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arXiv · 2608.17416

Bi-Layer Ant Colony Optimization for Multi-Robot Task Allocation and Routing in Delivery Applications

Le Na Nguyen·Thanh Long Nguyen·Thanh Thao Ton Nu·Quan Le·Manh Duong Phung
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Abstract

This paper addresses the multi-robot task allocation (MRTA) problem, which is essential for delivery and logistics applications. Our approach first defines a new cost function that transforms the MRTA into a unified optimization problem capturing both task assignment and routing. A bi-layer ant colony optimization (ACO) algorithm is then introduced, integrating two interdependent decision layers within a single colony process to solve the problem. This hierarchical framework enables simultaneous optimization of task allocation and route planning across multiple robots. Comparative experiments with mixed-integer linear programming (MILP) and particle swarm optimization (PSO) demonstrate that the proposed bi-layer ACO achieves the shortest total travel distance and fastest completion time across all task sizes. Specifically, it reduces total travel distance by up to 17.7% and completion time by nearly 20% compared with baseline methods. These results confirm the efficiency, scalability, and reliability of the proposed bi-layer ACO for multi-robot delivery tasks.

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Le Na Nguyen, Thanh Long Nguyen, Thanh Thao Ton Nu, Quan Le, Manh Duong Phung. 2026-08-18. Bi-Layer Ant Colony Optimization for Multi-Robot Task Allocation and Routing in Delivery Applications. https://doi.org/10.1145/3805862.3805901

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