Cellular manufacturing has been an important phenomenon in manufacturing in recent decades. Tremendous amount of work has been done regarding issues such as cell formation, cell loading and job scheduling. However, majority of literature lacks consideration of uncertainty in the problem definition phase, thus methodology. In this dissertation, the impact of uncertainty of demand, processing times and capacity requirements on a cellular manufacturing system (CMS) performance are addressed and stochastic optimization approaches are developed and applied to ten case problems from industrial companies and cellular manufacturing literature. This dissertation consists of mainly three phases, namely: stochastic CMS design, stochastic CMS control and the integrated modeling and analysis of CMS design and CMS control. Capacitated cell formation under the impact of uncertain demand and processing times is defined as the stochastic CMS design problem. On the other hand, cell loading, job sequencing and manpower allocation considering probabilistic demand and processing times are the main issues addressed in the stochastic CMS control phase. Finally, the relationship between stochastic CMS design and stochastic CMS control comprises the "integration" phase. Nonlinear stochastic programming models are developed to optimize each phase and simulation models are also built to validate the results of mathematical optimization and assess manufacturing system performance. To deal with larger problems, as one of the widely used metaheuristic optimization techniques, Genetic Algorithms (GA) is utilized; a GA model is developed and compared with stochastic programming model by using simulation modeling and statistical analysis. Results indicated that stochastic programming can assist with a better decision making on CMS design and control due to its capability of capturing probabilistic nature of problems. In all cases, the proposed stochastic optimization approaches outperformed the conventional deterministic methods. Moreover, the proposed stochastic models let the decision maker to decide the amount of risk to take prior to making design and control related decisions. All in all, I believe that the proposed stochastic optimization-based decision making concepts will open a new corridor in cellular manufacturing research. On the other hand, the proposed approaches can easily be implemented in other popular industrial engineering problem domains including supply chain, healthcare, transportation and logistics.
|Commitee:||Bhutta, M. Khurrum S., Feger, Ana L. Rosado, Masel, Dale, Schwerha, Diana|
|Department:||Industrial and Systems Engineering|
|School Location:||United States -- Ohio|
|Source:||DAI-B 78/11(E), Dissertation Abstracts International|
|Subjects:||Management, Statistics, Industrial engineering, Systems science, Operations research|
|Keywords:||Cellular manufacturing, Control, Genetic algorithms, Manufacturing system design, Simulation modeling, Stochastic programming|
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