Wasp-like Agents for Distributed Factory Coordination
Autonomous Agents and Multi-Agent Systems
Ant colony intelligence in multi-agent dynamic manufacturing scheduling
Engineering Applications of Artificial Intelligence
Using a local discovery ant algorithm for Bayesian network structure learning
IEEE Transactions on Evolutionary Computation
Information and Software Technology
Pheromone-based coordination for manufacturing system control
Journal of Intelligent Manufacturing
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In this paper, we introduce a new approach to autonomous decentralized shop floor routing. Our system, which we call Ant Colony Control (AC 2), applies the analogy of a colony of ants foraging for food to the problem of dynamic shop floor routing. In this system, artificial ants use only indirect communication to make all shop routing decisions by altering and reacting to their dynamically changing common environment through the use of simulated pheromone trails. For simple factory layouts, we show that the emergent behavior of the colony is comparable to using the optimal routing strategy. Furthermore, as the complexity of the factory layout is increased, we show that the adaptive behavior of AC 2 evolves local decision making policies that lead to near-optimal solutions from the standpoint of global performance.