Solving dynamic full truckload vehicle routing problem using an agent-based approach

In today's complex and dynamic transportation networks due to increasing energy costs and adverse environmental impact, it is crucial to transport goods or raw materials across a network in such a way that all vehicle assignment and routing decisions are taken to minimize all related costs. Vehicle routing problems under dynamic and stochastic conditions are known to be very challenging in both mathematical modeling and computational complexity. In this study, a special variant of the full truck load vehicle assignment and routing problem is investigated. For this purpose, first, a detailed analysis of the processes in a liquid transportation logistics firm with a large fleet of tanker trucks is conducted. Then, a new original problem with distinctive features as compared to the similiar studies in the literature, which includes loading/loading time windows, nodes with different fuctions(loading/unloading, washing facility, parking) heterogen trucks, multiple trips, multiple trailer types, multiple load types and setup times between changing load types is formulated. An intelligent multi-agent based approach is selected to solve this dynamic optimization problem along with the development of appropriate agent designs, vehicle assignment and routing algorithms. In order to assess the performance of the proposed approach under varying environmental conditions (e.g., system congestion ratio, ratio of orders with multiple trips) and different algorithmic parameter levels (e.g., latest response time to orders, whether swap option is active or inactive), a detailed scenario analyisi is conducted based on a set of designed simulation experiments. As a result of these simulation runs, it is found out that the developed algorithms are able to provide good and efficient solutions responding to dynamic conditions. Furthermore, using longer latest response times and activating the swap mechanism appear to have a very significant positive impact on the relavent costs, profitability, ratios of loaded trips over the total distance travelled, and the acceptance ratios of customer demand.