Wasp-like Agents for Distributed Factory Coordination

Abstract

Abstract. 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.

The effect of load on agent-based algorithms for distributed task allocation

“…Note that our hybrid algorithm shares similarities with the threshold-based R-WASP algorithm studied by Cicirello and Smith [6,7], Nouyan et al [24], and to the market-based algorithm with a "reserve price" studied by Kalra and Martinoli [17]. All three algorithms have the ability to select mail (similar to threshold-based algorithms) and to assign mail to the individual who is best suited to it (similar to the market-based algorithms).…”

“…Hence, robustness to high load levels is of paramount importance when selecting solution methods for a given problem. Partial results on the effect of load are given by Cicirello and Smith [7]. They investigate performance under both high and low "set up times", effectively time penalties paid by an agent upon violating a constraint.…”

An agent-based model to support negotiation-based order acceptance in a multi-plant enterprise

“…In AB modeling the focus is on individuals and their interrelationships with other individuals and entities (Cicirello and Smith, 2004;Jennings et al, 1998) This characteristic of AB approach is especially aligned with the aim of this research to model the individual buyer and seller along with the rules based on which they make decisions (e.g. utility maximization, opinion formation) and consequently give rise to the overall performance of the system.…”

Comparative Study of Market-Based and Threshold-Based Task Allocation

“…al. [7] and Cicirello and Smith [8] compare the two on the problem of allocating trucks to paint booths; however, they investigate the benefits of encoding the problem as a marketbased approach or a threshold-based approach, but not the costs and benefits of centralization and communication. We believe that no prior work compares these approaches along the dimensions in which we are interested.…”