Optimization method of low-carbon logistics network for automotive-parts based on IPSO
IPSOに基づく自動車部品の低炭素物流ネットワーク最適化手法 (AI 翻訳)
Chen Chunxia, Zhihua Yang
🤖 gxceed AI 要約
日本語
自動車部品の低炭素物流ネットワークを最適化するため、改良粒子群アルゴリズム（IPSO）を提案。輸送距離、積載量、輸送手段を考慮した多目的最適化モデルを構築し、従来のネットワークより低コスト・低時間・低炭素排出を実現した。
English
This paper proposes a low-carbon logistics network optimization method for automotive parts using an improved particle swarm algorithm (IPSO). A multi-objective optimization model considering distance, load, and transport modes achieves lower costs, delivery times, and carbon emissions compared to traditional networks.
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
日本の自動車産業はサプライチェーン全体でのカーボンフットプリント削減が課題であり、本手法は物流最適化を通じたScope 3排出削減に応用可能。
In the global GX context
For global automotive supply chains, this optimization method offers a practical way to reduce logistics-related carbon emissions, aligning with Scope 3 reduction targets and sustainability reporting frameworks like CDP and ISSB.
👥 読者別の含意
🔬研究者:The IPSO algorithm with grouping-control and mutation-crossover strategies provides a novel approach for multi-objective logistics optimization.
🏢実務担当者:Automotive parts logistics planners can adopt this model to simultaneously cut costs, delivery times, and carbon emissions.
📄 Abstract（原文）
We propose a low-carbon automotive-component logistics network optimization method based on the improved particle swarm algorithm (IPSO) to reduce delivery times and operational costs while lowering carbon footprints. Accounting for key variables such as transportation distance, load capacity and transport modes, a multi-objective optimization model of the cost-optimal green automotive-parts logistics network is established and grouping-control evolution strategy, opposition-based search and mutation-crossover strategies are introduced in particle swarm algorithm (PSO) to obtain a global optimal solution. Experimental results show that the proposed low-carbon logistics optimization network achieves lower costs, times and carbon emissions compared to the traditional networks.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1080/17445760.2026.2684419first seen 2026-06-18 04:55:32
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