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Generally, selecting an investment portfolio with appropriate returns that is also secure and auditable has been one of the issues raised in recent decades. For this purpose, the present research proposes an appropriate approach using ideal and anti-ideal values, ideal values, as well as maximum deviations of each objective, considering the sample in the examined market, fuzzy goals, interval fuzzy values for each asset, and their combination with satisfaction functions, fuzzy ideal planning, and weighting objectives using expert decision-makers' opinions, as well as the development of fuzzy basic weighting method. It seeks to select an investment portfolio in the digital currency market.

In this research, a new approach to selecting an investment portfolio based on uncertain data and multi-objective uncertain planning is proposed, and ultimately, the proposed approach is implemented in the digital currency market for portfolio selection.

The results of the present study show that the proposed model of investment portfolio compared to the base model not only led to higher returns but also had higher audibility and better risk control. In other words, the proposed model outperformed the base model in all the objectives under study.

Originality/Value:

 As distinguishing features of the proposed model of this research, one can mention: 1) constructing and using fuzzy distribution functions and calculating ideal values and expected ranges for all desired objectives considering the conditions of the examined market research using simple mathematical modeling, 2) utilizing the experience of financial market experts in planning model for selecting suitable investment portfolios in emerging financial markets, 3) presenting an approach to calculating portfolio risk in conditions of information scarcity in the problem environment using fuzzy theory, 4) development of the fuzzy benchmark-criterion method for weighting the objectives under study in the problem considering the expertise of financial market experts, and 5) simple modeling, considering interval fuzzy values in the model, and being usable for all individuals with different levels of investment knowledge.

Coronavirus disease 2019 or Covid-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future. Nine popular forecasting techniques are tested on the data of Covid-19 in Yazd city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. According to the selected evaluation criteria, the results of the comprehensive analysis emphasize that the most efficient models are the ARIMA model for predicting the cumulative cases of hospitalization of Covid-19 and the Theta model for the cumulative cases of death. Also, the autoregressive neural network model has the worst performance among other models for both hospitalization and death cases.

Coronavirus disease 2019 is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths during todays can be a useful step in predicting the costs and facilities needed in the future. This study aims to modeling, comparing the performance of models, and predict new cases and deaths efficiently in the future.

In this article nine popular forecasting techniques are tested on the data of Covid-19 in Bahabad city as a case study. Using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE), and the mean absolute percentage of error (MAPE) of the models are compared. 

The results of the analysis showed that the best model according to the evaluation criteria for forecasting cumulative cases of hospitalization of Covid-19 is the cubic spline smoothing model, and cumulative cases of death, KNN regression model. Also, autoregressive neural network and theta models for hospitalization cases, and for death cases, autoregressive neural network model has the worst performance among other models.

This study can provide a proper understanding of the spread of covid-19 disease in this region so that by taking precautionary measures and formulating appropriate policies, this epidemic can be effectively overcome. Also, unlike other studies, this study uses 9 different techniques and their comparison, which in turn increases the confidence factor in decision making. Also, an important point is that the data should be updated in real time.

Coronavirus disease 2019 or COVID-19, which is also called acute respiratory disease NCAV-2019 or commonly called corona, is a respiratory disease caused by acute respiratory syndrome coronavirus-2. Forecasting the number of new cases and deaths today can be a useful step in predicting the costs and facilities needed in the future. This study aims to model and predict new cases and deaths efficiently in the future.

In this article, 9 forecasting techniques were tested on the data of COVID-19 of Mehriz city, Iran as a case study from 2020/02/26 to 2021/12/19 and using the evaluation criteria of mean square error (MSE), root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the models were compared.

For cumulative cases of hospitalization, ARIMA, Exponential, Holt-Winters, and STL models performed better and autoregressive neural networks, Theta, and KNN regression showed poor performance. Also, for cumulative mortality cases, KNN regression, Exponential and Theta models have better performance in predicting cumulative mortality cases, and autoregressive neural networks, ARIMA, and cubic spline smoothing showed poor performance.

the best model according to the mentioned evaluation criteria for predicting cumulative cases of hospitalization of COVID-19 is STL model and for cumulative cases of death is the KNN regression model. Also, the autoregressive neural network model has the worst performance among other models, both for hospitalization and death cases. Also, the important point is that the data should be updated in real-time.

