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In this paper, we present a new primal-dual predictor-corrector interior-point algorithm for linear optimization problems. In each iteration of this algorithm, we use the new wide neighborhood proposed by Darvay and Takács. Our algorithm computes the predictor direction, then the predictor direction is used to obtain the corrector direction. We show that the duality gap reduces in both predictor and corrector steps. Moreover, we conclude that the complexity bound of this algorithm coincides with the best-known complexity bound obtained for small neighborhood algorithms. Eventually, numerical results show the capability and efficiency of the proposed algorithm.

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Due to the changes of performance measures, a vital aspect for decision makers is finding optimal scale sizes of entities. Moreover, there are undesirable measures in many investigations. In the existing data envelopment analysis (DEA) approaches, optimal scale sizes (OSSs), average-cost efficiency (ACE) and average-revenue efficiency (ARE) of decision making units (DMUs) with desirable measures under strong disposability have been estimated while undesirable factors are presented in many real world examinations. Accordingly, in this research, OSSs and ARE of DMUs with undesirable outputs are addressed under managerial disposability. ARE is defined as the composite of scale and output allocative efficiencies under managerial disposability. To illustrate in detail, a two-stage DEA-based approach is rendered to estimate ARE and OSSs in the presence of undesirable outputs. A numerical example and an illustrative case are given to explain the proposed approach in this study.

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The goal of this study is to use the grounded theory technique to uncover the characteristics that influence personal branding in the food business. The current study is quantitative in terms of approach and applied in terms of goal. The systematic technique of Strauss and Corbin was applied in this study, which involves three basic procedures: open coding, axial coding, and selective coding. The snowball sampling approach was utilised in this study for sampling. Snowball sampling is generally the first step, and it continues until saturation is reached. Based on this, 19 specialists and managers in Iran's food business who were familiar with the term personal branding were interviewed in-depth and semi-structured interviews. MAXQDA 11.1.4 software was utilised to analyse the data in this investigation. The results showed the components identified in personal branding in casual conditions of this study including personal characteristics, business characteristics, strategies including identity tools defined in behavior, appropriate tools for illustration, social networking, social responsibility, use different and distinctive methods, focus on goal, market research, contextual conditions including cultural values, political, economic and social issues, ideas and beliefs, dynamic world and intervening conditions including criticisms and suggestions, difference between cyberspace and real world, audience expectation level, social norms and outcomes include advancing the company's goals, saving time and money, gaining internal satisfaction, attracting and retaining audiences, gaining reputation, and improving the quality of communication.

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Nowadays, with the expansion of globalization, increasing competition, the entry of various domestic and foreign companies, various products and advances in technology, maintaining customer satisfaction and loyalty has become difficult. One of the hallmarks of successful companies today is their competitiveness. The main purpose of this study is structural-interpretive modeling of industrial brand competitiveness in the petrochemical industry. This research is qualitative-quantitative mixed exploratory research. The statistical population in the qualitative part of the research includes faculty members and experts in the field of industrial management, marketing and industrial brand, professors familiar with the subject of research and managers and deputies with experience in petrochemical companies in the country using 16 snowball sampling method were chosen.  In a small part, the statistical community includes personnel (managers, deputies and experts) of the marketing and sales department of petrochemical companies in the country. For sampling, due to the small size of the statistical population and the irreversibility of the questionnaires has been used the whole number and the whole population has been considered as a sample in a small part (N = 255). The research tool in the qualitative part of the interview is semi-structured and in the quantitative part the researcher has made a questionnaire. For data analysis in the qualitative part, fuzzy Delphi theme and technique analysis has been used and in the quantitative part, ISM technique has been used for data analysis. In the qualitative part of the research, a total of 14 variables were identified as factors affecting the competitiveness of the industrial brand. These 14 factors are: Technological opportunism, Brand strength, brand differentiation, Commercialization of innovation, Strategic entrepreneurship, Exploratory marketing, Innovative marketing, Brand charm, Strategic knowledge management, customer relation management, Brand management system, Strategic intelligence and strategic pricing
 

