Most productive scale size decomposition for multi-stage systems in data envelopment analysis

2018 ◽  
Vol 120 ◽  
pp. 279-287 ◽  
Author(s):  
Saeed Assani ◽  
Jianlin Jiang ◽  
Rongmei Cao ◽  
Feng Yang
Keyword(s):  
Data Envelopment Analysis ◽  
Data Envelopment ◽  
Scale Size ◽  
Multi Stage ◽  
Most Productive Scale Size

scholarly journals Productivity of Timber Processing in Ondo State, Nigeria

2012 ◽  
Vol 2 (1) ◽  
pp. 1
Author(s):  
Adegboyega Eyitayo Oguntade ◽  
Temitope Enitan Fatunmbi ◽  
Joshua Adio Folayan
Keyword(s):  
Data Envelopment Analysis ◽  
Sampling Technique ◽  
Fixed Cost ◽  
Data Envelopment ◽  
Forest Reserve ◽  
Scale Size ◽  
Multi Stage ◽  
Ondo State ◽  
Using Data ◽  
Most Productive Scale Size

<p>This study is aimed at evaluating the efficiency of timber processors in Ondo State, Nigeria, using Data Envelopment Analysis. Multi stage sampling technique was used to select two Local Government Areas with the highest number of sawmills, from each of which twenty saw millers were randomly selected, given a total of forty saw millers. Based on Constant Return to Scale Technical Efficiency, 35% of the saw millers were technically efficient while on the basis of Variable Return to Scale TE, 60% of the saw millers were technically efficient. About 35% of the saw millers were scale efficient. The Data Envelopment Analysis output revealed that 35% of the sampled saw millers were both technically and scale efficient and were hence operating at the most productive scale size. About 65% of the saw millers were operating at sub-optimal condition. Excesses in input utilization were observed in respect of total fixed cost, costs of electricity, servicing of mill, timber from forest reserve and operation of truck; and remuneration of labour. The inefficient firms should be encouraged to emulate the operating practices of the most productive firms so as to improve their performance.</p>

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.

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