Agent-based adaptive production scheduling - a study of cooperative-competition in federated agent architecture
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Date
2009
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Springer-Verlag, Tokyo
Abstract
An increasingly popular method of improving the performance of
complex systems operating in dynamic environments involves modeling such
systems as social networks made up of a community of agents working together
based on some basic principles of social interaction. However, this paradigm is
not without its challenges brought about by the need for autonomy of agents in
the system. While some problems can be solved by making the interaction
protocol either strictly competitive or strictly cooperative, some other models
require the system to incorporate both interaction schemes for improved
performance. In this paper, we study how the seemingly contradictory effects of
these two behaviours can be exploited for distributed problem solving by
considering a flexible job shop scheduling problem in a dynamic order
environment. The system is modeled using federated agent architecture. We
implement a simple auction mechanism at each processing center and a global
reinforcement learning mechanism to minimize cost contents in the system.
Results of simulations using the cooperative-competition approach and the
strictly competitive model are presented. Simulation results show that there
were improvements in cost objectives of the system when the various
processing centers cooperated through the learning mechanism, which also
provides for adaptation of the system to a stream of random orders.
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Keywords
Agent-Based Adaptive Production Planning, Cooperative-Competition, Federated Agent Architecture