Sustainability Assessment and Most Productive Scale Size: a Stochastic DEA Approach with Dual Frontiers

2021 ◽  
Author(s):  
Monireh Jahani Sayyad Noveiri ◽  
Sohrab Kordrostami ◽  
Alireza Amirteimoori
Keyword(s):  
Sustainability Assessment ◽  
Scale Size ◽  
Stochastic Dea ◽  
Dea Approach ◽  
Most Productive Scale Size

Assessing most productive scale size with fuzzy stochastic data envelopment analysis

2022 ◽  
pp. 1-11
Author(s):  
Hooshang Kheirollahi ◽  
Mahfouz Rostamzadeh ◽  
Soran Marzang
Keyword(s):  
Relative Efficiency ◽  
Data Envelopment ◽  
Random Inputs ◽  
Dea Model ◽  
Scale Size ◽  
Stochastic Dea ◽  
Probability Constraints ◽  
Most Productive Scale Size ◽  
Decision Making Units ◽  
Fuzzy Random

Classic data envelopment analysis (DEA) is a linear programming method for evaluating the relative efficiency of decision making units (DMUs) that uses multiple inputs to produce multiple outputs. In the classic DEA model inputs and outputs of DMUs are deterministic, while in the real world, are often fuzzy, random, or fuzzy-random. Many researchers have proposed different approaches to evaluate the relative efficiency with fuzzy and random data in DEA. In many studies, the most productive scale size (mpss) of decision making units has been estimated with fuzzy and random inputs and outputs. Also, the concept of fuzzy random variable is used in the DEA literature to describe events or occurrences in which fuzzy and random changes occur simultaneously. This paper has proposed the fuzzy stochastic DEA model to assess the most productive scale size of DMUs that produce multiple fuzzy random outputs using multiple fuzzy random inputs with respect to the possibility-probability constraints. For solving the fuzzy stochastic DEA model, we obtained a nonlinear deterministic equivalent for the probability constraints using chance constrained programming approaches (CCP). Then, using the possibility theory the possibilities of fuzzy events transformed to the deterministic equivalents with definite data. In the final section, the fuzzy stochastic DEA model, proposed model, has been used to evaluate the most productive scale size of sixteen Iranian hospitals with four fuzzy random inputs and two fuzzy random outputs with symmetrical triangular membership functions.

EFFICIENCY ANALYSIS OF PROVINCIAL DEPARTMENTS OF PHYSICAL EDUCATION IN IRAN

2012 ◽  
Vol 11 (05) ◽  
pp. 983-1008 ◽  
Author(s):  
JAHANGIR SOLEIMANI-DAMANEH ◽  
MAJID SOLEIMANI-DAMANEH ◽  
MEHRZAD HAMIDI
Keyword(s):  
Physical Education ◽  
Efficiency Score ◽  
Data Envelopment ◽  
Analytic Hierarchy ◽  
Scale Size ◽  
Dea Models ◽  
The Government ◽  
Student’S T ◽  
Most Productive Scale Size ◽  
Hierarchy Process

In many countries, including Iran, Provincial Departments of Physical Education try to develop the athletic sports and sports for all in their related areas (state), using the government resources. Their success rate has always been an important subject for the top sports managers of country. In this paper we use data envelopment analysis (DEA) and analytic hierarchy process (AHP) techniques for analyzing the performance of physical education organizations in Iran. Some convex and nonconvex DEA models have been used. Afterwards, we have used the Shannon's entropy for aggregating the results obtained from different models and providing a final efficiency score (FES) and a unified ranking. It can be seen that, in the ranking approach provided in this paper the most productive scale size (MPSS) units have the best rank (see Proposition 1). Our findings reveal that the average of FESs of the states is 0.472635 and 50% of the states have an FES more than this average. Classifying the sates to five efficiency classes, "Excellent, Good, Middle, Weak and Very Weak", the percentage of the states belonging to these classes are 6.7, 30, 16.6, 36.7 and 10, respectively. Also, some correlation and difference studies have been carried out using the Pearson's correlation and student's t-tests. Finally, comparisons between the results of some relevant existing publications and those given in the present paper are addressed.

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