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<p>Earthquake-prone regions in western China face substantial emergency-logistics challenges due to sudden disasters, disrupted transportation networks, and highly uncertain material demand. To improve pre-disaster defensive planning and post-disaster response efficiency, this study develops a robust optimization model for emergency warehouse location, capacity selection, and material allocation. The model incorporates dual uncertainties–demand fluctuation and warehouse disruption–and integrates construction, transportation, and response-time costs within a unified budgeted-uncertainty framework that ensures tractability while guarding against worst-case conditions. A case study based on the 2025 Shigatse earthquake in Tibet shows that the demand-uncertainty budget exhibits a clear saturation threshold around <InlineEquation ID="IEq1"> <EquationSource Format="TEX">\(\Gamma _d \approx 8\)</EquationSource> </InlineEquation>, beyond which additional conservatism yields only limited marginal benefit. The results further show that robust optimization induces a defensively reconfigured deployment pattern relative to the deterministic benchmark, while policy-driven weight adjustment changes the intensity of conservatism without altering the existence of the saturation pattern. These findings provide quantitative support for designing protection-oriented and operationally feasible emergency material reserve systems in disaster-prone regions.</p>

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Robust optimization of emergency warehouse location and resource allocation in natural disaster-prone regions

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月29日

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  • Dafu Wang,
  • Xiaoning Zhu,
  • Daqing Gong,
  • Qin Zhang

摘要

Earthquake-prone regions in western China face substantial emergency-logistics challenges due to sudden disasters, disrupted transportation networks, and highly uncertain material demand. To improve pre-disaster defensive planning and post-disaster response efficiency, this study develops a robust optimization model for emergency warehouse location, capacity selection, and material allocation. The model incorporates dual uncertainties–demand fluctuation and warehouse disruption–and integrates construction, transportation, and response-time costs within a unified budgeted-uncertainty framework that ensures tractability while guarding against worst-case conditions. A case study based on the 2025 Shigatse earthquake in Tibet shows that the demand-uncertainty budget exhibits a clear saturation threshold around \(\Gamma _d \approx 8\) , beyond which additional conservatism yields only limited marginal benefit. The results further show that robust optimization induces a defensively reconfigured deployment pattern relative to the deterministic benchmark, while policy-driven weight adjustment changes the intensity of conservatism without altering the existence of the saturation pattern. These findings provide quantitative support for designing protection-oriented and operationally feasible emergency material reserve systems in disaster-prone regions.