Scholarlink

<p>Over time, catastrophes have increasingly caused significant material and human losses. Effective logistics management in humanitarian aid is crucial to minimizing these impacts. Infrastructure damage from disasters introduces uncertainties that must be considered when routing trucks for relief item delivery. This study proposes a Mixed Integer Programming model for the Two-Echelon Vehicle Routing Problem in Humanitarian Aid Logistics (2E-VRP-HAL) to minimize total travel time. An earthquake scenario in Kartal, Istanbul is used to demonstrate the model's accuracy and applicability while accounting for road closures. A diverse fleet, including trucks and pedestrians, addresses delivery challenges, with handover stations enabling access to unreachable areas. To address larger problem instances, a set partitioning approach is used to cluster demand points, followed by a MIP-based local search heuristic to refine the results. Numerical analysis shows up to 15.83% improvement in medium-sized instances and feasible results for larger cases where the model struggles. These findings highlight the potential of proposed decision support methods.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Improving last-mile delivery in humanitarian logistics by solving a two-echelon routing problem with portering and infrastructure disruptions

  • 期刊论文

  • 发表日期：

    2026年1月19日

查看全文
  • İsmail Nurullah Mutlu,
  • Ergül Kısa Toğrul,
  • H. Cansın Uzgören Kazanç,
  • Kaan Kılıç,
  • Mehmet Soysal

摘要

Over time, catastrophes have increasingly caused significant material and human losses. Effective logistics management in humanitarian aid is crucial to minimizing these impacts. Infrastructure damage from disasters introduces uncertainties that must be considered when routing trucks for relief item delivery. This study proposes a Mixed Integer Programming model for the Two-Echelon Vehicle Routing Problem in Humanitarian Aid Logistics (2E-VRP-HAL) to minimize total travel time. An earthquake scenario in Kartal, Istanbul is used to demonstrate the model's accuracy and applicability while accounting for road closures. A diverse fleet, including trucks and pedestrians, addresses delivery challenges, with handover stations enabling access to unreachable areas. To address larger problem instances, a set partitioning approach is used to cluster demand points, followed by a MIP-based local search heuristic to refine the results. Numerical analysis shows up to 15.83% improvement in medium-sized instances and feasible results for larger cases where the model struggles. These findings highlight the potential of proposed decision support methods.