Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/85883
DC FieldValueLanguage
dc.contributorInstitute of Textiles and Clothing-
dc.creatorWong, Wai-keung-
dc.identifier.urihttps://theses.lib.polyu.edu.hk/handle/200/1691-
dc.language.isoEnglish-
dc.titleIntelligent optimization model for computerized fabric-cutting system-
dc.typeThesis-
dcterms.abstractSearching for an optimal and feasible production planning and control solution is significant for the apparel manufacturers in a dynamic apparel market. The optimization problems in apparel manufacturing process have characteristics that cannot be solved in polynomial time. Emulating and improving the decision-making process of the industrial experts under different manufacturing environments will become feasible by developing an intelligent optimization model. Since the late 80s, computerized fabric-cutting system has became popular in apparel manufacturing process. The performance of a cutting system, which is generally neglected by most manufacturers, is a significant factor on the smoothness of operation of sewing lines and hence the overall efficiency of an apparel manufacturing plant. 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, hi 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.-
dcterms.accessRightsopen access-
dcterms.educationLevelPh.D.-
dcterms.extentxxii, 353 leaves : ill. ; 30 cm-
dcterms.issued2002-
dcterms.LCSHHong Kong Polytechnic University -- Dissertations-
dcterms.LCSHTextile fabrics -- Cutting -- Data processing-
dcterms.LCSHGarment cutting -- Data processing-
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