An object oriented intelligent agent simulation environment Public Deposited

http://ir.library.oregonstate.edu/concern/graduate_thesis_or_dissertations/6395w942w

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  • Manufacturing intelligent agent simulation has not been widely applied in industry because of its application complexity. This complexity, which includes choosing priority machines or jobs, determining machine maintenance schedules, and allocating working shifts and breaks, requires intelligent decision making. Manufacturing systems are strongly influenced by intelligent decision makers. Especially for a fixed manufacturing layout, system performance improvement depends on intelligent manufacturing decision making. As a result, a manufacturing simulation can not be truly complete if intelligent decision making processes are not represented. This thesis describes an architecture which includes the representation of intelligent agents in manufacturing simulation model. An intelligent agent simulation environment (IASE) is developed under the concepts of distributed artificial intelligence and object oriented methodology. As an extension to an existing simulation environment, IASE inherits primary manufacturing simulation elements and material handling systems from object oriented manufacturing architecture (Beaumariage, 1990) and AGV simulation system (Beaumariage and Wang, 1995). In IASE, production operators, maintenance technicians and job releasers are created to represent manufacturing intelligent agents. Several basic elements such as the blackboard structure and knowledge base for supporting intelligent agent simulation are also developed. In contrast to traditional simulation environments designed for and in procedural programming languages, future extensions or modifications for IASE are eased since IASE is developed in an object oriented fashion. This paper introduces IASE structure both in the conceptual design and implementation methodology levels. At the end, two case studies are performed. The first case study is to verify IASE's implementation and results by comparing it with a model developed in SLAM II. The second case study, a mixed intelligent agent decision making example, demonstrates the intelligent agent simulation ability of IASE.
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