A Vision of the Internet of Things: A Review of Critical Challenges

Today, Information Communication Technology has brought many benefits to have a better life. Meanwhile, the concept of the Internet of Things (IoT), which has transformed the traditional lifestyle into a modern lifestyle and is growing rapidly, is of great importance. This research deals with the critical challenges of IoT. Although not much time has passed since the advent of the concept of the IoT, today the Internet of Things has faced a great deal of complexity in the industry, which requires in-depth studies to realise its potential and challenges. This study introduces and examines IoT challenges including security and privacy, scalability, interoperability, mobility, protocol & standardisation, and energy consumption. In this study, the relationship between these challenges has been clearly defined. Finally, based on the research, some main challenges or sub-challenges considered for these challenges

Imprecise DEA for setting scale efficient targets.

This paper discusses the new aspects of setting scale efficient targets in DEA with imprecise data such as ordinal and interval. The achieved models are non-linear but it can be solved in linear Appa and Yue models with determining a set of exact data from imprecise input and output data. Numerical examples are provided to show the projection of DMUs to their most productive scale size under input minimization and output maximization criteria

Target setting in data envelopment analysis using MOLP.

Data envelopment analysis (DEA) and multiple objective linear programming (MOLP) can be used as tools in management control and planning. The existing models have been established during the investigation of the relations between the output-oriented dual DEA model and the minimax reference point formulations, namely the super-ideal point model, the ideal point model and the shortest distance model. Through these models, the decision makers’ preferences are considered by interactive trade-off analysis procedures in multiple objective linear programming. These models only consider the output-oriented dual DEA model, which is a radial model that focuses more on output increase. In this paper, we improve those models to obtain models that address both inputs and outputs. Our main aim is to decrease total input consumption and increase total output production which results in solving one mathematical programming model instead of n models. Numerical illustration is provided to show some advantages of our method over the previous methods

A full investigation of the directional congestion in data envelopment analysis

One of interesting subjects in Data Envelopment Analysis (DEA) is estimation of congestion of Decision Making Units (DMUs). Congestion is evidenced when decreases (increases) in some inputs result in increases (decreases) in some outputs without worsening (improving) any other input/output. Most of the existing methods for measuring the congestion of DMUs utilize the traditional definition of congestion and assume that inputs and outputs change with the same proportion. Therefore, the important question that arises is whether congestion will occur or not if the decision maker (DM) increases or decreases the inputs dis-proportionally. This means that, the traditional definition of congestion in DEA may be unable to measure the congestion of units with multiple inputs and outputs. This paper focuses on the directional congestion and proposes methods for recognizing the directional congestion using DEA models. To do this, we consider two different scenarios: (i) just the input direction is available. (ii) none of the input and output directions are available. For each scenario, we propose a method consists in systems of inequalities or linear programming problems for estimation of the directional congestion. The validity of the proposed methods are demonstrated utilizing two numerical examples

A Ranking Method Based on Common Weights and Benchmark Point

The highest efficiency score 1 (100% efficiency) is regarded as a common benchmark for Decision Making Units (DMUs). This brings about the existence of more than one DMU with the highest score. Such a case normally occurs in all Data Envelopment Analysis (DEA) models and also in all the Common Set of Weights (CSWs) methods and it may lead to the lack of thorough ranking of DMUs. And ideal DMU based on its specific structure is a unit that no unit would do better than. Therefore, it can be utilized as a benchmark for other units. We are going to take advantage of this feature to introduce a linear programming problem that will produce CSWs. The proposed method assures that the efficiency of all the units is less than that of the benchmark unit. As a result, it provides a comprehensive ranking of DMUs. Moreover, the proposed method is also noteworthy regarding computation. A numerical example is suggested to clarify and explain the proposed method and compare it to two other CSWs methods. Finally, 33 universities in Iran were ranked and compared using the proposed method

Using slacks-based model to solve inverse DEA with integer intervals for input estimation

This paper deals with an inverse data envelopment analysis (DEA) based on the non-radial slacks-based model in the presence of uncertainty employing both integer and continuous interval data. To this matter, suitable technology and formulation for the DEA are proposed using arithmetic and partial orders for interval numbers. The inverse DEA is discussed from the following question: if the output of DMUo increases from Y-o to /beta(o), such the new DMU is given by (alpha(o)& lowast;, /3) belongs to the technology, and its inefficiency score is not less than t-percent, how much should the inputs of the DMU increase? A new model of inverse DEA is offered to respond to the previous question, whose interval Pareto solutions are characterized using the Pareto solution of a related multiple-objective nonlinear programming (MONLP). Necessary and sufficient conditions for input estimation are proposed when output is increased. A functional example is presented on data to illustrate the new model and methodology, with continuous and integer interval variables

Modificación de la condición de convexidad en el Análisis Envolvente de Datos (AED)

