In this thesis, three problems relating to the optimization of a computerized fabric-cutting system: selection of system configuration before installation, table-planning before production, and production control during production (which significantly influence the productivity and potentiality of a computerized cutting system) were addressed and handled by an intelligent optimization model. The formulation of the intelligent optimization model for computerized fabric-cutting system (IOMCFS) used in this research consists of a Queuing model, a Hybrid Flowshop Table-Planning (HFTP) model, and a Fuzzy Capacity-Allocation (FCA) model which is based on two artificial intelligence techniques (Genetic Algorithms and Fuzzy Logic) and one operation research theory (Queuing Theory).; Three experiments were designed to demonstrate the performance of the proposed intelligent model. In each experiment, actual production data were collected from three different types of manufacturing environments which operate small-sized, medium-sized, and large-sized production orders in the cutting rooms of local apparel manufacturing companies.; In order to evaluate the performance of the proposed techniques, the experimental results generated by the three models of IOMCFS were compared with industrial practice. The experimental results indicated that the performance of the HFTP and FCA model were better than that of industrial experts. In these experiments, the author validates the applicability of the Queuing model on system configuration decision making; the results demonstrated that the solutions generated by the Queuing model were very close to those derived by the HFTP model. The results also indicated that the IOMCFS could emulate the decision-making process of the industrial experts and apparel manufacturers to achieve the optimization of a computerized fabric-cutting system. (Abstract shortened by UMI.)
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