低炭素転換におけるグリーン倉庫運用経路の省エネ最適化モデルとシミュレーション分析
Energy-saving optimization model and simulation analysis of green warehouse operation path in low-carbon transformation (原題)
Jiafeng Lai
🤖 gxceed AI 要約
日本語
物流倉庫の低炭素化に向け、荷役機器の移動最適化による省エネ経路設計を扱う。総エネルギー消費・炭素排出・作業時間の最小化を目的とする多目的最適化モデルを構築し、機器の起動停止や負荷連成などの動的要素を組み込んだ。NP困難性に対応するため局所探索と適応的パラメータ調整を組み合わせた改良メタヒューリスティックを提案する。
English
This paper addresses low-carbon warehouse operations by optimizing handling-equipment movement paths to cut energy use. It builds a multi-objective model minimizing energy consumption, carbon emissions, and operation time, incorporating dynamic factors like start-stop energy and load coupling. An improved metaheuristic with local search and adaptive parameter tuning is proposed for the NP-hard problem.
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 排出量のうち物流・倉庫は見落とされがちな削減領域であり、SSBJ や有報でのサプライチェーン排出開示を進める日本企業にとって、倉庫運用の省エネ設計は算定根拠として活用余地がある。ただし制度・開示要件との直接接続はなく、実務改善の参考情報としての位置づけが中心。
In the global GX context
Warehousing and logistics are an under-addressed slice of Scope 3, and this optimization framing could feed into emissions-accounting methodologies for downstream logistics under CSRD/ISSB. It offers a modeling template rather than disclosure guidance, so its global contribution is methodological.
👥 読者別の含意
🔬研究者:Provides a multi-objective, dynamic-energy-aware warehouse path optimization formulation with a tailored metaheuristic worth benchmarking against.
🏢実務担当者:Suggests that re-sequencing handling-equipment movements can cut energy, emissions, and time simultaneously in warehouse operations.
📄 Abstract（原文）
Under the global "dual carbon" goals, the low-carbon transformation of the logistics industry has become an inevitable trend. As the core nodes of logistics systems, warehouses face significant energy consumption challenges (particularly from inefficient movement of handling equipment), which have become a critical bottleneck hindering green transformation. Path optimization, as the key approach to energy conservation and carbon reduction in warehousing, can achieve precise control of energy consumption and carbon emissions by reducing redundant equipment movements and improving operational coordination efficiency. Addressing existing research limitations, such as neglecting the dynamic characteristics of equipment energy consumption, insufficient optimization accuracy under multi-constraint coupling, and a lack of dynamic scenario adaptability, this paper focuses on energy-saving optimization of green warehouse operation paths through model construction and simulation analysis. First, we define warehouse operation scenarios and constraints, extract core optimization problems under single-device multi-tasking, multi-device coordination, and dynamic environments, and establish a multi-objective energy-saving optimization model with objectives of minimizing total energy consumption, carbon emissions, and operation time, incorporating dynamic energy factors such as equipment start-stop energy consumption and load coupling. Second, given the NP-hard nature of the model, we design an improved metaheuristic algorithm (combining local search mechanisms and adaptive parameter adjustment strategies) to enhance solution efficiency and global optimization capabilities.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1117/12.3124583first seen 2026-09-24 04:41:45
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