Blekinge Institute of Technology, School of Engineering, Department of Systems and Software Engineering
An agent-based tool for micro-level simulation of transport chains (TAPAS) is described. It is more powerful than traditional approaches as it is able to capture the interactions between individual actors of a transport chain, as well as their heterogeneity and decision making processes. Whereas traditional approaches rely on assumed statistical correlation between different parameters, TAPAS relies on causality, i.e., the decisions and negotiations that lead to the transports being performed. An additional advantage is that TAPAS is able to capture time aspects, such as, the influence of timetables, arrival times, and time-differentiated taxes and fees. TAPAS is composed of two layers, one layer simulating the physical activities taking place in the transport chain, e.g., production, storage, and transports of goods, and another layer simulating the different actors decision making processes and interaction. The decision layer is implemented as a multi-agent system using the JADE platform, where each agent corresponds to a particular actor. We demonstrate the use of TAPAS by investigating how the actors in a transport chain are expected to act when different types of governmental control policies are applied, such as, fuel taxes, road tolls, and vehicle taxes. By analyzing the costs and environmental effects, TAPAS provides guidance in decision making regarding such control policies. We argue that TAPAS may also complement existing approaches in different ways, for instance by generating input data such as transport demand. Since TAPAS models a larger part of the supply chain, the transport demand is a natural part of the output. Studies may concern operational decisions like choice of consignment size and frequency of deliveries, as well as strategic decisions like where to locate storages, terminals, etc., choice of producer, and adaptation of vehicle fleets.
IFAAMAS , 2008.
Seventh International Joint Conference on Autonomous Agents and Multi-Agent Systems