Optimizing transportation and reducing carbon footprint: a multi-objective approach with carbon cap and offset policy in fixed charge scenarios under type-2 neutrosophic uncertainty
輸送最適化とカーボンフットプリント削減：タイプ2ニュートロソフィック不確実性下での炭素上限・オフセット政策を考慮した固定費用シナリオにおける多目的アプローチ (AI 翻訳)
Vincent F. Yu, Abhijit Bera, Rupantar Das, Soumen Kumar Das
🤖 gxceed AI 要約
日本語
本研究は、タイプ2ニュートロソフィック不確実性を考慮した多目的固定費用輸送モデルを提案し、輸送コスト、時間、製品劣化の同時最適化を図る。炭素上限・オフセット政策を統合し、持続可能な物流計画の意思決定を支援する。数値実験により、ファジーアプローチがIFPより優れていることを示した。
English
This study proposes a multi-objective fixed-charge transportation model under type-2 neutrosophic uncertainty, optimizing cost, time, and deterioration while integrating carbon cap and offset policies. Numerical experiments show fuzzy approaches outperform IFP, providing insights for sustainable logistics planning.
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
日本の物流業界では、2024年問題やカーボンニュートラル宣言に対応するため、輸送効率化と排出削減の両立が急務。本モデルは、不確実性下での意思決定支援に有用で、SSBJ開示やサプライチェーン排出量削減にも応用可能。
In the global GX context
Globally, this research aligns with ISSB and CSRD requirements for supply chain emissions disclosure. The integration of carbon offset policies in transportation planning offers a practical framework for companies to balance cost and sustainability, relevant for logistics and manufacturing sectors.
👥 読者別の含意
🔬研究者:Provides a novel multi-objective optimization framework combining neutrosophic uncertainty and carbon policies, useful for further research in sustainable logistics.
🏢実務担当者:Offers a decision-support tool for logistics managers to optimize transportation while meeting carbon reduction targets, potentially aiding in Scope 3 reporting.
🏛政策担当者:Demonstrates how carbon cap and offset policies can be integrated into operational models, informing policy design for sustainable transport.
📄 Abstract（原文）
Global warming, driven by urbanization, industrial growth, and increased vehicle usage, has made carbon emission reduction a critical priority for industries alongside their financial goals. This study proposes a multi-objective fixed-charge transportation (MOFCT) model that incorporates type-2 neutrosophic parameters to capture the inherent uncertainty in supply, demand, and other key factors. The model simultaneously optimizes three objectives: total transportation cost, including fixed route cost, preservation cost, and carbon emission costs; transportation time; and product deterioration level. A ranking method is applied to convert the neutrosophic parameters into crisp values for computational analysis. Leontief-scalarized fuzzy programming (LS-FP) is proposed to derive Pareto-optimal solutions, while traditional fuzzy programming (FP) and intuitionistic fuzzy programming (IFP) are used for comparison to assess the relative performance. Numerical experiments demonstrate that fuzzy approaches are more suitable than IFP for generating optimal solutions. The key advantage of neutrosophic sets in this study lies in their ability to represent complex uncertainties more effectively than traditional fuzzy sets, particularly in logistics systems with fluctuating demand. The integration of carbon offset mechanisms ensures that transportation planning aligns with sustainability goals, allowing decision-makers to balance economic objectives with environmental responsibilities. The novelty of this work is in combining type-2 neutrosophic uncertainty with carbon offset policies and product preservation strategies within a multi-objective framework, providing actionable insights for sustainable conscious logistics planning.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1007/s00500-026-11398-5first seen 2026-08-05 04:56:34
🔔 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。