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Agent-based approaches to manufacturing scheduling and control have gained increasing attention in recent years. Such approaches are attractive because they offer increased robustness against the unpredictability of factory operations. But the specification of local coordination policies that give rise to efficient global performance and effectively adapt to changing circumstances remains an interesting challenge. In this paper, we present a new approach to this coordination problem, drawing on various aspects of a computational model of how wasp colonies coordinate individual activities and allocate tasks to meet the collective needs of the nest.
We focus specifically on the problem of configuring parallel multi-purpose machines in a factory to best satisfy product demands over time. Wasp-like computational agents that we call routing wasps act as overall machine proxies. These agents use a model of wasp task allocation behavior, coupled with a model of wasp dominance hierarchy formation, to determine which new jobs should be accepted into the machine’s queue. If you view our system from a market-oriented perspective, the policies that the routing wasps independently adapt for their respective machines can be likened to policies for deciding when to bid and when not to bid for arriving jobs.
We benchmark the performance of our system on the real-world problem of assigning trucks to paint booths in a simulated vehicle paintshop. The objective of this problem is to minimize the number of paint color changes accrued by the system, assuming no a priori knowledge of the color sequence or color distribution of trucks arriving in the system. We demonstrate that our system outperforms the bidding mechanism originally implemented for the problem as well as another related adaptive bidding mechanism.