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二重の不確実性下における環境配慮型マスタープロダクションスケジューリング：機械学習と確率的最適化のハイブリッドアプローチ

Eco-Conscious Master Production Scheduling Under Dual Uncertainty: A Hybrid Approach Using Machine Learning and Stochastic Optimization (原題)

Wiam ALAMI CHENTOUFI, Amine ZITOUNI, Abdellah El BARKANY

Crossrefプレプリント2026-08-13#エネルギー転換経営インパクト: コスト削減対象セクター: manufacturing
DOI: 10.21203/rs.3.rs-10568612/v1
原典: https://doi.org/10.21203/rs.3.rs-10568612/v1

🤖 gxceed AI 要約

日本語

電力価格の高変動と需要の非定常性という二重の不確実性に対処するため、LSTMによる価格予測とLightGBM分位点回帰による需要予測を組み合わせた「予測・最適化」手法を提案。シナリオ削減を経て二段階確率MILPに投入し、欧州EPEX SPOT市場参加の多品種工場で検証した。確率的解の価値(VSS)は34.3%に達し、負の電力価格を活用した動的負荷シフトと意図的な過剰在庫によりコスト削減とScope 2排出削減を同時に実現する。

English

This study proposes a predict-and-optimize framework combining LSTM energy-price forecasting with LightGBM quantile demand forecasting, feeding a two-stage stochastic MILP via Kantorovich scenario reduction. Validated on a multi-product plant in the EPEX SPOT market, it achieves a 34.3% Value of the Stochastic Solution, exploiting negative power prices through dynamic load shifting and deliberate overstocking. The purely economic cost reduction incidentally lowers Scope 2 emissions, offering supply-chain practitioners a scalable tool for the green energy transition.

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

再エネ大量導入に伴う電力価格変動・負の価格は日本でも顕在化しつつあり、需要家側の生産計画と電力調達を統合する発想は、Scope 2削減とエネルギーコスト最適化を両立させる実務的示唆を与える。SSBJ開示におけるScope 2算定の前提となる操業データ管理にも関連する。

In the global GX context

As negative and volatile power prices spread with renewable integration globally, this work links operational scheduling directly to Scope 2 emissions outcomes, complementing disclosure frameworks (GHG Protocol Scope 2, CSRD) with an operational lever for emissions reduction. It illustrates how economically motivated optimization can deliver climate co-benefits relevant to transition-plan credibility.

👥 読者別の含意

🔬研究者:確率的生産計画と機械学習予測を統合する手法として、エネルギー・需要の同時不確実性を扱う研究設計の参考になる。

🏢実務担当者:電力価格変動下での生産・調達計画の立案と、コスト削減がScope 2削減に波及するメカニズムを実務に応用できる。

🏛政策担当者:負の電力価格や需給変動が産業側の操業・排出行動に与える影響を、柔軟性活用策の設計に反映する際の示唆となる。

📄 Abstract（原文）

Abstract The inherent weaknesses of conventional, deterministic production scheduling have been exposed by the current European energy crisis and the growing integration of renewable sources, which have brought high-frequency volatility and negative price occurrences into day-ahead power markets. This study suggests an integrated, data-driven "predict-and-optimize" methodology to overcome the concurrent uncertainties of severe energy price swings and non-stationary consumer demand. We create a hybrid machine learning pipeline that uses a Long Short-Term Memory (LSTM) deep learning network to capture intricate, non-linear energy pricing dynamics and LightGBM with quantile regression for probabilistic demand forecasting. A Kantorovich distance-based scenario reduction approach is used to optimally condense these continuous prediction distributions after they have been discretized using Monte Carlo sampling. A Two-Stage Stochastic Mixed-Integer Linear Programming (MILP) model is directly fed the resultant scenario tree. The suggested framework exhibits strong economic and environmental resilience after being validated on a multi-product manufacturing plant taking part in the European EPEX SPOT market. The Value of the Stochastic Solution (VSS) is 34.3%, according to computational results, which successfully minimizes predicted operating expenses. The stochastic approach takes advantage of negative power prices by proactively implementing dynamic load shifting and intentional overstocking, while eschewing stringent Just-In-Time (JIT) restrictions. Additionally, the facility's Scope 2 greenhouse gas emissions are naturally reduced by this strictly economic drive of cost reduction. In the end, this study effectively bridges the gap between stochastic mathematical programming and predictive sequence learning, providing supply chain professionals with a scalable solution to manage the current green energy transition.

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