An emission-capacitated vehicle routing model for sustainable urban waste collection using hybrid guided local search
ハイブリッド誘導局所探索を用いた持続可能な都市廃棄物収集のための排出容量制約付き車両ルーティングモデル (AI 翻訳)
Qazi Salman Khalid, Shahid Maqsood, Jabir Mumtaz, S. M. Qureshi
🤖 gxceed AI 要約
日本語
本論文は、都市廃棄物収集における排出量を明示的に考慮した車両ルーティング問題（E-CVRPTW）を提案する。ハイブリッド誘導局所探索により、燃料消費とCO2排出を9-11%削減しつつ、コストも8-9%削減する。実際のケーススタディで有効性を確認し、炭素予算等の政策制約にも対応可能。
English
This paper introduces an emission-capacitated vehicle routing problem (E-CVRPTW) for urban waste collection, explicitly modeling fuel consumption and emissions. A hybrid guided local search algorithm reduces fuel consumption and CO2 emissions by 9-11% while cutting costs by 8-9% in a real-world case study. The model incorporates policy constraints like carbon budgets, offering practical insights for sustainable logistics.
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
本論文は、ルーティング最適化を排出削減目標に直接結びつけるモデルを提供する。日本の自治体や物流企業が、グリーン成長戦略などのGX目標に整合するために有用である。炭素予算や排出強度上限の明示的な組み込みは、日本でも増えつつある政策枠組みを反映している。
In the global GX context
This paper contributes to the global GX context by providing a practical optimization tool for reducing logistics emissions, which is a significant source of Scope 1 emissions for many companies. The E-CVRPTW framework aligns with the growing emphasis on emission reduction targets in logistics, supporting compliance with frameworks like the Science Based Targets initiative (SBTi) and carbon pricing mechanisms.
👥 読者別の含意
🔬研究者:Provides a new optimization model (E-CVRPTW) and algorithm for researchers interested in sustainable logistics and emission-aware routing.
🏢実務担当者:Useful for waste management companies and logistics providers to design routes that meet emission targets while minimizing costs.
🏛政策担当者:Demonstrates how emission constraints (carbon budget, intensity ceiling) can be integrated into urban logistics planning, informing policy design.
📄 Abstract（原文）
Urban logistics services, such as municipal solid waste collection, play a crucial role in shaping cities’ sustainability. These services are significant contributors to fuel consumption, operational costs, and greenhouse gas emissions. Traditional vehicle routing models, such as the capacitated vehicle routing problem with time windows, typically focus on minimizing distance or cost, which indirectly impacts emissions. However, these models fail to address the growing need for sustainable and environmentally conscious logistics strategies. This study introduces the emission-capacitated vehicle routing problem with time windows (E-CVRPTW), a novel optimization formulation that explicitly integrates a load-dependent fuel consumption model and an emission objective. The formulation also incorporates fleet-level policy constraints, including a carbon budget and an emission-intensity ceiling, providing a more comprehensive approach to minimizing both operational costs and environmental impacts. To solve the E-CVRPTW, a hybrid guided local search (HGLS) approach is employed with additional embedded features: (i) a novel cheapest insertion first initialization to generate high-quality starting solutions; (ii) adaptive feature penalties to diversify the search, while controlled neighborhood switching between 2-opt and 3-opt moves ensures an optimal balance between intensification and diversification. These features help the proposed algorithm to achieve better optimization solutions. Moreover, a rigorous experimental protocol using the Solomon and Gehring-Homberger benchmark instances demonstrates that HGLS, with additional features, significantly improves fuel consumption and emission reductions compared to baseline heuristics. Furthermore, a real-world case study on municipal waste collection reveals that optimized routing plans reduce fuel consumption and CO2 emissions by 9–11% while lowering total costs by 8–9%. The optimized solutions also meet strict policy targets under constrained conditions, showcasing the potential of E-CVRPTW in real-world applications. A sensitivity analysis explores the trade-offs among fuel prices, carbon prices, and emission weights, providing valuable insights for decision-makers in urban service planning and sustainability-focused policy formulation.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1038/s41598-026-38829-5first seen 2026-05-15 17:31:40
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