Tuyển tập các báo cáo nghiên cứu về hóa học được đăng trên tạp chí hóa hoc quốc tế đề tài : Gamma distribution approach in chanceconstrained stochastic programming model | Atalay and Apaydin Journal of Inequalities and Applications 2011 2011 108 http content 2011 1 108 Journal of Inequalities and Applications a SpringerOpen Journal RESEARCH Open Access Gamma distribution approach in chance-constrained stochastic programming model Kumru D Atalay 1 and Aysen Apaydin2t Correspondence katalay@ department of Medical Education Faculty of Medicine 06490 Bahgelievler Ankara Turkey Full list of author information is available at the end of the article Springer Abstract In this article a method is developed to transform the chance-constrained programming problem into a deterministic problem. We have considered a chance-constrained programming problem under the assumption that the random variables ữý- are independent with Gamma distributions. This new method uses estimation of the distance between distribution of sum of these independent random variables having Gamma distribution and normal distribution probabilistic constraint obtained via Essen inequality has been made deterministic using the approach suggested by Polya. The model studied on in practice stage has been solved under the assumption of both Gamma and normal distributions and the obtained results have been compared. Keywords chance-constrained programming Essen inequality Gamma distribution 1. Introduction A chance-constrained stochastic programming CCSP models is one of the major approaches for dealing with random parameters in the optimization problems. Charnes and Cooper 1 have first modelled CCSP. Here they have developed a new conceptual and analytic method which contains temporary planning of optimal stochastic decision rules under uncertainty. Symonds 2 has presented deterministic solutions for the class of chance-constraint programming problem. Kolbin 3 has examined the risk and indefiniteness in planning and managing problems and presented chance-constraint programming models. Stancu-Minasian 4 has suggested a .
