Multi-Factor Task Assignment and Adaptive Window Enhanced Conflict-Based Search: Multi-Agent Task Assignment and Path Planning for a Smart Factory

Abstract

Multi-Agent Systems (MAS) are widely deployed in smart factory environments, where efficient task assignment and path planning for agents can greatly enhance production efficiency. Existing algorithms usually ignore resource constraints, overly simplify the geometric shape of agents, and perform poorly in large-scale scenarios. In this paper, we propose a Multi-Factor Task Assignment and Adaptive Window Enhanced Conflict-Based Search (MTA-AWECBS) algorithm to solve these problems, which considers the resource constraints and volume of agents, improving the algorithm’s scalability and adaptability. In task assignment, a novel scheme is designed by considering distance cost, maximum travel distances, and maximum number of executable tasks. In path planning, we first propose a new mathematical description of global traffic congestion level. Based on this, an adaptive window is proposed to dynamically adjust the time horizon in the WECBS algorithm, improving search efficiency and solving the deadlock issue. Additionally, based on experimental observations, two optimization strategies are proposed to further improve operation efficiency. The experimental results show that MTA-AWECBS outperforms Token Passing (TP), Token Passing with Task Swaps (TPTSs), and Conflict-Based Steiner Search (CBSS) in handling a large number of tasks and agents, achieving an average $39\%$ reduction in timestep cost and an average $22\%$ reduction in total path cost.