Multi-Objective Collaborative Allocation Strategy of Local Emergency Supplies Under Large-Scale Disasters
大規模災害時における現地緊急物資の多目的協調配分戦略 (AI 翻訳)
Yi Zhang, Yafei Li
🤖 gxceed AI 要約
日本語
大規模災害初期の現地緊急物資配分において、持続可能性を考慮した多目的協調モデルを提案。TOPSIS法で被災地の緊急度を評価し、遺伝的アルゴリズムで効率・公平性・持続可能性を最適化。雅安地震のケースで、資源効率向上・応答時間短縮・カーボンフットプリント低減を実証。
English
This study proposes a multi-objective collaborative model for local emergency supply allocation under large-scale disasters, integrating sustainability. Using improved TOPSIS, entropy weight, AHP, and a double-layer genetic algorithm, it optimizes efficiency, fairness, and carbon footprint reduction. Validated with the 2013 Ya'an Earthquake, the model outperforms traditional methods in resource utilization, response time, and sustainability.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本では南海トラフ地震や首都直下地震に備え、自治体レベルの備蓄・配分計画が重要。本モデルはSSBJやTCFDのレジリエンス開示にも関連する災害対応の持続可能性を数値化しており、日本の防災・GX政策に示唆を与える。
In the global GX context
Global disaster resilience frameworks (e.g., Sendai Framework, TCFD) increasingly emphasize sustainability. This model offers a quantitative approach to balancing equity, efficiency, and carbon reduction in emergency logistics, relevant for ISSB-aligned disclosure on climate resilience and transition planning.
👥 読者別の含意
🔬研究者:Provides a novel multi-objective optimization model integrating sustainability into emergency logistics, with a validated genetic algorithm approach.
🏢実務担当者:Local emergency managers can adopt the model to improve allocation fairness and reduce carbon footprint in relief operations.
🏛政策担当者:Supports development of disaster risk reduction policies that align with climate goals, offering empirical evidence for local-external collaboration.
📄 Abstract（原文）
In the initial phase of large-scale disasters, delayed external relief supplies make scientific local emergency supply allocation crucial—not only for reducing casualties, but also for advancing sustainable disaster response, a key link in enhancing post-disaster resilience. Existing research mostly focuses on cross-regional material allocation while overlooking local challenges like low resource efficiency and unbalanced supply–demand dynamics. To tackle these limitations in the existing research, this study develops a multi-objective collaborative local emergency supply allocation model centered on sustainability. It uses an improved TOPSIS method to quantify the urgency of needs in disaster-stricken areas, prioritizing material distribution to vulnerable regions in line with the principle of “no vulnerable area left neglected in relief efforts”. The study also integrates the entropy weight method and analytic hierarchy process (AHP) to ensure rational indicator weighting, and designs a double-layer encoded genetic algorithm to obtain optimal allocation schemes that balance efficiency, fairness, and sustainability. Validated using the 2013 Ya’an Earthquake case study, the model outperforms traditional local allocation approaches: it boosts resource utilization efficiency by reducing material shortage rates, accelerates post-disaster recovery by shortening response times, and improves allocation fairness. Findings provide empirical support for the establishment of “local–external” collaborative rescue systems and sustainable disaster risk reduction frameworks. Empirical calculations using case-specific data and real-world estimates verify the model’s practical applicability: it meets the requirements for fair and rapid allocation needs, aligns with the goals of sustainable disaster management, and lowers the carbon footprint of relief operations by lessening reliance on long-distance external materials.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3390/su18020573first seen 2026-06-29 08:04:34
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