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The Last Mile Delivery (LMD) problem, involving the delivery of goods from a hub to the final destination, is a critical challenge in logistics, especially with the rise of e-commerce. Optimizing LMD is essential for reducing costs and improving delivery speed. The Travelling Salesman Problem with Drones (TSP-D) has gained popularity as a solution, but optimizing UAV routing in urban settings presents challenges such as payload limits, battery life, and airspace restrictions. This research introduces the Pity Beetle Algorithm (PBA), a novel metaheuristic inspired by the Pity Beetle's natural search behavior, to address the TSP-D. PBA is applied by encoding delivery routes as potential solutions and refining these routes iteratively. The algorithm's ability to escape local optima makes it effective for complex optimization problems. The results show that PBA significantly reduces delivery time, achieving competitive average solutions. These results highlight PBA's potential as a robust method for optimizing UAV routing in last-mile delivery scenarios.

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Optimizing the TSP-D with Pity Beetle Algorithm to Minimize the Delivery Completion Time for UAV Routing Problems

  • 会议论文

  • 发表日期：

    2025年

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  • Yugeswary Kanesen,
  • Hasneeza Liza Zakaria,
  • M. S. Asi,
  • Rozmie Razif Othman,
  • Wan Nur Suryani Firuz Wan Ariffin,
  • Nuraminah Ramli,
  • Mohd Alif Hasmani Abd Ghani

摘要

The Last Mile Delivery (LMD) problem, involving the delivery of goods from a hub to the final destination, is a critical challenge in logistics, especially with the rise of e-commerce. Optimizing LMD is essential for reducing costs and improving delivery speed. The Travelling Salesman Problem with Drones (TSP-D) has gained popularity as a solution, but optimizing UAV routing in urban settings presents challenges such as payload limits, battery life, and airspace restrictions. This research introduces the Pity Beetle Algorithm (PBA), a novel metaheuristic inspired by the Pity Beetle's natural search behavior, to address the TSP-D. PBA is applied by encoding delivery routes as potential solutions and refining these routes iteratively. The algorithm's ability to escape local optima makes it effective for complex optimization problems. The results show that PBA significantly reduces delivery time, achieving competitive average solutions. These results highlight PBA's potential as a robust method for optimizing UAV routing in last-mile delivery scenarios.