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Manual Multi-Agent Systems for Concurrent Intelligent Design and Manufacturing

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By using our site, you agree to our collection of information through the use of cookies. To learn more, view our Privacy Policy. Log In Sign Up. Intelligent fuzzy decision mechanism in multi-agent systems for flexible manufacturing environment Cemalettin Kubat. Safiye Turgay. Ercan Oztemel. Intelligent fuzzy decision mechanism in multi-agent systems for flexible manufacturing environment. Emails: kubat sakarya. The behavior style of this decision making mechanism is expressed in multi agent system.

The fuzzy logic approach is preferred to expedite the process of decision making in the system. This will also reduce uncertainty in the process in the interface environment. The suggested system provides decision-making and evaluation of results for manufacturing activities in the fuzzy situation. The evaluation of the decision- making process includes the agent list, task list, rule list and knowledge list structures that are combined in the fuzzy inference mechanism. Keywords: Multi-agent system, fuzzy decision-making, flexible manufacturing environment 1.

Introduction The next generation of manufacturing systems will be very flexible and knowledge intensive that produce and decide to manufacturing decisions by themselves in the virtual software development. Development of agents will support these innovations for improved manufacturing operations. A model was developed by applying the intelligent fuzzy agent interface to the manufacturing demand.

Fuzzy-based decision mechanism takes into consideration the relations between the taskbase, rulebase and knowledgebase in each agent structure. Proper situation is selected by the decision mechanism. The decision mechanism evaluates the taskbase, rulebase and knowledgebase under the changing conditions of manufacturing.

Multi-Agent Systems

Ralph et al, , designed and reviewed the fuzzy decision mechanism in a tool assignment system. The designed tool control system included the tool scheduling activities and tool information monitoring mechanism. While Naumann and Gu presented the part dispatching rules, optimized the rules and scheduled activities in a fuzzy logic approach, Ho and Hsieh used the fuzzy c-means approach for assignment of the part and tool in FMS. Tool assignment conditions were determined and evaluated in decision mechanism. Vidyarthi and Tiwari reviewed the machine loading problem with a fuzzy-based heuristic approach in FMS.

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Sangwan and Kodali suggested the fuzzy part family formation for cellular manufacturing systems. They derived only the fuzzy rules for the part properties. Mutel and Ostrosi reviewed the feature-based manufacturing cell formation for part-machine relations using a fuzzy set approach. This study includes the part-machine relations and features in a flexible manufacturing environment.

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Components characteristics evaluate alternative situations within the fuzzy agent interface based on the events and defined decision strategies. The agent architecture and communication structure are evaluated by Wilmott and M. Calisti in general [1]. James S. Albus [2] presents the reference architecture model. This model enlightens the robot architecture mechanism. The mechanism properties are defined in the model. Edward Dawidowicz1 [3] reviews the system structure which is the intelligent agent technology application of army command and control system in logistic planning and conduct of war.

Multi-agent systems for concurrent intelligent design and manufacturing | UTS Library

This model was defined but not realized. Decker, K. Suna Y. The relation between the user and agent is reviewed.

When the user asks a question, the agent is able to represent the knowledge as an answer. The contribution of the study can be explained as the evaluation of the manufacturing mechanism in an integrated structure. As can be seen in literature review, the agent or flexible manufacturing system was evaluated in a single dimension such as the assignment of the part or tool or selection of machine. In this study, the system is reviewed as a whole and assignment of the part, operation and machine procedures are fulfilled in integrity.

A new approach is proposed here to solve the task allocation in a flexible manufacturing system FMS environment using the fuzzy decision mechanism in a multi-agent approach, which ensures making optimal decisions in a dynamic system structure. In this paper, the second section reviews the fuzzy approach for multi-agent system, and then determines the architecture of the fuzzy decision mechanism and the general features of the suggested fuzzy inference system.

The third section presents the numerical example in the previous suggested model in manufacturing activities and the last section includes the evaluation of the suggested model. A system architecture and an agent structure are proposed. We also propose a novel scheme named distributed dynamic conceptual tree to overcome the difficulty of understanding between agents during the problem solving process and reduce the amount of communications between agents. An demonstration example validating our work is presented.

Information Intelligence and Systems Cat. Article :. DOI: Google Scholar. Barbuceanu, M.

Int Journal of Human-Computer Studies , 41 , Zreik and B. Trousse, eds. Europia, Paris, pp. Brennan, R. Brown, D. Butler, J.

Famili, D. Nau and S.

The Management of Operations

Cutkosky, M. IEEE Computer , 26 1 , Communication of the ACM , 39 9 , Finin, T. Report, University of Maryland, Baltimore. Fischer, K. Goldmann, S.