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The Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP) has new approach to deliver the good to customer in faster way. Addressing challenges like limited battery life, payload constraints, and navigation, the problem is solved using the Artificial Bee Colony algorithm (ABC). This swarm-based optimization method delivers efficient, near-optimal solutions faster than traditional Mixed Integer Linear Programming (MILP) approaches solved using Gurobi, demonstrating its potential for scalability and practicality under computational time constraints. ABC and traditional MILP were coded in Python.

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Efficient Parallel Drone Scheduling Traveling Salesman Problem Solver Using Artificial Bee Colony Algorithm with Local Optimization (2-opt)

  • 会议论文

  • 发表日期：

    2026年

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  • Prasad Dattaram Salunke,
  • T. G. Pradeepmon

摘要

The Parallel Drone Scheduling Traveling Salesman Problem (PDSTSP) has new approach to deliver the good to customer in faster way. Addressing challenges like limited battery life, payload constraints, and navigation, the problem is solved using the Artificial Bee Colony algorithm (ABC). This swarm-based optimization method delivers efficient, near-optimal solutions faster than traditional Mixed Integer Linear Programming (MILP) approaches solved using Gurobi, demonstrating its potential for scalability and practicality under computational time constraints. ABC and traditional MILP were coded in Python.