Conventional Data Envelopment Analysis (DEA) models are based on a production possibility set (PPS) that satisfies various postulates. Extension or modification of these axioms leads to different DEA models. In this paper, our focus concentrates on the convexity axiom, leaving the other axioms unmodified. Modifying or extending the convexity condition can lead to a different PPS. This adaptation is followed by a two-step procedure to evaluate the efficiency of a unit based on the resulting PPS. The proposed frontier is located between two standard, well-known DEA frontiers. The model presented can differentiate between units more finely than the standard variable return to scale (VRS) model. In order to illustrate the strengths of the proposed model, a real data set describing Iranian banks was employed. The results show that this alternative model outperforms the standard VRS model and increases the discrimination power of (VRS) models.Los modelos de análisis envolvente de datos convencionales (DEA) se basan en un conjunto de posibilidades de producción (PPS) que satisface varios postulados. La extensión o modificación de estos axiomas conduce a diferentes modelos DEA. En este artículo, nuestro enfoque se concentra en el axioma de convexidad, dejando los otros axiomas sin modificar. Modificar o extender la condición de convexidad puede conducir a un PPS diferente. A esta adaptación le sigue un procedimiento de dos pasos para evaluar la eficiencia de una unidad en función del PPS resultante. La frontera propuesta está ubicada entre dos fronteras de la DEA estándar y conocidas. El modelo presentado puede diferenciar entre unidades con mayor precisión que el modelo de retorno a escala variable estándar (VRS). Para ilustrar las fortalezas del modelo propuesto, se utilizó un conjunto de datos reales que describen los bancos iraníes. Los resultados muestran que este modelo alternativo supera al modelo estándar de VRS y aumenta el poder de discriminación de los modelos (VRS)

Modificación de la condición de convexidad en el Análisis Envolvente de Datos (AED)

Los modelos de análisis envolvente de datos convencionales (DEA) se basan en un conjunto de posibilidades de producción (PPS) que satisface varios postulados. La extensión o modificación de estos axiomas conduce a diferentes modelos DEA. En este artículo, nuestro enfoque se concentra en el axioma de convexidad, dejando los otros axiomas sin modificar. Modificar o extender la condición de convexidad puede conducir a un PPS diferente. A esta adaptación le sigue un procedimiento de dos pasos para evaluar la eficiencia de una unidad en función del PPS resultante. La frontera propuesta está ubicada entre dos fronteras de la DEA estándar y conocidas. El modelo presentado puede diferenciar entre unidades con mayor precisión que el modelo de retorno a escala variable estándar (VRS). Para ilustrar las fortalezas del modelo propuesto, se utilizó un conjunto de datos reales que describen los bancos iraníes. Los resultados muestran que este modelo alternativo supera al modelo estándar de VRS y aumenta el poder de discriminación de los modelos (VRS)

Modificación de la condición de convexidad en el Análisis Envolvente de Datos (AED)

Conventional Data Envelopment Analysis (DEA) models are based on a production possibility set (PPS) that satisfies various postulates. Extension or modification of these axioms leads to different DEA models. In this paper, our focus concentrates on the convexity axiom, leaving the other axioms unmodified. Modifying or extending the convexity condition can lead to a different PPS. This adaptation is followed by a two-step procedure to evaluate the efficiency of a unit based on the resulting PPS. The proposed frontier is located between two standard, well-known DEA frontiers. The model presented can differentiate between units more finely than the standard variable return to scale (VRS) model. In order to illustrate the strengths of the proposed model, a real data set describing Iranian banks was employed. The results show that this alternative model outperforms the standard VRS model and increases the discrimination power of (VRS) models.Los modelos de análisis envolvente de datos convencionales (DEA) se basan en un conjunto de posibilidades de producción (PPS) que satisface varios postulados. La extensión o modificación de estos axiomas conduce a diferentes modelos DEA. En este artículo, nuestro enfoque se concentra en el axioma de convexidad, dejando los otros axiomas sin modificar. Modificar o extender la condición de convexidad puede conducir a un PPS diferente. A esta adaptación le sigue un procedimiento de dos pasos para evaluar la eficiencia de una unidad en función del PPS resultante. La frontera propuesta está ubicada entre dos fronteras de la DEA estándar y conocidas. El modelo presentado puede diferenciar entre unidades con mayor precisión que el modelo de retorno a escala variable estándar (VRS). Para ilustrar las fortalezas del modelo propuesto, se utilizó un conjunto de datos reales que describen los bancos iraníes. Los resultados muestran que este modelo alternativo supera al modelo estándar de VRS y aumenta el poder de discriminación de los modelos (VRS)

A cost-utility analysis of different antiviral medicine regimens in patients with chronic hepatitis C virus genotype 1 infection

Background: Despite the introduction of new drug regimens with high effectiveness for the hepatitis C virus (HCV) patients, especially in HCV genotype 1, no cost-effectiveness study on the selection of the superior drug strategy in Iran has been conducted yet. Objectives: This study is aimed to assess the cost-effectiveness of the three drug regimens of pegylated interferon and ribavirin (PR), sofosbuvir (SOF) + PR and ledipasvir and sofosbuvir (LDV/SOF) in patients with HCV genotype 1 in Iran in the year 2014. Methods: A Markov micro-simulation model was used to evaluate the cost-effectiveness of the three drug strategies for a cohort of 10000 patients. Quality-adjusted life-years (QALYs) were extracted from published studies. Cost data was estimated through the review of medical records and obtaining experts opinion. Results: The results showed that the SOF + PR drug compared with PR had a lower cost and was more effective, but compared with the LDV/SOF, in spite of its lower cost, it was less efficient. The QALY values obtained for PR, SOF + PR and LDV/SOF, respectively, were 10.98, 12.08 and 12.28 and their costs were $41,741,$ 7,676 and $46,993. Moreover, the results obtained from acceptability curves showed that SOF + PR were the most cost-effective treatment for thresholds below$ 45,270 PPP. Conclusions: The use of SOF + PR regimen or LDV/SOF can significantly reduce the incidence of complications associated with the disease. For example, short and long-term outcomes are better than the current drug regimens for HCV genotype 1 patients in all stages of the disease