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- W2301371404 abstract "This research endeavor is motivated by a manufacturing system in which the jobs visit the stations multiple times in a probabilistic manner due to product failures. The probability is governed by the yield of the product at different stages of processing. The system under consideration is an optoelectronics assembly and testing facility. The primary objective of this research is to develop a method that optimally allocates resources to different product classes in the presence of product failures at different stages of processing, such that the flow time is minimized. The problem has been primarily dealt using the concepts of queuing networks. Jackson networks are used to predict the critical performance measures such as flow time, throughput, and Work in Process (WIP) of the manufacturing system. Traffic equations are used to obtain the net arrival rates and the number of visits made by the jobs to different nodes. Preliminary studies assume a single product manufacturing system with exponential inter-arrival and processing times. Having validated the fundamental equations, the concept has been extended to incorporate realistic scenarios such as shared nodes, multiple product classes, and general distributions for arrival and service rates. The analytical models are validated through simulation. In the second phase of this research, the output of the analytical model has been integrated with a capacity planning and allocation model to determine the optimum number of resources required and how they should be cross-trained. A two-staged mixed-integer non-linear stochastic capacity planning model has been formulated in order to obtain the optimal server configurations to minimize the flow time of the products in the system. Several problem instances were randomly generated to validate the analytical approximations. These problems were also solved using the capacity planning model to identify the optimum number of servers required and the cross-training matrix. In summary, the research is expected to provide a tactical capacity planning tool for the managers to allocate the capacity amongst different work stations in an uncertain environment driven by product failures and stochastic demands. (Abstract shortened by UMI.)" @default.
- W2301371404 created "2016-06-24" @default.
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- W2301371404 date "2005-01-01" @default.
- W2301371404 modified "2023-09-24" @default.
- W2301371404 title "Capacity planning and allocation for a complex manufacturing system with product failures" @default.
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