Optimizing Drone-Based Delivery Using the Pity Beetle Algorithm A Novel Approach.

Abstract

The Travelling Salesman Problem with Drones (TSP-D) has emerged as a significant optimization challenge in last-mile delivery systems. Addressing scalability issues with datasets exceeding 250 nodes, this study introduces the Pity Beetle Algorithm (PBA), a novel metaheuristic. The PBA demonstrates superior performance in balancing exploration and exploitation to optimize delivery routes effectively. Results from new simulations conducted for this journal show delivery time reductions in a broader range of scenarios, with improvements up to 60% over standard benchmarks. Statistical analyses confirm the algorithm’s capability to enhance computational efficiency and scalability. Additionally, the PBA’s dynamic tuning mechanisms enable it to adapt effectively to varying dataset sizes and configurations. Beyond its computational benefits, this study underscores the real-world applicability of the PBA in logistics, providing industries with a robust tool to optimize delivery times and reduce operational costs. This research opens avenues for integrating PBA with hybrid models and real-time optimization techniques, further enhancing its potential to tackle complex logistical challenges.