Abstract
Agent based simulation has successfully been applied to model complex organizational behavior and to improve or optimize aspects of organizational performance. Agents, with intelligence supported through the application of a genetic algorithm are proposed as a means of optimizing the performance of the system being modeled. Local decisions made by agents and other system variables are placed in the genetic encoding. This allows local agents to positively impact high level system performance. A simple, but non trivial, peg game is utilized to introduce the concept. A multiple objective bin packing problem is then solved to demonstrate the potential of the approach in meeting a number of high level goals. The methodology allows not only for a systems level optimization, but also provides data which can be analyzed to determine what constitutes effective agent behavior.
Highlights
Over the past ten years, there has been an increasing interest in agent based modeling and simulation
It is clear from the wealth of research activity in the application of agent based modeling and simulation that the approach offers a valid means for addressing complex, real world problems
The contribution of this work is in the demonstration of how a combination of evolutionary optimization methods and agent based simulation can solve decision support problems
Summary
Over the past ten years, there has been an increasing interest in agent based modeling and simulation. Specific instances of the utilization of agent based simulation in conjunction with various optimization approaches have been presented by Deshpande [8] and Gjerdrum et al [9] for manufacturing scheduling, Sirikipanichkul [10] for freight hub location, Neagu [11] for transport logistics and by Botterud [12] for expansion in electricity markets It is clear from the wealth of research activity in the application of agent based modeling and simulation that the approach offers a valid means for addressing complex, real world problems. The ability to share the intelligence between an agent’s knowledge base and an optimization algorithm helps to insure that all goals are factored into the solution and the entire solution space is investigated The results from such an optimization in practice can help improve the behavior of a local agent over time and some of the intelligence supplied through the optimization can be incorporated into the logic employed by the agent. The contribution of this work is in the demonstration of how a combination of evolutionary optimization methods and agent based simulation can solve decision support problems
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