مقاله Designing a new multi-objective fuzzy stochastic DEA model in a dynamic ‎environment to estimate efficiency of decision making units (Case Study: An Iranian Petroleum Company)

توجه : به همراه فایل word این محصول فایل پاورپوینت (PowerPoint) و اسلاید های آن به صورت هدیه ارائه خواهد شد

 مقاله Designing a new multi-objective fuzzy stochastic DEA model in a dynamic ‎environment to estimate efficiency of decision making units (Case Study: An Iranian Petroleum Company) دارای ۲۵ صفحه می باشد و دارای تنظیمات در microsoft word می باشد و آماده پرینت یا چاپ است

فایل ورد مقاله Designing a new multi-objective fuzzy stochastic DEA model in a dynamic ‎environment to estimate efficiency of decision making units (Case Study: An Iranian Petroleum Company)  کاملا فرمت بندی و تنظیم شده در استاندارد دانشگاه  و مراکز دولتی می باشد.

توجه : در صورت  مشاهده  بهم ریختگی احتمالی در متون زیر ،دلیل ان کپی کردن این مطالب از داخل فایل ورد می باشد و در فایل اصلی مقاله Designing a new multi-objective fuzzy stochastic DEA model in a dynamic ‎environment to estimate efficiency of decision making units (Case Study: An Iranian Petroleum Company)،به هیچ وجه بهم ریختگی وجود ندارد

بخشی از متن مقاله Designing a new multi-objective fuzzy stochastic DEA model in a dynamic ‎environment to estimate efficiency of decision making units (Case Study: An Iranian Petroleum Company) :

سال انتشار : ۲۰۱۵

تعداد صفحات :۲۵

This ‎paper presents a new multi-objective fuzzy stochastic data envelopment analysis model (MOFS-DEA) under mean chance constraints and common weights to estimate the efficiency of decision making units for future financial periods of them. In the initial MOFS-DEA ‏model, the outputs and inputs are ‎characterized by random triangular fuzzy variables with normal distribution, in which data are ‎changing sequentially. Since the initial MOFS-DEA model is a complex model, we ‎convert it to its equivalent one-objective stochastic programming by ‎using infinite-norm approach. To solve it, we design a new hybrid meta-heuristic algorithm by integrating Imperialist Competitive Algorithm and Monte Carlo simulation. Finally, this paper presents a real application of the proposed model and the designed hybrid algorithm for predicting the efficiency of five gas stations for the next two periods of them, with using real information which gathered from credible sources. The results will be compared with the Qin’s hybrid algorithm in terms of solution quality and runtime.