Optimal Decision Making in Agent-based Autonomous Groupage Traffic

The dynamics and complexity of planning and scheduling processes in groupage traffic require efficient, proactive, and reactive system behavior to improve the service quality while ensuring time and cost efficient transportation. We implemented a multiagent-system to emerge an adequate system behavior and focus on the decision making processes of agents that is based on the Traveling Salesman Problem (TSP) with aspects like contract time windows, individual restricted capacities of trucks, premium services and varying priorities of dynamically incoming orders. We present an optimal depth-first branch-and-bound asymmetric TSP solver with constraints on tour feasibility and depot reachability at any step of the process. To evaluate our approach, we use established benchmarks as well as its inclusion in a real-life multiagent-based simulation. Simulated scenarios are based on real customer orders of our industrial partner Hellmann Worldwide Logistics GmbH & Co. KG and are applied on real world infrastructures. The results reveal that efficient optimal decision making in multiagent systems increases the service quality and meets the requirements and challenges in logistics.