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In the traditional cost-efficiency model, the information about each decision unit includes inputs, outputs, and the input prices are fixed and specific. In practice, the price of the inputs often fluctuates at different times, and these prices for the decision-making unit are time-dependent. By the traditional method, the efficiency of decision units is impossible in the presence of time-dependent input prices. On the other hand, the exact method of cost-efficiency calculation is also difficult and time-consuming. In this study, a new method for calculating cost efficiency of decision making units with time-dependent prices during a period of time using numerical integral is presented. As  the information of the decision-making units varies over time, a method for calculating their cost efficiency accurately is presented. however,  the exact method is difficult or impossible to be solved  in some cases. Therefore, in this study, an approximate method for calculating the cost efficiency in the given state is presented. This is a suitable replacement for the precise method. The efficiency of decision making units at different time is measured and the units are ranked using the proposed method. Finally, a numerical example is provided to indicate the method and compare it with the precise method. This study shows that the efficiency obtained by the approximate method is very close to the efficiency obtained by the exact method, and at the same time, the calculation speed increases.
 

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Data Envelopment Analysis (DEA) is a nonparametric approach for evaluating the relative efficiency of a homogenous set of Decision Making Units (DMUs). To evaluate the relative efficiency of all DMUs, DEA model should be solved once for each DMU. Therefore, by increasing the number of DMUs, computational requirements are increased. The Cerebellar Model Articulation Controller (CMAC) is a neural network that resembles a part of the brain known as cerebellum. The CMAC network with a simple structure is capable of estimating nonlinear functions, system modelling and pattern recognition. Meanwhile, the CMAC approach has fast learning convergence and local generalization in comparison to other networks. The present paper is concerned with assessing the efficiency of DMUs by the CMAC neural network for the first time. The proposed approach is applied to a large set of 600 Iranian bank branches. The efficiency results are analyzed and compared with the Multi-layer Perceptrons (MLP) network outcomes. Based on the results, it can be seen that the DEA-CMAC results tend to be similar to those of DEA-MLP in terms of accuracy. In addition, the Mean Squared Error (MSE) in DEA-CMAC decreases much faster than that in DEA-MLP. The DEA-CMAC model takes 1008 and 1107 iterations to reach MSE errors of 2.03×10-4  and of 6.01×10-4 , respectively, while the DEA-MLP model takes 1190 iterations keeping the MSE error stable at 2.07×10-1 . Moreover, DEA-CMAC requirements for CPU time are far less than those needed by DEA-MLP.
 

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This study evaluates the effectiveness of factors affecting the development of virtual education in the era of COVID-19 based on SCORM model from the viewpoint of students at Ferdowsi University of Mashhad. SCORM includes six dimensions that provide a comprehensive picture of the implementation process of a web-based learning management system. This research is a descriptive survey in terms of practical purpose and in terms of the data collection method. The statistical population of this research includes all postgraduate students in 2019. According to Morgan's table, the sample size is 370 people. The data collection tool is a researcher-made questionnaire, and the validity and reliability of the questionnaire are confirmed. The stratified random sampling method is used to distribute the questionnaires. One-sample t-test and "SPSS" software are used to test the research hypotheses. The results of the research showed that in the era of COVID-19 pandemic, virtual education has been effective and at a higher than average level in all six dimensions of SCORM standard (Accessibility, Consistency, Financial facilities, Durability, ability to run, reusability) and every six hypotheses are confirmed. Based on this, it is concluded that the virtual education programs planned at the Ferdowsi University of Mashhad are on the right path and can be strengthened and developed by receiving periodic feedback. Also, considering the successful performance of virtual education in the pandemic, in the future, this technology can be used as an effective supplement in educational and research activities in the university.

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Here, we first associate a graph to a university course timetabling problem (UCTP) and use the components of this graph and some customary and organizational rules to transform the original large scale problem into some smaller problems. Then, we apply the branch and cut method to obtain the optimal solution of each smaller problem. Our presented approach enables us to apply exact methods to obtain high quality solutions for large scale UCTPs. Finally, we examine the numerical efficiency of the resulting algorithm.
 

