Fair and efficient multi-agent routing for cooperative and autonomous agricultural fleets with implements

Abstract

The growing use of autonomous tractor fleets with detachable implements presents complex logistical challenges in agriculture. Current systems often rely on simple heuristics and avoid implement swapping, limiting efficiency. A central challenge is to dynamically coordinate vehicle routing and implement exchanges to enable efficient, low-intervention task execution. Due to high costs, such fleets are owned mainly by large enterprises or cooperatives, where fair task allocation and profit sharing are critical. Addressing both coordination and fairness, in this paper, we introduce the Agricultural Fleet Vehicle Routing Problem with Implements (AFVRPI). We propose a distributed model derived from a centralized formulation also presented in this paper. This model is embedded within a Distributed Multi-Agent System Architecture (DIMASA), where autonomous vehicle agents manage routing and implement use under limited fuel autonomy, while implement agents ensure compatibility and sufficient capacity to meet task demands. Our solution applies systematic egalitarian social welfare optimization to iteratively maximize the profit of the worst-off vehicle, balancing fairness with system efficiency. To enhance scalability, we use column generation in the distributed model, achieving solution quality comparable to the centralized model while significantly reducing computing time. Simulation results on new benchmark instances demonstrate that our distributed multi-agent AFVRPI approach is scalable, efficient, and fair.