Resource management, especially suitable resource allocation, is one of the concerned issues in organization. In this article, we proceed for a real problem in which decider want to select a group among decider units (DMU), to achieve two desired goals: first, because of extra cost involvement caused by maintenance, decider inclined to use more his accessed resources. And second, decider likes to select units that established group posses most set efficiency. In this article a integer programming model based on Data Envelop Analysis is introduced for selecting a group with most set efficiency which realizes concerned conditions of decider. 

Energy is one of the primary inputs for the production of goods and services. Higher production and growth requires energy efficiency improvements. Categorizing provinces into homogenous industrial clusters, this study evaluates the energy productivity of industrial energy use of Iran Provinces. Energy efficiency as measured by a non-parametric Data Envelop Analysis model (DEA) for the period of 1385-1393, is used as a proxy for energy productivity. According to the results, there are considerable disparities in industrial energy productivity within industrial provincial clusters. In the cluster consisting of Tehran, Isfahan, and Khuzestan, the highest energy productivity belongs to Isfahan has and Khuzestan has the lowest energy productivity. Khorasan Razavi has the lowest energy efficiency in the fourth industrial cluster. Hormozgan has the lowest energy efficiency in the fifth cluster.

Regarding necessity of performance evaluation an business excellence achievement, this paper is trying to present a business excellence model (BEM)through the combination of two qualitative and quantitative approach to evaluation which are the models taken from total quality management (TQM) and Data Envelopment Analysis (DEA). The model discussed covers all the advantages of the two models and reduces the defficiencies as mach as possible. By reviewing BEMs, in this research, the appropriate crieria for performance evaluation and business excellence are identified and also applied in DEA, which by using tangible inputs and outputs evaluate the organzations.

Measuring is one of the basic elements of productivity cycle and acts as a control ‎system for other elements in the cycle. In this article, for measuring productivity and ‎evaluating operation of organizations, a comprehensive model has been designed by ‎using Data Envelopment Analysis models (DEA) that is very accurate and true. The ‎Captioned model has been called "Dynamic Productivity Model" due to the fact that ‎time is considered as a decision making unit in this model‏.‏‎‎ In this research monthly tangible inputs and outputs have been used, CCR-BCC, ‎BCC-CCR, CCR and BCC have been tested based on Data of a big company and CCR ‎has been considered suitable for executing dynamic model‏.‏‎‎ Evaluating operation by dynamic model is an efficient tool for the managers in order ‎to distinguish the degree of deviation from efficiency in operation in the previous ‎periods, and before facing with new situation, through studying the reason of ‎deviation, they can make necessary planning for improving the efficiency. Also in this ‎research it is possible to compare uncongenial units that are one of the limits of DEA ‎model and also it is possible to evaluate operation of unique organization‏.‏

Human resource development makes more knowledge, insight and skills for employees to implement their jobs and task responsibilities. It ensures the existence of system, while appraising performance, to feedback the employees’ performance compensation, so that individual reviews his behavior and performance. Such feedback should be transmitted to human resource planners for formulation and adjustment of training programs. This paper, offers employees performance appraisal model, designed for Islamic Azad University of Firoozkuh, using Data Enveloped Analysis and determining efficiency of employees’ in different areas. Since some measures are not clearly defined and have some ambiguity and uncertainty, verbal research were considered in this research, in which suitable fuzzy digits were used and related computation were implemented. In this research, specifically how a combination model is developed based on DEA and fuzzy set of employees appraisal and its capability in reality is described.

The purpose of present study is to compare the relative efficiency of Iranian ports and identify the efficient ports in 2009 to 2011. Applied-analytical method was used and statistical population includes important Iranian ports. Data are gathered from Port and Maritime Organization of Iran. Data include the years 2009, 2010 and March to November 2011. CCR model is used to determine the efficiency ratio of ports and to distinguish efficient and inefficient ports. For the units that are identified as efficient by CCR model, Anderson–Pearson (AP) was used. For analyzing Data, LINGO and SPSS software was used. Findings showed that in 2009, 36%, in 2010,  27% and in 2011, 27% of main Iranian ports have been efficient. In 2009 and 2010, Bahonar and in 2011, Rajaei ports were recognized as the most efficient Iranian ports. Finally, virtual ports were introduced as a pattern for inefficient ports. Inefficient ports will achieve efficiency if they can be promoted to achieve the conditions of their similar virtual ports.

Benchmarking is a tool for monitoring of performance and search for the best practice through learning from others. This paper introduces a new model, namely Virtual Benchmarking Model (VBM), for benchmarking the R&D projects. This model uses the Data Envelopment Analysis (DEA) technique and identifies output's indicators based on R&D steps and addresses the problem of outputs inhomogeneous.The proposed model was examined empirically with input-output indicators captured from 30 electronics R&D projects of IROST. Results indicate that for identifying the best practices, efficiency and effectiveness criteria should be used jointly. Based on the VBM model, 17 % of R&D projects were distinguished as benchmark for other projects. The main characteristics of these projects were: 1) effective human resources planning and; 2) marketing for result of R&D projects. The use of virtual benchmarking process can lead to enhance the performance of R&D projects.

