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State based modeling of scheduling agents in intelligent manufacturing

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dc.contributor Graduate Program in Industrial Engineering.
dc.contributor.advisor Ünal, Ali Tamer.
dc.contributor.author Sözer, Kamer.
dc.date.accessioned 2023-03-16T10:27:53Z
dc.date.available 2023-03-16T10:27:53Z
dc.date.issued 2007.
dc.identifier.other IE 2007 S68
dc.identifier.uri http://digitalarchive.boun.edu.tr/handle/123456789/13185
dc.description.abstract In dynamic and stochastic manufacturing environments, a scheduling system should be able to modify its schedule to adapt to changing situations such as machine breakdowns, due-date changes, new rush orders, canceled orders and so on. In centralized monolithic scheduling systems, dynamic updating may be prohibitive mostly due to computational constraints in generating new schedules, or due to non-existence of effective shop floor data monitoring/data collection systems across the organization. One of the approaches to generate timely scheduling decisions, especially for large scale problems, is to decompose the problem and distribute responsibility of sub-problems to various agents. When agents are organized as loosely coupled autonomous scheduling units, it is easier to manage and control the data with respective local availability constraints, and eventually, faster to respond to system changes. However, distributing the scheduling problem brings in additional issues such as: choice of the decomposition methodology (defines the boundaries of independence for the agents); accuracy and timing of representation of local reality in each agent; scheduling algorithms and frequency of re-scheduling in each agent; and maybe most importantly, handling mechanism for coupling constraints between agents. In this study, we propose a state based modeling approach to represent the workings of a scheduling agent so that effect of choices made in distributing the problem in terms of the overall performance of the scheduling methodology can formally be defined and analyzed. The proposed model will be a framework to properly identify conditions and timing of possible inconsistencies and inaccuracy, and analyze the effectiveness of various distribution mechanisms in terms of scheduling performance.
dc.format.extent 30cm.
dc.publisher Thesis (M.S.)-Bogazici University. Institute for Graduate Studies in Science and Engineering, 2007.
dc.relation Includes appendices.
dc.relation Includes appendices.
dc.subject.lcsh Computer integrated manufacturing systems.
dc.subject.lcsh Production scheduling.
dc.title State based modeling of scheduling agents in intelligent manufacturing
dc.format.pages x, 73 leaves;


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