低炭素都市物流に向けた航空中継輸送と地上配送の協調
Coordinating Aerial Transshipment with Ground Delivery for Low-Carbon Urban Logistics (原題)
Shukang Zheng, Changjiang Zheng, Genhua Ma, Hanpei Yang, Ye Lu, Lin Ge, Yingjia Chen
🤖 gxceed AI 要約
日本語
UAVとUGVを組み合わせた二階層協調配送モデルを構築し、運用コスト・配送時間・炭素排出コストを統合した単一目的最適化を提案する。深層強化学習(DQN)でALNSの破壊・修復オペレータ選択を強化するDQN–ALNSを開発。南京南駅周辺の実データシナリオで交通時間帯別に評価し、オフピーク時に統合目的値3042.543元・配送完了1.6時間と最小となることを示した。
English
Proposes a two-echelon UAV–UGV collaborative delivery model with intermediate transshipment, jointly optimizing customer allocation, UAV missions, and UGV routing under a weighted single objective combining cost, delivery time, and carbon-emission cost. A DQN-enhanced ALNS (DQN–ALNS) selects destroy–repair operators via prioritized experience replay. Tests on nine Solomon-based instances and a Nanjing South Railway Station scenario show competitive, instance-dependent gains, with the off-peak case yielding the lowest objective (RMB 3042.543) and 1.6 h completion.
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
都市ラストマイル配送の脱炭素化は国内でも物流2024年問題とGXの交差点として重要。電動配送・ドローン活用の炭素コスト内部化手法は、国内物流企業のScope 3削減や省エネ投資判断に示唆を与える。
In the global GX context
Adds to the growing literature on decarbonizing last-mile logistics by internalizing carbon-emission cost into routing optimization. Relevant to global corporate Scope 3 logistics accounting and to disclosure of transport emissions under CSRD/ISSB, though the AI component is operational rather than ESG-evaluation focused.
👥 読者別の含意
🔬研究者:UAV–UGV協調配送に炭素コストを組み込む最適化とDQN強化ALNSの設計・評価手法を参考にできる。
🏢実務担当者:ラストマイル配送の炭素コストを経路計画に内部化する際のモデル化と時間帯別シナリオ比較の枠組みが活用できる。
🏛政策担当者:都市物流の低炭素化に向け、炭素排出コストの内部化やドローン・地上配送の協調を促す制度設計の参考になる。
📄 Abstract（原文）
Growing urban last-mile delivery demand and low-carbon transition pressures motivate the coordinated use of unmanned aerial vehicles (UAVs) and unmanned ground vehicles (UGVs). This study develops a two-echelon collaborative delivery model with intermediate transshipment, jointly considering customer allocation, UAV transshipment missions, and UGV routing. A weighted single-objective formulation integrates operational cost, monetized aggregate delivery time, and carbon-emission cost under capacity, UAV-range, and time-window constraints. A learning-enhanced adaptive large neighborhood search method, termed DQN–ALNS, is proposed. A standard deep Q-network with prioritized experience replay selects destroy–repair operator combinations according to the current search state, while ALNS executes these combinations to improve candidate solutions. Experiments cover nine Solomon-based instances with 25, 50, and 100 customers, with ten independent runs per method and instance. DQN–ALNS achieves lower mean objective values than standalone ALNS on six instances, including five of the six medium- and large-scale instances, indicating competitive but instance-dependent performance. Ablation results suggest potential benefits from prioritized experience replay in solution quality and run-to-run stability. A real-data-based scenario study around Nanjing South Railway Station examines different traffic periods. The off-peak scenario yields the lowest integrated objective value of RMB 3042.543 and the shortest delivery completion time of 1.6 h. The proposed framework supports offline route planning and scenario comparison, with DQN–ALNS positioned as a competitive learning-enhanced variant of ALNS.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su182010513first seen 2026-10-17 04:44:46
🔔 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。