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This research was carried out with the aim of providing a comprehensive model for redesigning the organizational structure that fulfills the requirements of activity in the era of the fourth industrial revolution with the approach of developing smart business. The research was of a sequential mixed type of quantitative and qualitative type. In the quantitative part, which was conducted using a descriptive survey method, in the first stage, organization pathology and organizational design models were examined and identified through a systematic review of the research literature, and Burton's multi-contingency model, which simultaneously includes pathology and organization design, was selected. In the second stage, the researcher developed a questionnaire based on different editions of Burton's multi-contingency model, and the validity and reliability of the questionnaire (content validity using the Laushe method, face validity and construct validity using the confirmatory factor analysis method with Smart PLS software and reliability It was evaluated by the methods of Cronbach's alpha, composite reliability and divergent and convergent reliability of Fornell and Lockerre), the results of which indicate the high internal validity of the research and the validity of the tool designed in the society. Finally, a 90-item questionnaire was developed to measure 14 dimensions and 28 components, which was arranged in the form of a Likert scale and distributed among 263 managers and experts of Esfahan Steel Company, who were selected by stratified random method. After data collection and analysis, it was found that the different dimensions of the organizational structure of Esfahan Steel Company based on Burton's multi-contingency model are not located in one area and are scattered in four different areas of the diagram, and the dimensions of the structure are not proportional and aligned. In the future, in order to redesign the appropriate structure, the research was continued using the qualitative method. In this section, with the purposeful sampling technique dependent on the criterion among 30 academic and steel industry experts related to the subject, the most important dimensions of the organizational structure (using the fuzzy Delphi method and in two rounds) and the components of business intelligence (using the fuzzy Delphi method) and during three rounds) were determined. Then, by using the fuzzy Delphi technique during four rounds, which stopped at the Schmidt agreement criterion, a comprehensive model of organizational structure redesign with the approach of developing smart business was obtained. The findings showed that the most effective components of business intelligence include commercial intelligence, artificial intelligence, strategic intelligence, and competitive intelligence, which can provide suitable platforms and facilitators to achieve a suitable and intelligent organizational structure. The results show that the use of business intelligence factors consisting of strategic, competitive, commercial and artificial intelligence models are effective platforms for making appropriate decisions in order to make changes and redesign the organizational structure (organization plan) and create appropriateness and alignment. It is between dimensions with the aim of developing smart business.

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The paper presents a scheme to supply energy consumers by using a multicarrier energy system (MES). Each MES unit consists of electrical vehicles (EVs), and combined heat and power (CHP) units, which are called energy hubs (EHs) hereinafter. The objective function minimizes the cost of energy of the whole system while considering power flow equations in electricity, heat, and gas grids, where constraints include technical index limits of MESs, EVs, and CHPs. The model has been formed as a non-linear problem (NLP), in the following, the present study proposes a linear programming (LP) model as a substitute for equations of the NLP method so that the global optimal solution is found with a low computation error. Furthermore, the demand parameters, electricity price, and characteristics of EVs are uncertain. To model these uncertainties, we adopt the point estimate approach. The case study of this research considers electricity, gas, and heating grids simultaneously. The energy hubs relate all three grids to each other. The method is tested on a system through simulation using GAMS software. According to obtained numerical results, the suggested LP approach reaches an optimal point with reduced computation time and low error compared to the original formulations. As a result, the indices of different networks are improved using power management of the energy hubs.  

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This study aims to develop a capacitated hub location-routing model to design a rapid transit network under uncertainty. The mathematical model is formulated by making decisions about the location of the hub and spoke (non-hub) nodes, the selection of the hub and spoke edges, the allocation of the spoke nodes to the hub nodes, the determination of the hub and spoke lines, the determination of the percentage of satisfied origin-destination demands, and the routing of satisfied demand flows through the lines. Capacity constraints are considered in the hub and spoke nodes and also the hub and spoke edges. Uncertainty is assumed for the demands and transportation costs, represented by a finite set of scenarios. The aim is to maximize the total expected profit, where transfers between the lines are penalized by including their costs in the objective function. The performance of the proposed model is evaluated by computational tests and some managerial insights are also provided through the analysis of the resulting networks under various parameter settings.
 

