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The authors of this book tried to make these experiences available to those interested, considering the experience of several years of training, research, and implementation of projects in the supply chain performance evaluation field. This book intends to identify the current performance and competitive position of that supply chain compared to other supply chains by presenting and reviewing the techniques and models for measuring the efficiency and performance of the supply chain. Determining the performance of a supply chain is a good description of the status quo (what is). Determining the performance of a supply chain is useful for describing the past and present of supply chain processes, and on the other hand, it can be used to set performance goals and initiate the improvement process. To realize this, a strategic framework or model is needed to be able to extract indicators related to the efficiency of the supply chain and design the appropriate model.

Intro -- Preface -- Contents -- 1 Introduction to Data Envelopment Analysis and Fuzzy Sets -- Abstract -- 1.1 A Brief Review on Data Envelopment Analysis -- 1.2 Basic Definitions -- 1.3 Different Models of DEA -- 1.4 Fuzzy Set Theory -- 1.5 Conclusion -- References -- 2 Introductions and Definitions of R -- Abstract -- 2.1 Preliminaries of R -- 2.2 Basic Definitions -- 2.3 Definition of Different Variables Types -- 2.4 Attributes -- 2.5 Data Storage -- 2.5.1 Vectors -- 2.5.2 Matrixes -- 2.5.3 Arrays -- 2.5.4 Lists -- 2.5.5 Data Frames -- 2.6 Mathematical Operators -- 2.7 Logical Operators -- 2.8 Use R in Calculation -- 2.9 Basic Mathematical Functions -- 2.10 If Structure -- 2.11 If Conditional -- 2.11.1 Example: Consider the Following Example -- 2.12 If Else Conditional -- 2.13 For Function -- 2.14 While Command -- 2.15 Repeat Command -- 2.16 Import and Read Data in R -- 2.16.1 Data Command -- 2.16.2 Scan Command -- 2.16.3 Read.table Command -- 2.16.4 Read.delim Command -- 2.16.5 Fread Command -- 2.16.6 Excel_sheets Command -- 2.16.7 Read_excel Command -- 2.17 Storage and Writing Data -- 2.17.1 Write.table Command -- 2.17.2 Sink Command -- 2.18 Write Functions in R -- 2.19 Convert Objects -- 2.20 Conclusion -- References -- 3 Basic DEA Models with R Codes -- Abstract -- 3.1 Introduction -- 3.2 Input-Oriented DEA Models with R Codes -- 3.2.1 Input-Oriented CCR Envelopment Model with R Code -- 3.2.2 Input-Oriented CCR Multiplier Model with R Code -- 3.2.3 Input-Oriented BCC Multiplier Model with R Code -- 3.2.4 Input-Oriented BCC Envelopment Model with R Code -- 3.3 Output-Oriented DEA Models with R Codes -- 3.3.1 Output-Oriented CCR Envelopment Model with R Code -- 3.3.2 Output-Oriented CCR Multiplier Model with R Code -- 3.3.3 Output-Oriented BCC Multiplier Model with R Code -- 3.3.4 Output-Oriented BCC Envelopment Model with R Code.

This book presents the theory and application of the models presented in this regard and establishes a meaningful relationship between data envelopment analysis and multi-attribute decision making. The issue of "choice" using the aggregation of voters' votes is one of the most important group decision-making issues that are always considered by decision makers in electoral systems. Voting is a method of group decision making in a democratic society that expresses the will of the majority. Voting is perhaps the simplest way to gather the opinions of experts, and this ease of application has made it a multi-attribute decision-making method in group decisions. Preferential voting is a type of voting that may refer to electoral systems or groups of the electoral system. In preferential voting, voters vote for multiple candidates, and how the candidates are arranged on the ballot is important. Researchers have made many efforts to provide models of voter aggregation, and one of the best results of these efforts is the aggregation of votes based on the policy of data envelopment analysis. Thus, in group decisions, the opinions of experts are obtained in a simple structure and consolidated in an interactive and logical structure, and the results can be a powerful tool for decision support. This book provides a complete set of voting models based on data envelopment analysis and expressing its various applications in industry and society. However, most decision-making methods do not use the opinions of experts or reduce the motivation of experts to participate in complex interactions and time, while voting methods do not have this shortcoming. This book is suitable for graduate students in the fields of industrial management, business management, industrial engineering, applied mathematics, and economics. It can also be a good source for researchers in decision science, decision support systems, data envelopment analysis, supply chain management, healthcare management, and others. The methods presented in this book can not only offer a comprehensive framework for solving the problems of these areas but also can inspire researchers to pursue new innovative hybrid methods.

BACKGROUND: Building upon decades of continuous reforms, Iran has been implementing various initiatives to reach universal health coverage (UHC). Improving efficiency is a crucial intermediate policy objective for UHC. Therefore, this article aimed to measure the efficiency and productivity changes of the Iranian health system in making progress towards UHC during 2010−2015 in comparison with 36 selected other upper-middle-income countries. METHODS: We used panel data to measure the variations in technical efficiency (TE) and total factor productivity (TFP) through an extended data envelopment analysis (EDEA) and Malmquist productivity index, respectively. General government health expenditure (GGHE) per capita (International dollar) was selected as the input variable. Service coverage of diphtheria, tetanus and pertussis; family planning; antiretroviral therapy; skilled attendants at birth; Tuberculosis treatment success rate; and GGHE as % of total health expenditure (THE) were considered as output variables. The data for each indicator were taken from the Global Health Observatory data repository and World Development Indicator database, for 6 years (2010−2015). RESULTS: The TE scores of Iran's health system were 0.75, 0.77, 0.74, 0.74, 0.97, and 0.84 in the period 2010–2015, respectively. TFP improved in 2011 (1.02), 2013 (1.01), and 2014 (1.30, generally). The overall efficiency and TFP increased in 2014. Changes made in CCHE per capita and GGHE/THE attributed to the increase of efficiency. CONCLUSION: There is a growing demand for efficiency improvements in the health systems to achieve UHC. While there are no defined set of indicators or precise methods to measure health system efficiency, EDEA helped us to draw the picture of health system efficiency in Iran. Our findings highlighted the essential need for targeted and sustained interventions, i.e., allocation of enough proportion of public funds to the health sector, to improve universal financial coverage against health costs aiming to enhance the ...