Production Strategies for Batch Processing with Random Yield

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Olsen 9713727 This grant provides funding for the study of batch production environments with random yield. In these environments, products are produced in batches because either setup times or setup costs between product types are significant. Furthermore, the output from production is assumed to be highly variable. The motivation for this work is from the automotive parts industry and particularly a plant which produces molded and painted plastic bumpers. The paint line in this plant has highly unpredictable yield and changeovers between different paint colors are expensive. This research will further the development of (a) models that take into account random yield in a continuous time environment; (b) solution methods for these models that achieve computable error bounds on optimal values using both discrete and continuous approximations; (c) characterizations of general properties of optimal solutions of these random yield models that provide insight into managerial decisions; and (d) comparisons of discrete and continuous time models in general with insight into fundamental model characteristics of each approach and their relationship to underlying situations. The main impact of the research will be to improve decision making in random yield systems. It will provide methods that have computable bounds and that can be applied in a variety of settings from machining to painting to semiconductor fabrication. The methodology will be tested on real data to ensure that it is implementable in real time in complex manufacturing environments. The results of this research are expected to have a significant effect on industries critical to U.S. competitiveness (such as the automotive parts industry) where product yield is highly variable. The investigators will work with interested companies to ensure that the results are relevant. The research is also expected to have significant academic contributions in the areas of queueing theory, scheduling, and stochastic programming.