Electric power and power distribution are prominent infrastructures for economic development in any developing country like Iran. Also, the power distribution network is a very important supply chain that combines a variety of processes. Smart electrical energy distribution networks are one of the latest technologies in the world. The main goal of these networks is to provide reliable electricity, increase the reliability factor and network stability, and respond to the growing needs of customers with minimal damage to the environment, profit, and high efficiency. In the last three decades, the rapid evolution and prevalent adoption of information systems, distribution analysis tools, computational models, and more recently, the emergence of smart grid technologies have given utilities access to the data and tools required for improving these analyses and the possibility of increasing the efficiency of power distribution systems (by, for example, reducing losses and optimizing voltage profiles). Forecasting the future state of the network with the least error brings us closer to the smart network. Because electricity is a mortal product, a comprehensive approach to unplanned power extinction (outage) time is very valuable in preventing any power distribution losses. Various accidents disrupt (cause breakdowns in) the power distribution network, which can be repaired and restored without a hotline. One of the main reasons for customers' power outages is the blackouts in the distribution field, which are affected by technical and non-technical events in the electricity distribution networks. Forecasting these events and managing them can be effective in reducing unplanned power extinction (outage) time. The purpose of this article is to present a model for predicting the duration of unplanned power extinction (outage) and unsold energy based on the data recorded from 121 systems (controllers), the urban network of the three power distribution companies in Yazd province. The final result shows that the ARIMAX model(s) shows less error than the ARIMA model(s) and presents better prediction. Therefore, using exogenous variables in predictions and not being satisfied with the fluctuations of a variable can improve predictions. The model proposed for predicting unsold energy is ARIMAX(1,0,1)(0,0,0) considering the number of incidents and the time of unplanned outages as exogenous variables. The model also shows that in July 2022, the unplanned power extinction (outage) time of this network will be approximately ten hours and also the unsold power will be approximately 6 MWH. On the other hand, the community is without electricity and dissatisfaction has arisen, which lies in economic and social losses. Therefore, with this warning, managers should re-examine the factors of disruption and lack of electricity supply and think of measures to reduce these blackouts when planning for this month of the year

Nowadays, it is obvious that media has a dramatic role in people's lives. Among all types of media, TV can still be powerful if it tries to know its audience and uses creative management. A management that considers the interests of the media and the audience as one is effective.To achieve this goal, we should look for solutions to increase media influence by analyzing the audience and the programs together.In this article, we used innovative joint clustering of audiences based on demographic characteristics and characteristics of television programs. Solutions are provided for members of each cluster in order to increase media influence. The data was obtained from a researcher-made questionnaire and a sample of 390 related experts and people of Yazd.According to the demands of the audience during watching peaks, the managers of Yazd’s local TV channel have to review their broadcast schedule and use the solutions provided in this article on their agenda. Evaluating the quality of clustering shows its suitable structure. The proposed solutions have been validated according to the opinions of media experts and the degree of result’s compliance with the sources related to the research topic.In this article, due to a new technique called joint clustering, the audience is clustered hierarchically and simultaneously based on demographic characteristics and television programs. in addition, the solutions are provided for members in each cluster to increase media influence.

The present study aims to evaluate and improve the performance of human resources with an integrated approach of fuzzy hierarchical analysis and TOPSIS in the model of organizational excellence in Shahid Bahonar Copper Industries in Kerman. In order to prioritize each of the criteria of the organizational excellence model in order to improve the performance of human resources from the perspective of experts and employees, the fuzzy hierarchical analysis technique was used. First place, leadership criteria with the final score in the second place, business results (process) with the final score in the third place. In this study, in order to prioritize the sub-criteria of each of the research criteria, the TOPSIS model was used. External stakeholders are ranked first and below the achievement criteria of leadership improvement projects are ranked last. According to the results of prioritizing the obtained criteria, it was found that in order to achieve the desired performance of human resources using organizational excellence, the company needs to manage the correct strategy and principles in order to effectively implement the processes governing the company.

