Sustainable and Resilient Production–Distribution Planning Under Stochastic Demand: A Carbon-Aware MILP Framework with Lost Sales and Rolling Horizon Replanning
確率的需要下での持続可能でレジリエントな生産・配送計画：ロストセールとローリングホライズン再計画を考慮した炭素意識MILPフレームワーク (AI 翻訳)
Mohammed Machkour, A. El Barkany, Bilal Harras
🤖 gxceed AI 要約
日本語
本研究は、自動車サプライチェーンにおける炭素意識型の生産・配送計画のための確率的混合整数線形計画（MILP）フレームワークを開発した。需要の不確実性をシナリオで表現し、内部炭素価格で排出を貨幣化、ロストセールペナルティでサービス低下を捉える。ローリングホライズン分析により計画の応答性を評価し、炭素価格は主に経済的評価メカニズムとして機能し、ロストセールペナルティがサービス性能に強く影響することを示した。
English
This study develops a stochastic MILP framework for carbon-aware production-distribution planning in an automotive supply chain. It integrates demand uncertainty, internal carbon pricing, and lost-sales penalties, and evaluates rolling-horizon replanning. Results show carbon pricing acts mainly as an economic valuation mechanism, while lost-sales penalties strongly influence service performance; demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand.
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SSBJ開示やサプライチェーン排出量算定が進む中、内部炭素価格を生産・配送計画に組み込む本手法は、自動車業界を中心に実務的な示唆を与える。需要不確実性下でのコスト・炭素・サービス最適化は、日本企業のレジリエンス強化と脱炭素経営の両立に貢献する。
In the global GX context
Globally, this framework aligns with TCFD/ISSB expectations by integrating carbon pricing into operational planning. It offers a decision-support tool for balancing cost, carbon, and service under uncertainty, relevant for supply chain resilience and transition finance considerations.
👥 読者別の含意
🔬研究者:Provides a stochastic MILP model integrating carbon pricing and lost sales, useful for further research on supply chain optimization under uncertainty.
🏢実務担当者:Offers a practical framework for automotive supply chain planners to evaluate cost-carbon-service trade-offs and internal carbon pricing.
🏛政策担当者:Demonstrates how internal carbon pricing can be operationalized in supply chain decisions, informing policy on carbon pricing mechanisms.
📄 Abstract（原文）
Background: Manufacturing supply chains must increasingly coordinate cost, environmental impact, and service continuity under demand uncertainty and limited capacity. Methods: This study develops a stochastic mixed-integer linear programming framework for carbon-aware production–distribution planning in an automotive supply chain. The model jointly optimizes production quantities, inventory levels, shipments, truck usage, and lost sales over a multi-period horizon. Demand uncertainty is represented through scenarios, while production- and transportation-related emissions are monetized using an internal carbon price. Lost-sales penalties capture service degradation when demand cannot be fulfilled by the focal plant, and a rolling-horizon analysis evaluates planning responsiveness as demand information is updated. The framework is applied to an industrially inspired, capacity-constrained automotive case with multiple products, production lines, destinations, and demand scenarios. Computational experiments assess carbon pricing, lost-sales penalties, demand volatility, deterministic versus stochastic planning, and rolling-horizon replanning. Results: Results show that carbon pricing mainly acts as an economic valuation mechanism under the studied fixed-structure configuration, whereas lost-sales penalties strongly influence service performance. Demand volatility increases unmet demand, and lower emissions may reflect lower fulfilled demand rather than improved efficiency. Conclusions: The study provides a decision-support framework for evaluating cost–carbon–service trade-offs under stochastic demand while acknowledging single-plant and fixed-routing limitations.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://www.mdpi.com/2305-6290/10/8/175/pdf?version=1785745895first seen 2026-08-09 05:32:22
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