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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 introduce 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 machines in a factory to best satisfy (potentially changing) product demands over time. Our system models the set of jobs queued in front of any given machine as a wasp nest, wherein wasp-like agents interact to form a social hierarchy and prioritize the jobs that they represent. Other wasp-like agents external to the nest act as overall machine proxies, and use a model of wasp task allocation behavior to determine which new jobs should be accepted into the machine’s queue. We show for simple factories that our multi-agent system achieves the desired effect. For a given job mix, the system converges to a factory configuration that maximizes overall performance, and as the job mix changes, the system quickly adapts to a new, more appropriate configuration.