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In Fisher's and Arrow-Debreu's market equilibrium models with linear utilities, a set B of buyers and a set G of divisible goods, suppose that there are some buyers with surplus money w.r.t current prices of goods. If there does not exists an equilibrium, then, there are some buyers with surplus money w.r.t the given prices. A set of buyers with surplus money called a violated set. Computing this set helps to find the set of buyers with maximum surplus money w.r.t the given prices.  In this paper, two new kinds of violated sets are defined, which called maximum proportion and most violated sets. We present an algorithm to compute a maximum proportion set, which runs in at most |B| maximum flow computations. Also, we show that the set of all buyers B is a most violated set.
 

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Cost efficiency in which cost coefficients are given for some inputs (cost coefficients can be different for disparate decision-making units (DMUs)) is one of the most important concepts in data envelopment analysis (DEA) to analyze the performance. Moreover, in some occasions, the cost performance and changes of input measures should be addressed while the convexity property is violated. Therefore, in this paper, first a DEA model is provided to assess cost efficiency based on the free disposal hull (FDH) model. Then, by considering cost and technical efficiencies achieved, a multi-objective problem called the inverse FDH cost model is presented to determine input values based on output changes while the cost and technical efficiency levels are preserved. The multi-objective problem is computed applying two approaches. Also, a dataset from the literature is presented to show the performance of the proposed method. For this purpose, we used the data of six banks in different countries. We added 2% to the outputs and analyzed the inputs with two models. In the first model, we used cost coefficients for weights, and in the second model, we used the same weights. Contrary to forecasts, some entries have decreased and others have increased. But from the results, we have noticed that the first model is more realistic because most of the solutions have increased in this model.
 

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Today, a high volume of multimedia information is transmitted in computer networks, such as the internet, that the requirement for achieving high security level against unauthorized access. the visual secret sharing scheme is a cryptographic system without requiring a secret key that is able to encrypt information in a multi - user computer network. in this research, a visual secret sharing scheme including two strategies for binary communications and multiple relationships were considered. in binary communications, an entity plays a role in the role of the server and an entity in the role of the client 's service in this strategy using a network - based visual secret sharing scheme ( 2 , 2 ) a secure approach to establish double relations where the provider entity is sending confidential information after authentication of the client 's service. then, two main phases of registration and verification are performed on the basis of the visual secret sharing scheme based on random networks. in tuple communication ( n , n ) an entity plays a role in the role of the server and several entities in the role of the service, and then two main phases of registration and verification are executed.
 

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Abstract: Recently, Multi-objective optimization by ratio analysis (MOORA) as a new and efficient Multiple-criteria decision-making (MCDM) method was applied in different areas for ranking alternatives and choosing the best ones. MOORA method evaluates the studied options by using positive and negative criteria. In this paper, a literature review is presented to study the MOORA methodology and its applications. So, all published papers in Science Direct journals are investigated and categorized from different perspectives (application area, journal of publication, year of publication, authors’ nationality, and type of data in form of fuzzy /crisp). The papers covered several filed: material selection, energy, welding process, and surface roughness, automotive and wire, fuel selection, logistics and transportation, heat transfer, optimization, and other topics. It is hoped that the study is useful for researchers and also a useful reference for practitioners and academics to improve their future research. The highest amount of using the MOORA method with the number of 15 articles is related to material selection, which shows the importance of using the MOORA method for material selection. And the lowest amount of using the MOORA method with the number of two articles is related to fuel selection. The present study was able to provide a framework for future research by reviewing the MOORA method. The results show that the MOORA method is one of the most efficient methods for evaluating options in different fields, which can be used in different areas.
 

