Industry 4.0ポートフォリオ選定におけるライフサイクル炭素負荷とリーン価値の均衡のためのゴールプログラミング手法
A goal programming approach for balancing life-cycle carbon burden and lean value in Industry 4.0 portfolio selection (原題)
Bilal Ervural, Turan Paksoy
🤖 gxceed AI 要約
日本語
Industry 4.0技術の導入がリーン価値と炭素負荷の双方に影響する点に着目し、両者を目標値からの乖離最小化で均衡させる二値重み付きゴールプログラミングモデルを構築。技術起因のライフサイクル炭素（生産・運用・保守・廃棄）と投資コスト・導入難易度・リーン貢献・技術依存関係を制約下で考慮し、炭素優先・均衡・リーン優先の各戦略のトレードオフを数値例で示す。
English
This study builds a binary weighted goal programming model to select Industry 4.0 technology portfolios that balance lean value creation against life-cycle carbon burden (production, operation, maintenance, end-of-life), without crediting avoided emissions. It constructs feasible portfolios under target, cost, and dependency constraints, and an illustrative application compares carbon-priority, balanced, and lean-priority strategies, revealing trade-offs and testing robustness to parameter uncertainty.
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
Scope 3・カーボンニュートラル投資の意思決定に直結する手法で、SSBJ開示や設備投資の炭素評価を進める日本企業の実務担当者に参考となる。ただし日本固有の制度・事例は扱っていない。
In the global GX context
Offers a decision-support tool for firms integrating carbon accounting into capital allocation, relevant to Scope 3 and internal carbon pricing discussions under ISSB/CSRD-driven disclosure regimes, though it does not engage disclosure standards directly.
👥 読者別の含意
🔬研究者:ライフサイクル炭素とリーン価値を統合した多目的最適化の定式化として、持続可能なオペレーションズリサーチ研究に資する。
🏢実務担当者:設備・技術投資の選定時に炭素負荷とコスト・リーン効果を同時評価する枠組みとして活用できる。
📄 Abstract（原文）
The rapid diffusion of Industry 4.0 technologies creates a dual decision challenge for firms: these technologies can support lean-oriented value creation while also increasing the carbon footprint associated with hardware, energy use, data processing, maintenance, and digital infrastructure. This study develops a multi-objective framework for selecting an Industry 4.0 technology portfolio that balances lean value creation against carbon control. A binary weighted goal programming model minimizes normalized deviations from predefined carbon and lean value targets while considering technology-attributable, life-cycle-oriented carbon burden, investment cost, implementation difficulty, multidimensional lean value contribution, and technology dependencies. The carbon boundary covers production, operation, maintenance, and end-of-life burdens attributable to the technologies themselves; technology-enabled avoided emissions are not credited. Unlike ranking-oriented multi-criteria decision-making approaches, the framework constructs feasible technology portfolios under explicit target, resource, and dependency constraints. An illustrative numerical application evaluates carbon-priority, balanced, and lean-priority strategies, revealing distinct trade-offs between carbon excess and lean value shortfall. Sensitivity and robustness analyses further examine the effects of managerial settings and parameter uncertainty on portfolio selection. By integrating environmental and lean objectives within a single optimization framework, the study contributes to sustainable and lean operations research and provides a structured decision-support tool for carbon-aware and lean-oriented Industry 4.0 investment planning.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.20517/cf.2026.106first seen 2026-11-07 04:40:14
🔔 こうした論文の新着を逃したくない方は キーワードアラート に登録（無料・3キーワードまで）。
gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。