Data Envelopment Analysis from simulation on the Lattice QCD using CCR model

• Main Author: Mohammad hossein Darvish motevally
• Format: Article
• Language: English
• Published: Islamic Azad University, Rasht Branch 2017-06-01
• Series: Iranian Journal of Optimization
• Subjects: data envelopment analysis; CCR model; Ranking model; Lattice QCD; Continuum limit
• Online Access: http://ijo.iaurasht.ac.ir/article_530812_7a72617bf37767dc261381bdc04c4318.pdf

One of the most serious principles in production theory in economic is the principle of "efficiency". Simply put, efficiency can be defined as the demand that the desired goals (outputs) are achieved with the minimum use of the available resources (inputs). In order to, distinguish the relative efficiency of organizational units with multiple inputs to produce multiple outputs, "Data Envelopment Analysis" (DEA) method was introduced by Charnes, Cooper and Rhodes. In fact, DEA is a linear mathematical programming which calculates the efficiency of an organization within a group relative to observed best practice within that group. Unlike common statistical analysis which are based on central tendencies, it is a methodology directed at the frontier. Recently, DEA has become one of the most favorite fields in operations research. The background was a motivation for us to in this paper, via running the CCR model in "DEA-Solver Software", present data envelopment analysis from simulation on the lattice QCD with temporal extent N_τ=4,6, respectively. Astonishingly, results are derived for both cases, indicating the fact that efficient data set belong to the areas of high temperature (deconfinement phase). It is very interesting to highlight that even an efficient data has not reported at low temperature (confinement phase). Note that the data obtained at the critical temperature is also efficient. As expected from practical lattice QCD, the DEA-CCR model presented in this paper also confirms the fact which the best data set arises from simulation in continuum limit a→0. Indeed, by taking the limit of vanishing lattice spacing, the efficiency of algorithms can be further.