Evaluating City Logistics Measure in E-Commerce with Multiagent Systems

Abstract

This paper presents a multi-agent systems (MAS) model to evaluate City Logistics measure for an urban road network in an e-commerce delivery system environment. Most notable contribution of this evaluation methodology is the combination of vehicle routing and scheduling problem with time window (VRPTW), auction theory and reinforcement learning in a multi-agent framework. This approach seeks to represent the behaviour of each stakeholder involved in the delivery of goods between producers and customers. The preliminary results of the model shows that Government-driven City Logistics measures such as freight vehicle road pricing has the potential of reducing truck emission when the administrator learns and price the road links.