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One of the most useful tools in Operations Research (OR) which is essentially applied to evaluate the performance of treated Decision-Making Units (DMUs) is Data Envelopment Analysis (DEA). Because of in the current decades, DEA models have been used and extended in many disciplines and hence attracted much interests. The traditional DEA treats DMUs as black boxes and calculates their efficiencies by considering their initial inputs and their final outputs. Since, in the real situations, input data are included some uncertainties, hence in this study we consider a DEA with fuzzy stochastic data and suggest a three-stage DEA model by taking into account undesirable output. To achieve this aim, an extended probability approach is applied to the reform of three-stage DEA models. Finally, we give an illustrative example by considering a case study on the banking industry.

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The most convenient models of Solid Transportation (ST) problems have been justly considered a kind of uncertainty in their parameters such as fuzzy, grey, stochastic, etc. and usually, they suggest solving the main problems by solving some crisp equivalent model/models based on their proposed approach such as using ranking functions, embedding problems etc. Furthermore, there exist some shortcomings in formulating the main model for the realistic situations, since it omitted the flexibility conditions in their studies. Hence, to overcome these shortages, we formulate these conditions for the mentioned these problems with fuzzy flexible constraints, where there are no exact predictions for the values of the resources. In particluar, numerical investigation shows that each increasing for the values of the supply and demand is not effective for improving the objective function.  The value of the objective function is sensitive when supply and demand change, so we conduct a new study to diversify the value of the objective function, due to changes in resource and demand levels simultaneously.

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In this paper, a new method is presented using a combination of deep learning method, specifically recursive neural network, and Markov chain. The aim is to obtain more realistic results with lower cost in predicting COVID-19 patients. For this purpose, the BestFirst algorithm is used for the search section, and the Cfssubseteval algorithm is implemented for evaluating the features in the data preprocessing section. The proposed method is simulated using the real data of COVID-19 patients who were hospitalized in treatment centers of Tehran treatment management affiliated to the Social Security Organization of Iran in 2020. The obtained results were compared with three valid advanced methods. The results showed that the proposed method significantly reduces the amount of memory resource usage and CPU usage time compared to similar methods, and at the same time, the accuracy also increases significantly.
 

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Tone [29] proposed a method of super-efficiency slack-based measures (SBM) for ranking efficient decision-making units (DMUs), so that this model would rank efficient DMUs. The established model was able to measure radially. It calculates and measuring the efficiency of inefficient DMUs and the amount of super-efficiency of efficient DMUs. Du et al. [11] developed the Charens et al. [6] model in to the additive DEA model, as well as the additive super performance model. Turn et al. [32] used a linear SBM and S-SBM integrated model that had the properties of both models and reduced the time factor compared to previous models. In order to be able to calculate the amount of additive super efficiency; First we identify the efficient DMUs and then apply the additive super-efficiency model to the efficient DMUs. In this paper, the proposed model obtains the additive efficiency value of inefficient DMUs and the additive super efficiency value of efficient DMUs with less computation time. The amount of DMUs calculated from the integrated model in this article can be compared to the Guo et al. [15] article in comparison with the time table of the text of the article.
 

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Due to the importance of vehicle routing for delivering a large number of orders with different restrictions in the world, various optimization methods have been studied in past researches. In this article, a number of researches of recent years have been discussed, then the proposed model is described in 3 phases with the penalty index. This model has the ability to assign orders, route vehicles and determine the number of active vehicles dynamically with the aim of minimizing the total cost of distribution. By examining valid metaheuristic models and using their strengths and weaknesses, and considering multiple limitations, a new model of "dynamic 3-phase optimization" has been designed. The main application of the proposed model is for vehicle routing problems with capacity constraints of fleet number and capacity constraints (maximum and minimum number of orders). Finally, with simulation, the outputs of the model have been analyzed in different conditions . Although the limitation of maximum and minimum capacity is added to the problem, by dynamically considering the number of vehicles and using star clustering (initiative of this research), three social, environmental and economic dimensions were improved. The time for orders to reach customers decreased by 19.3%, fuel consumption and air pollution by 14.9%, and logistics costs by 8.7%. To calculate the final value of system stability, a unique 3D fuzzy model has been used. With the sensitivity analysis, we came to the conclusion that the 3-phase dynamic optimization model has led to a 14.58% improvement in system stability.