IntroductionThis study evaluates and ranks the scientific journals of psychology using the Data Envelopment Analysis (DEA) model. Data Envelopment Analysis (DEA) is a linear programming-based technique for measuring the relative performance of organizational units where the presence of multiple inputs and outputs makes comparison difficult.  DEA was originally developed to measure the efficiency of a Decision Making Units (DMU) as a whole unit, without considering its internal structure. In other words, the system is treated as a black box, within which inputs are supplied to produce outputs, with there generally being a positive correlation between the two.Traditional DEA models are only able to evaluate DMUs with deterministic inputs and outputs, while in real-world problems, Data are usually uncertain. So far, various approaches have been introduced to overcome the uncertainty of Data. In this paper, two robust DEA models are presented to evaluate the performance of systems with continuous uncertain Data under constant return to scale (CRS) and variable return to scale (VRS) conditions. The main advantage of the proposed robust DEA models over the previous robust DEA models is that they are able to formulate uncertainty in both input and output Data. Moreover, these models are also developed directly on basic traditional DEA models (not alternative models).Research Question(s)What is the status of effective indicators (citation and non-citation) in the ranking of valid scientific journals of psychology?What are the top scientific journals in different fields of psychology?What is the ranking status of psychology journals using the Data Envelopment Analysis (DEA) model?What is the ranking status of psychology scientific journals based on the Anderson-Peterson CRS and Anderson-Peterson VRS models?Literature ReviewDEA is used for ranking journals in different fields. A study by Rosenthal and Weiss (2017) ranks business journals, using Data from the Thomson Reuters Journal Citation Reports® (JCR). They ranked 358 business journals from five different JCR categories according to such citation-based factors as the number of articles, the number of citations, impact factor, five-year impact factor, immediacy index, eigenfactor score, and article influence score. They compute the relative efficiencies of the journals and thereby create plausible journal rankings that largely, but not completely, corroborate three widely used business publication journal ranking lists. In addition, we show how the different characteristics of the JCR Data impact the DEA ranking model. Finally, they identify journals that are not on the business publication lists but consistently perform very well relative to those benchmark journals, and should possibly be included in the business publication ranking lists. They also identify journals whose inclusion in widely used business publication rankings cannot be justified by their methodology.MethodologyIn this study of Data Envelopment Analysis technique, Anderson-Peterson model, CRS and VRS output-oriented, 2018 JCR ratings scientific journals of psychology were evaluated and the top journals ranked. This study has a quantitative approach and is applied research. The research method is scientometrics and bibliometrics using Operational Research techniques. In order to evaluate and rank psychology scientific journals, seven citation indicators were extracted from the Analytics Clarivate Database. These indicators are the total number of citations, journal impact factor, five-year impact factor, immediate index, citeable items, journal influence factor, and journal article influence factor.In this study, using Data Envelopment Analysis technique, Anderson-Peterson model, CRS and VRS output-oriented, 2018 JCR ratings scientific journals of psychology were evaluated and the top journals ranked.ResultsFindings show that with the help of JCR indicators and using the Anderson-Peterson CRS and VRS model, more accurate rankings of journals can be achieved. The comparison of scientific journals is in the ranking of CRS and VRS models. The Journal of Psychological Science in the Public Interest tops in both models. The top 10 journals based on CRS respectively are: Psychological Science in the Public Interest, International Review of Sport and Exercise Psychology, Annual Review of Psychology, Psychological Bulletin, Psychological Inquiry, Behavioral and Brain Sciences, Social Issues and Policy Review, Personality and Social Psychology Review, Annual Review of Clinical Psychology, Journal of Personality and Social Psychology.The top 10 journals based on VRS respectively are: Psychological Science in the Public Interest, International Review of Sport and Exercise Psychology, Annual Review of Psychology, Frontiers in Psychology, Psychological Bulletin, Journal of Personality and Social Psychology, Trends in Cognitive Sciences, Psychological Science, Frontiers in Human Neurosciences.DiscussionHere goes the text for your discussion.ConclusionScientific journals of psychology were evaluated and ranked using the Anderson-Peterson CRS and VRS models. Also, the evaluation indicators of scientific journals in the Clarivate Analytics Database were reviewed and the top three journals in 11 areas of psychology and the top 30 journals in this field were identified and introduced. The top 30 journals and their differences in CRS and VRS models were compared and analyzed. In the part of descriptive findings, the frequency of journals in different fields of psychology was found, including 10.25% of psychology journals in general, 10.91% of applied psychology journals, 1.86% of biological psychology journals, 17.17% scientific journals in the clinical psychology field, 10.11% scientific journals in advanced psychology field, 7.85% scientific journals in educational psychology field, 11.71% scientific journals in experimental psychology field, 1.73% scientific journals in mathematics psychology field, 18.28% are scientific journals in the field of interdisciplinary psychology, 1.73% are scientific journals in the field of psychoanalytic psychology, and 8.38% are scientific journals in the field of social psychology. Finally, considering the needs of the country's scientific community, conducting research with other research communities (different scientific fields and disciplines), conducting research and ranking journals with other indicators and normalized indicators are recommended.