İktisadi ve İdari Bilimler Fakültesi / Faculty of Economics and Administrative Sciences
Permanent URI for this collectionhttps://hdl.handle.net/11727/1399
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Item Evaluating Efficiency of Cloud Service Providers in Era of Digital Technologies(ANNALS OF OPERATIONS RESEARCH, 2024) Azadi, Majid; Toloo, Mehdi; Ramezani, Fahimeh; Saen, Reza Farzipoor; Hussain, Farookh Khadeer; Farnoudkia, HajarThe rapid growth of advanced technologies such as cloud computing in the Industry 4.0 era has provided numerous advantages. Cloud computing is one of the most significant technologies of Industry 4.0 for sustainable development. Numerous providers have developed various new services, which have become a crucial ingredient of information systems in many organizations. One of the challenges for cloud computing customers is evaluating potential providers. To date, considerable research has been undertaken to solve the problem of evaluating the efficiency of cloud service providers (CSPs). However, no study addresses the efficiency of providers in the context of an entire supply chain, where multiple services interact to achieve a business objective or goal. Data envelopment analysis (DEA) is a powerful method for efficiency measurement problems. However, the current models ignore undesirable outputs, integer-valued, and stochastic data which can lead to inaccurate results. As such, the primary objective of this paper is to design a decision support system that accurately evaluates the efficiency of multiple CSPs in a supply chain. The current study incorporates undesirable outputs, integer-valued, and stochastic data in a network DEA model for the efficiency measurement of service providers. The results from a case study illustrate the applicability of our new system. The results also show how taking undesirable outputs, integer-valued, and stochastic data into account changes the efficiency of service providers. The system is also able to provide the optimal composition of CSPs to suit a customer's priorities and requirements.Item Malmquist Index Evaluation of Countries: 2000-2019(2023) Farnoudkia, HajarOne of the sophisticated mathematical techniques for evaluating the relative efficiency of decision-making units (DMUs) in a multi-inputs-output setting is data envelopment analysis. A non-parametric productivity index called the Malmquist index (MI) tracks changes in a DMU's overall factor output over time. This study seeks to present a broad overview of the changes in the MI for nearly all of the world's nations over 19 years beginning in 3061. This research evaluates a time series data set made up of 16 economic indexes. Each nation's MI, which compares 2000 and 2019, shows how each nation has changed over that period. One of the most important purposes of this study comparing the countries by their MI is that it allows for a fair comparison of productivity across different time periods and regions. It also provides insights into the sources of productivity growth, such as changes in technology or improvements in efficiency. As a result, the countries can allocate resources more wisely and develop more effective investment plans by understanding the elements that lead to productivity increase. By implementing some statistical techniques, nations are further divided into four categories based on their MI. Furthermore, a yearly distribution of the MI has been included to illustrate its trends between the years 2000 to 2019. Finally, the changing flow for some countries of each category is shown in three-year tracks.Item Vine copula graphical models in the construction of biological networks(2021) Farnoudkia, Hajar; Purutcuoglu, VildaThe copula Gaussian graphical model (CGGM) is one of the major mathematical models for high dimensional biological networks which provides a graphical representation, espe-cially, for sparse networks. Basically, this model uses a regression of the Gaussian graphical model (GGM) whose precision matrix describes the conditional dependence between the variables to estimate the coefficients of the linear regression model. The Bayesian inference for the model parameters is used to overcome the dimensional limitation of GGM under sparse networks and small sample sizes. But from the application in bench-mark data sets, it is seen that although CGGM is successful in certain systems, it may not fit well for non-normal multivariate observations. In this study, we propose the vine copulas to relax the strict normality assumption of CGGM and to describe networks from a variety of copulas alternates besides the Gaussian copula. Accordingly, we evaluate the best fitted bivariate copula distribution for every pairwise gene and compute the estimated adjacency matrix which denotes the presence of an edge between the corresponding genes. We assess the performance of our proposed approach in three network data via distinct accuracy measures by comparing the outputs with the results of the CGGM.