elderly well-being. This study proposes a distributionally robust optimization (DRO) framework for public seating facility planning, addressing the limitations of conventional models in handling multidimensional uncertainties. We construct Wasserstein metric-based ambiguity sets to characterize stochastic interactions among individual physiological traits (e.g., balance capacity), environmental factors (e.g., terrain irregularity), and fatigue-induced risk accumulation. A dual-layer constraint system synergizes safety assurance (fall probability control) and functional adequacy (demand-responsive coverage) under budget limits. To address computational challenges in large-scale networks, we develop a Two-stage Greedy-deterministic (TSGD) algorithm with dynamic candidate screening and dual-variable correction, achieving high computational efficiency and scalability for large-scale networks. For small-scale validation, we further propose a Distributionally Robust Simulated Annealing (DRSA) algorithm. Numerical experiments demonstrate the following facts: 1) TSGD reduces computation time by 66% compared to Gurobi in 200-node scenarios with < 3% optimality gap; 2) The Wasserstein-DRO formulation ensures 89.3% risk compliance under worst-case uncertainty. Theoretically, this work integrates safety-functionality synergy into stochastic facility location models. Practically, it provides a decision-making paradigm for spatial optimization in aging communities, with algorithmic innovations enabling scalable implementation.
Keywords:
Age-friendly retrofit
facility location problem
distributionally robust optimization
wasserstein metric
heuristic algorithm
Journal
J
IF:
2
Papers:
38
Citations:
0