Today, the simultaneous reduction of location costs and transportation costs in the establishment of urban facilities is of critical importance. This becomes extremely sensitive when the area under consideration is in crisis situations and the inevitable transit of vehicles and inland transportation is difficult in those circumstances and may cause financial and irreparable damages.
In this study, an integrated hospital location hardening is proposed in presence of disruption conditions and facility failures. With respect to the predetermined budget, demands of different points, and some predicting of disruption conditions, the optimizing is done. According to the importance of time in proposing of hospital emergency services, one of the objective functions is minimizing of the maximum allocated distances. Another objective function is minimizing the number of demands, which are allocated to far points. Regarding to the bi-objective mathematical model, the ε-constraint and GAMS software are applied. According to the application of the proposed model for the large-scale problems and The NP-Hard structure of the problem, the NSGA-II is applied and evaluated.  In this study, an integrated hospital location hardening is proposed in presence of disruption conditions and facility failures. With respect to the predetermined budget, demands of different points, and some predicting of disruption conditions, the optimizing is done. According to the importance of time in proposing of hospital emergency services, one of the objective functions is minimizing of the maximum allocated distances. Another objective function is minimizing the number of demands, which are allocated to far points.
According to the two-objective mathematical model, the ε-constraint method and the GAMS 24.1.2 software are used. Due to the possibility of using the model in the large dimensional applications as well as the NP-hardness of the model, the NSGA-II meta-algorithm is applied and the efficiency of the proposed approach is examined and evaluated.

Hospital location is a critical issue, that many criteria should be considered for solve it and unlike the past, selecting a location, only in terms of economic criteria, it is difficult for all stakeholders and organization. Therefore, considering the sustainability criteria (criteria of economic, social, environmental) in decisions, may to-be inevitable. Locate critical facilities such as hospitals optimal decision is also a controversial issue that must be done according to criteria of sustainability. On the other hand, because of the importance of the facilities in critical situations (such as war), location of hospital also is a part of passive defense planning. In exist researches about critical site, few sustainable criteria have been considered by passive defense criteria, but in this paper, by check the significant sustainable and passive defense criteria, a total framework are proposed for the first, and by this criteria, optimum location is selected. With classification this criteria, using by Group AHPTOPSIS, this criteria used for construction of hospital in Amol city . Results show the best site for location of hospital with considering defenses and environmental factor.

Reverse logistic and recursive management is one of the important subjects in logistic and supply chain management which is not drastically considered in our country in the last two decades. A lot of companies and industries of advanced countries have begun studies in terms of reverse logistics and considered it as one of the most crucial process in their supply chain. Continues improvement and consumer satisfaction is the primary purpose of indirect logistics system. Reverse logistic network design, as one of the strategic supply chain management levels, which is very principal.
In this paper, an integrated direct and reverse logistic considering production, distribution, customer, devastation, retrieval centers under uncertainty is developed. In this model, cost parameters are not certain which the scenario-based robust optimization method is applied. The aim of the model is to minimize the total cost and obtain robust solution. Finally, a practical case study is presented to verify the proposed model. Moreover an efficient solution algorithm is presented. The computational results illustrate the efficiency of the proposed approach.

The Multiple Facility Location Problem (MFLP) is to locate certain facilities so as to serve a given set of customers, whose locations and requirements are known. When the locations have to be selected from a given set of candidate locations, the problem becomes a Discrete Multiple Facility Location Problem (DMFLP). In this study, a special case of DMFLP is discussed where multiple facilities that are of different type are to be placed (location decision) and assign customers to these facilities (allocation or assignment). Both cases with and without interactions among new facilities are considered in this problem. In this paper, efficient lower and upper bounds are used to propose a branch-and-bound scheme as an exact method. Also, new heuristic method is provided for both cases and compared for large-job sizes. Computational results on randomly generated data in comparison with optimal solutions indicate that the heuristic method is accurate and efficient.