On the Integration of Agent-Based and Mathematical Optimization Techniques

Abstract

The strengths and weaknesses of agent-based approaches and classical optimization techniques are compared. Their appropriateness for resource allocation problems were resources are distributed and demand is changing is evaluated. We conclude that their properties are complementary and that it seems beneficial to combine the approaches. Some suggestions of such hybrid systems are sketched and two of these are implemented and evaluated in a case study and compared to pure agent and optimization-based solutions. The case study concerns allocation of production and transportation resources in a supply chain. In one of the hybrid systems, optimization techniques were embedded in the agents to improve their decision making capability. In the other system, optimization was used for creating a long-term coarse plan which served as input the agents that adapted it dynamically. The results from the case study indicate that it is possible to capitalize both on the ability of agents to dynamically adapt to changes and on the ability of optimization techniques for finding high quality solutions.