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

• URL: http://arxiv.org/abs/2608.17416v1
• Date: Tue, 18 Aug 2026 06:36:20 GMT
• Title: Bi-Layer Ant Colony Optimization for Multi-Robot Task Allocation and Routing in Delivery Applications
• Abstract summary: This paper defines a new cost function that transforms the multi-robot task allocation (MRTA) problem into a unified optimization problem.<n>A bi-layer ant colony optimization (ACO) algorithm is then introduced, integrating two decision layers within a single colony process to solve the problem.<n> 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.
• Score: 1.7650642097771525
• License: http://creativecommons.org/licenses/by/4.0/
• 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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