Sustainable reverse logistics network design for end-of-life electric vehicle batteries under uncertainty: a fuzzy chance-constrained multi-objective approach for Saudi Arabia
不確実性下における廃電気自動車バッテリーの持続可能なリバースロジスティクスネットワーク設計：サウジアラビアを対象としたファジィ機会制約多目的アプローチ (AI 翻訳)
Majdi Argoubi, Khaled Mili
🤖 gxceed AI 要約
日本語
サウジアラビアのEV普及に伴う廃バッテリーリサイクルネットワークの最適設計を提案。不確実性をファジィ数でモデル化し、経済コストと環境コストのトレードオフを分析。リヤド地域のケーススタディでは、わずかな経済的追加支出で大幅な環境改善が可能であることを示した。
English
Proposes a fuzzy multi-objective optimization model for designing a reverse logistics network for end-of-life EV batteries in Saudi Arabia. The model balances economic and environmental costs under uncertainty. A case study in Riyadh shows that a minimal economic sacrifice (0.002% cost increase) can reduce environmental costs by 4.1%, supporting circular economy and net-zero targets.
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
サウジアラビアの事例ですが、日本でもEV廃バッテリーのリサイクルインフラ整備が急務であり、本論文の最適化アプローチは日本の地域ネットワーク設計に応用可能です。特に、不確実性下での経済性と環境性のトレードオフ分析は、日本のSSBJや循環経済政策においても有用な知見を提供します。
In the global GX context
While focused on Saudi Arabia, the optimization framework is broadly applicable to any region building EV battery recycling networks. The demonstrated low-cost environmental gains offer a strong business case for circular economy investments, relevant for global disclosure frameworks like ISSB and transition finance.
👥 読者別の含意
🔬研究者:Provides a rigorous multi-objective optimization model with fuzzy uncertainty that can be adapted to other regions and battery chemistries.
🏢実務担当者:Offers a decision-support tool for designing cost-effective and environmentally efficient battery recycling networks, with clear economic trade-offs.
🏛政策担当者:Quantifies the minimal economic cost of achieving significant environmental benefits, supporting policy on mandatory recycling targets and infrastructure subsidies.
📄 Abstract（原文）
The rapid expansion of electric vehicle (EV) adoption in Saudi Arabia under Vision 2030 generates an urgent need for end-of-life (EoL) power battery management infrastructure. Despite ambitious circular economy targets, the Kingdom currently lacks a standardized recycling network, and no quantitative optimization framework exists for the Gulf region's specific regulatory and market context. This paper proposes a fuzzy chance-constrained multi-objective mixed-integer nonlinear programming (MO-MINLP) model for the optimal design of a three-tier EV battery reverse logistics network, simultaneously minimizing economic costs and environmental carbon emission costs under uncertain battery State of Health (SoH) distributions and fluctuating market prices. Uncertain parameters are represented as triangular fuzzy numbers handled through possibility theory at a confidence level of χ = 0.85, yielding a tractable crisp equivalent solved using LINGO 18.0. A linear weighted Pareto method identifies the optimal economic-environmental trade-off across 11 weight combinations. The model is validated through a case study of the Riyadh metropolitan area comprising 34 candidate network nodes. At the preferred compromise weight combination ( w 3 , w 2 ) = (0.7, 0.3), the construction and operating cost is SAR 24,314.70 thousand and the environmental cost is SAR 815.24 thousand. Relative to the purely economic optimum, an incremental economic expenditure of only SAR 0.48 thousand (less than 0.002% of total costs) reduces environmental costs by SAR 35.12 thousand (4.1%), demonstrating that substantial ecological gains are achievable at virtually negligible economic sacrifice. The proposed framework provides a quantitative decision-support tool aligned with Saudi Arabia's 2060 net-zero target and directly supports SDG 9, SDG 12, and SDG 13.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.3389/frsus.2026.1868046first seen 2026-07-07 05:04:11 · last seen 2026-07-07 05:24:30
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