季節的供給と確率的需要を考慮したヤナギペレット生産のバイオマス在庫最適化
Biomass inventory optimization for willow-based pellet production integrating seasonal supply and stochastic demand (原題)
Zhuoxiao Wu, Yu Wei, Nathaniel Anderson
🤖 gxceed AI 要約
日本語
ヤナギペレット生産施設を対象に、季節的な収穫能力と確率的な需要を考慮した二段階確率混合整数計画モデルを開発。ニューヨーク州の事例で、確率的在庫政策が決定論的アプローチと比べ平均コストを1%削減し、コスト変動を44%低減することを示した。多原料戦略によるサプライチェーン強靭性も提案。
English
Develops a two-stage stochastic MILP model for willow-based pellet production, integrating seasonal harvesting and stochastic demand. Case study in New York shows stochastic inventory policies reduce mean cost by 1% and cost variability by 44% vs deterministic approach. Suggests multi-feedstock strategy for resilience. Framework is computationally efficient and broadly applicable.
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
日本では木質バイオマス発電の安定調達が課題であり、季節変動や需要不確実性への対応はFIT後の事業継続に重要。本モデルは国内のバイオマスサプライチェーン最適化に応用可能で、地域分散型エネルギーシステムの効率化に寄与する。
In the global GX context
Contributes to global biomass supply chain optimization literature, addressing seasonality and demand uncertainty. Provides a robust inventory planning framework applicable to diverse biomass industries, supporting renewable energy transition and supply chain resilience. Relevant for regions with seasonal feedstock constraints.
👥 読者別の含意
🔬研究者:Provides a stochastic optimization framework for biomass inventory management, useful for extending to other renewable supply chains.
🏢実務担当者:Offers a practical model for biomass facility managers to reduce cost variability and improve supply chain resilience.
📄 Abstract（原文）
Biomass supply chains face both internal challenges (low energy density, bulkiness, seasonality) and external challenges (market uncertainty). We developed a two-stage stochastic mixed-integer linear programming (MILP) model that configures an (s, S) inventory control policy for a willow-based pellet production facility. This model accounts for seasonal willow harvesting capacity and stochastic daily wood pellet demand. Using a case study of a proposed wood pellet production facility in Schenectady County, New York, USA, we evaluated model-suggested inventory control policies using multiple out-of-sample pellet demand scenarios by assuming a moderate demand uncertainty with a 10% coefficient of variation. Inventory policies acquired from modeling stochastic demands resulted in a 1% mean cost reduction compared to the deterministic approach. More importantly, these policies reduced cost variability by 44% under moderate demand uncertainty. We expected the performance advantage of the stochastic model to increase as uncertainty levels rise. Test cases show that frequent inventory policy adjustments provide additional cost savings. The model successfully accounted for seasonal supply constraints and stochastic market demand to facilitate a multi-feedstock strategy that offers additional supply chain resilience and associated cost reduction. Overall, the stochastic modeling framework provides facility managers with more robust inventory planning under real-world constraints. The framework’s computational efficiency and broad applicability make it suitable for adoption by diverse biomass industries with uncertainties in their supply chains, particularly those facing seasonal feedstock supply constraints and stochastic end product demands.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.25675/3.025585first seen 2026-08-19 04:33:21
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