Deep Learning-Enhanced Proactive Strategy: LSTM and VRP/ACO for Autonomous Replenishment and Demand Forecasting in Shared Logistics
深層学習によるプロアクティブ戦略：共有物流における自律補充と需要予測のためのLSTMおよびVRP/ACO (AI 翻訳)
Martin Straka, K. Kleinová
🤖 gxceed AI 要約
日本語
共有物流の自律補充システムを提案。LSTMによる需要予測、VRP/ACOによる動的経路最適化、異常検知を統合し、シミュレーションで在庫枯渇リスク25-30%減、補充距離25%減を実証。運用コストとCO2排出削減の可能性を示す。
English
This paper proposes a three-layer autonomous replenishment system for shared logistics using LSTM demand forecasting, VRP/ACO route optimization, and anomaly detection. Simulations show 95% inventory prediction accuracy (MAPE=4.02%), reducing stock-out risks by 25–30% and replenishment distance by 25%, thereby cutting operational costs and CO2 emissions.
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
国内物流業界は人手不足と脱炭素圧力に直面しており、AIを活用した共有物流モデルは中小企業の効率化と排出削減に寄与し得る。SSBJ対応を含むサプライチェーン排出削減の一助としても注目に値する。
In the global GX context
Contributes to global green logistics research by demonstrating how AI-driven operational optimization can yield measurable sustainability outcomes, relevant to companies facing CSRD or SEC climate disclosure requirements that expect quantitative emission reduction evidence.
👥 読者別の含意
🔬研究者:LSTMとVRP/ACOの統合による自律補充システム設計とその評価方法が参考になる。
🏢実務担当者:物流・小売企業が在庫管理と配送ルート最適化を通じたコスト・CO2削減を検討する際の実装例として有用。
🏛政策担当者:共有物流とAI活用を促進する政策に裏付けとなる効果指標を提供する。
📄 Abstract（原文）
At present, the global logistics sector faces critical challenges, including rising energy costs and pressure to reduce CO2 emissions. Traditional linear supply chains are becoming inefficient, necessitating a transition toward shared logistics based on the principles of the sharing economy. This paper presents a progressive three-layer architecture that transforms conventional reactive data collection into an autonomous, proactive management system for the distribution of consumable materials. While previous research established foundations in IoT connectivity for smart vending machines, this study advances the process by integrating an intelligent layer of artificial intelligence (AI) algorithms. The framework utilizes Long Short-Term Memory (LSTM) neural networks for demand forecasting, dynamic route optimization (VRP/ACO) for replenishment, and Isolation Forest/DBSCAN algorithms for real-time anomaly detection. To evaluate the framework, a numerical simulation was conducted using representative pilot scenarios. The results indicate that within the simulated environment, the system achieves over 95% accuracy in inventory depletion prediction (MAPE = 4.02%). In these analyzed instances, this leads to a 25–30% reduction in stock-out risks and a 25% reduction in replenishment distance. These findings demonstrate the significant potential for reducing operational costs and carbon footprints in green logistics. The study confirms that the synergy between IoT infrastructure and AI-driven analysis provides a robust foundation for transitioning from static methodologies to resilient, collaborative logistics ecosystems.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/app16062838first seen 2026-08-02 17:44:31
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