Bottlenecks, the key ingredients for improving the performances of the production networks, have been profoundly studied during the last decade. Yet, because of the complexity of the research results, there is still a...Bottlenecks, the key ingredients for improving the performances of the production networks, have been profoundly studied during the last decade. Yet, because of the complexity of the research results, there is still a significant gap between theory and practice. In this paper, we review various bottleneck definitions, detection methods and the asymptotic results and provide a practical guidance for recognizing and utilizing the bottlenecks in production networks. Queueing theory works as the mathematical foundation in our study. Various definitions of the bottlenecks are classified as either Performance in Processing （PIP） based or sensitivity based definitions, which reflect the preferences of the managers. Detection methods are surveyed closely based on the definitions. These methods are used to recognize the bottlenecks and to provide diagnosis results to managers. Comparisons show that different detection methods may lead to vastly different conclusions. The recognition of the bottlenecks has another advantage： the ultimate phenomena of the bottlenecks can greatly reduce the computation complexity in calculating the system performances. Bottlenecks based approximation and asymptotic results are studied to exhibit the contribution of bottlenecks in performance estimation and theoretical analysis.展开更多
基金This work was supported by NSFC Grant. No. (60074012,60274011) and NCET-04-0094 program.
文摘Bottlenecks, the key ingredients for improving the performances of the production networks, have been profoundly studied during the last decade. Yet, because of the complexity of the research results, there is still a significant gap between theory and practice. In this paper, we review various bottleneck definitions, detection methods and the asymptotic results and provide a practical guidance for recognizing and utilizing the bottlenecks in production networks. Queueing theory works as the mathematical foundation in our study. Various definitions of the bottlenecks are classified as either Performance in Processing （PIP） based or sensitivity based definitions, which reflect the preferences of the managers. Detection methods are surveyed closely based on the definitions. These methods are used to recognize the bottlenecks and to provide diagnosis results to managers. Comparisons show that different detection methods may lead to vastly different conclusions. The recognition of the bottlenecks has another advantage： the ultimate phenomena of the bottlenecks can greatly reduce the computation complexity in calculating the system performances. Bottlenecks based approximation and asymptotic results are studied to exhibit the contribution of bottlenecks in performance estimation and theoretical analysis.
