需要変動と賞味期限信頼性を考慮した三段階低炭素乳製品コールドチェーンネットワークの注文駆動型多目的最適化
Order-Driven Multi-Objective Optimization of a Three-Echelon Low-Carbon Dairy Cold-Chain Network Considering Demand Variability and Shelf-Life Reliability (原題)
Yutong Zhang, Yuguo Li, Yiru Wu, Mengyu Yuan, Jian Li
🤖 gxceed AI 要約
日本語
本研究は、需要変動・製品劣化・環境制約下での乳製品コールドチェーン計画のため、三段階ネットワークの戦術計画を扱う注文駆動型多目的MINLPモデルを構築した。総コスト・輸送由来CO2・鮮度損失率の三目的を、DC選定・在庫・輸送配分・車両構成・冷蔵判断の調整により最小化する。NSGA-IIでパレート解を生成し、エントロピー重み付きTOPSISで妥協解を選定、MOEA/Dをベンチマークとする。感度・シナリオ分析によりサービス水準・賞味期限・道路容量が経済・環境・鮮度性能に与える影響を示した。
English
This study formulates an order-driven multi-objective MINLP for tactical planning of a three-echelon dairy cold-chain network, coordinating DC selection, inventory, transport allocation, vehicle configuration, and refrigeration to minimize cost, transport carbon emissions, and freshness-loss rate. Demand variability uses service-level-based safe demand, and freshness is modeled via Weibull shelf-life reliability with congestion effects. NSGA-II generates Pareto solutions with entropy-weighted TOPSIS for compromise selection, benchmarked against MOEA/D. Sensitivity and scenario analyses reveal how service levels, shelf-life, and road capacity shape economic, environmental, and freshness performance.
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
日本では食品ロス削減と物流2024年問題が喫緊の課題であり、冷蔵輸送の低炭素化と鮮度維持の両立はScope 3排出削減・物流効率化に直結する。乳製品メーカーや物流事業者の戦術計画に応用可能な枠組みとして参考になる。
In the global GX context
While not a disclosure paper, it speaks to the operational side of Scope 3 logistics emissions and food-loss reduction, themes increasingly relevant to CSRD/ISSB supply-chain reporting and to corporate net-zero transition plans for cold-chain-intensive sectors.
👥 読者別の含意
🔬研究者:多目的最適化と鮮度信頼性モデリングを組み合わせたコールドチェーン研究の手法例として有用。
🏢実務担当者:乳製品・冷蔵物流の配送計画でコスト・排出・鮮度のトレードオフを定量化する意思決定支援に使える。
🏛政策担当者:食品ロス削減と物流脱炭素の政策設計において、サービス水準や道路容量が環境・経済に与える影響の参考になる。
📄 Abstract（原文）
Dairy cold-chain network planning requires coordinated decisions under demand variability, product perishability, and environmental constraints. To address these interrelated challenges, this study formulates an order-driven multi-objective mixed-integer nonlinear programming (MINLP) model for the tactical planning of a three-echelon dairy cold-chain network. The model coordinates distribution-center selection, inventory, transportation allocation, vehicle configuration, and refrigeration decisions to minimize total cost, transportation-related carbon emissions, and the quantity- and importance-weighted average freshness-loss rate. Demand variability is represented through service-level-based safe demand, whereas product freshness is evaluated using Weibull-based shelf-life reliability and inventory–transportation exposure. Transportation congestion is further incorporated to capture its effects on travel time, refrigeration emissions, and freshness deterioration. NSGA-II is employed to generate Pareto solutions, with entropy-weighted TOPSIS used for compromise-solution selection and MOEA/D serving as the benchmark algorithm. Numerical results indicate that NSGA-II achieves favorable convergence performance and comparable solution diversity relative to MOEA/D, while small-scale mixed-integer approximation tests support the quality of the obtained solutions. Multi-scale experiments demonstrate stable computational performance as network size increases. Sensitivity and scenario analyses further reveal distinct effects of service levels, shelf-life characteristics, and road capacity on economic, environmental, and freshness performance. The proposed framework provides tactical decision support for coordinating demand-responsive supply, low-carbon operations, and freshness preservation in dairy cold-chain networks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/math14183337first seen 2026-09-16 04:39:03
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