Multi-Objective Optimization of a Three-Level Sustainable Food Supply Chain: Modeling the Impact of Government Subsidies
3層の持続可能な食品サプライチェーンの多目的最適化：政府補助金の影響のモデル化 (AI 翻訳)
Reza Kiani Mavi, Majid Semiari, Seyed Ashkan Hosseini Shekarabi, Neda Kiani Mavi, Fatemeh Moshkdanian, Arezoo Nikravesh, Sadegh Golsorkhi
🤖 gxceed AI 要約
日本語
本研究は、サプライヤー・製造業者・小売業者の3層からなる食品サプライチェーンの持続可能な設計を支援する統合最適化フレームワークを開発。総コストと炭素排出量の最小化と、認証されたグリーン工程の割合の最大化を同時に図る。政府の政策として、グリーン生産への単位補助金と代替燃料車への使用補助金をモデル化し、NSGA-IIを用いて求解。乳製品のケーススタディでは、適切な補助金により総コスト40%削減、排出量25%削減、グリーン割合80%以上を達成。補助金の臨界範囲と限界収益逓減も示した。
English
This study develops an integrated optimization framework for designing a sustainable three-echelon food supply chain (suppliers, manufacturer, retailers), minimizing cost and carbon emissions while maximizing certified green output. Government subsidies for green production and alternative fuel vehicles are modeled. Using NSGA-II on a dairy case, calibrated subsidies cut total cost by over 40%, emissions by ~25%, and raise green share above 80%. Critical subsidy ranges and diminishing returns are identified, offering actionable guidance for low-carbon supply chain policy.
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、食品産業のサプライチェーン排出削減が喫緊の課題であり、Scope3算定やサプライヤーとの連携が重要。本モデルは補助金政策の効果を定量的に示し、国内の政策立案や企業のサプライチェーン設計に示唆を与える。
In the global GX context
Globally, this research contributes to the growing literature on sustainable supply chain management and policy design for decarbonization. It provides a quantitative framework that aligns with TCFD/ISSB disclosure requirements by linking operational decisions to emissions reduction, and offers insights for designing effective subsidies to accelerate the transition to low-carbon food systems.
👥 読者別の含意
🔬研究者:Provides a novel multi-objective optimization model integrating sustainability pillars and policy incentives, with a scalable NSGA-II heuristic.
🏢実務担当者:Offers a tool to optimize supply chain design balancing cost, emissions, and green output, and to evaluate subsidy impacts on operational decisions.
🏛政策担当者:Demonstrates the effectiveness of targeted subsidies in achieving significant cost and emissions reductions, informing subsidy design for food supply chains.
📄 Abstract（原文）
Abstract This study develops an integrated optimization framework which supports the sustainable design of a food supply chain with three echelons: suppliers, a central manufacturer, and retailers. The model minimizes total cost and carbon emissions while simultaneously maximizing the share of products made with certified green processes, capturing economic, environmental, and social pillars of sustainability. Government policy is represented through two distinct incentives: a per-unit subsidy for green production and a per-use subsidy for alternative fuel vehicles, both directly reducing relevant costs in the decision space. For scalability, a tailored non-dominated sorting genetic algorithm II (NSGA-II) is developed and benchmarked against the exact solution method. Computational experiments based on the data of a dairy products case study indicate that carefully calibrated policy incentives can cut the total system cost by more than 40% and reduce greenhouse gas emissions by around 25% while raising the share of green output to above 80%. The results also indicated a critical range of subsidy values that trigger rapid adoption of clean technologies and demonstrate diminishing marginal returns beyond that range. Comparative tests confirmed that the heuristic achieves solutions within 1% of proven Pareto fronts on moderate examples and maintains high solution quality with substantial time savings on larger problems. The study provides an integrated tool for researchers and decision-makers to align economic performance with environmental and social goals, and it offers actionable guidance on subsidy design for low-carbon resilient food supply chain networks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s40171-025-00454-yfirst seen 2026-08-02 17:57:28
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