Stochastic DEA Model And Its Application In Anti-HIV Immunotherapy

Data Envelopment Analysis(DEA) is an intersectional field of operation research, management science and mathematical economics. DEA depends on mathematical programming to evaluate the relative efficiency of DMUs multiple inputs and multiple outputs. In this dissertation, the development of DEA theories, DEA methods and DEA application are discussed. The basic DEA models and DEA efficiency theories are analyzed.However, the prevailing use of DEA has been mainly applied to performance evaluation on the basis of an anterior and exact data set, while inputs and outputs of DMUs are actually ever-changeful and unexpectedly. As a result, DEA efficiency measurement may be sensitive to such stochastic variations. Therefore, he study of efficiency evaluation under uncertainty is now highlighted by the vast existence of stochastic factors into the stochastic sphere in the evaluated problem. This dissertation is to extend the existing DEA methods into the stochastic sphere. It introduces some special approaches to measure production operations with determined inputs and stochastic outputs and try to construct the basic theory and methods of the efficiency evaluation under uncertainty, especially in the case of multiple inputs and outputs.Finally, we apply the new type of stochastic DEA model into practice. As now the widely spreading of AIDS in the world deeply threats the healthy and existing of human, the model is used in a HIV immunology model and the efficient drug treatment strategies are then explored. Stochastic errors and side effects are considered in this model, which is an advantage over other deterministic efficiency models. Some of our conclusions agree with those published in related papers.