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• W2508254242 abstract "Production planning and control techniques are crucial to successful apparel manufacturing, but the search for a feasible or optimal system of production planning and control often involves solving a large-scale combinatorial problem. It is difficult to have a system that can automatically incorporate production orders, supervisory skill, production performance, and their relationship to the interacting mechanisms within the constraints of plant resources and the management policies of individual apparel corporations. Standardisation of production planning and control processes will become feasible when an intelligent system has been developed to emulate the decision making of production planners, identify suitable methods of resource allocation and determine the parameters of process conditions from the manufacturing system. By applying computational intelligence techniques (CITs) to four selected problems—job complexity, line balancing, prediction of sewing performance of the fabric, and balance control in the assembly line—that commonly occur in the apparel manufacturing system, new solutions for management were devised. Of the four major CITs (inductive learning, genetic algorithms, fuzzy logic, and neural networks), an appropriate computational intelligence technique was chosen for each of the four problems cited above, based on the nature and characteristics of the same four experiments were designed for the four selected problems to demonstrate the applicability of CITs to apparel manufacturing systems. The algorithms found to be effective in handling the corresponding problem were developed according to the properties of the selected CIT. The experimental results were compared with the actual performance using data collected from local apparel manufacturers in order to evaluate the effectiveness of the techniques. When an inductive learning algorithm was applied to the job complexity problem, and when a neural network was applied to the prediction of sewing performance of fabrics, both sets of experimental results showed that the performance of selected CITs were close to those derived on the basis of human judgement. In the case of applying the genetic algorithm to the assembly line balancing problem, and in the case of applying fuzzy logic to the balance control in the assembly line of apparel manufacturing, it was found that the selected CIT worked more effectively and efficiently than the existing practice. All four experiments successfully demonstrated that CITs were able to emulate human decisions and could be used to good effect for production planning and control in apparel manufacturing. This study introduced the application of CITs to the apparel manufacturing system and has consequences for the development of an intelligent apparel manufacturing system." @default.
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• W2508254242 modified "2023-09-27" @default.
• W2508254242 title "Towards an intelligent apparel manufacturing system" @default.
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