Coordination of Multiple Agents in Distributed Manufacturing Scheduling

Distribiit,ed Problem Solving (DPS) is concerned with a group of agents, each with only local views, that cooperate and coordinate to solve a given problem in a coherent and efficient manner. As networked computer systems and practical concurrcnt processing architectures develop, DPS research has seen increasing realization in real world problems. As opposed to using sophisticated agents in problem solving, DPS approaches based on simple reactive agents have received growing interest. These reactive agents are simple in the sense that they do not have representations of bheir environments and they act by stimulus and response. They are embedded in the environment and follow simple pat.terns of behavior that can be easily programmed. Generating sophisticated group behavior from coordination and integration of activit,ies of simple individuals has potentially great significance. This thesis presents DPS approaches based on reactive agents, and applied to sc,heduling in t h e domain of manufacturing job shops. In general, scheduling deals with organiz.ing possibly related activities over time and limited resources. Scheduling problems occur in many domains, such as manufacturing, transportation, computer processing, coInmunicat,ion, services, health care, education, etc. Distributed scheduling typically is concerned with coordination among resources over processing of relat,ed act,ivities in environments where knowledge and control distribution are desired. The objective of this work is t,he problem solving efficacy of reactive agents in terms of computat~ional cost and solution quality. Our thesis is that multi-agent coordination kchniques can provide substantial gains in terms of problem solving efficiency and solution quality. The main contributions of this research are t,he coordination schemes and the DPS techniques implemented in the two effective multiagent scheduling systems: CORA and COFCAST. In addition, CORA and COFCAST are shown to provide equivalent ur superior performance to competing centralized scheduling techniques, and t,lierefore. enrich viable approaches t,o scheduling problems. In a broader view, this work supplements DPS research with innovative approaches to tightly coupled problems a.nd successful demonstration of competitive performance. This work also provides an essential component for larger-scaled DPS research in manufacturing nlanagemcnt and c.ontro1.