نشریه تصمیم گیری و تحقیق در عملیات
سال پنجم شماره 4 (زمستان 1399)

Corona is currently the world's health crisis and the biggest challenge humans have experienced since World War II. Given the epidemic of the disease, it is invaluable to forecasting the number of cases and the resulting deaths to better understand the current situation and provide a short-term plan by managers. Accordingly, in this paper, a neuro-fuzzy network model is proposed to forecast the number of cases and deaths in countries that are most affected by this disease. The performance of the proposed neuro-fuzzy network has been compared with time series forecasting neural network as well as radial basic functions neural networks. The proposed model is able to predict the number of cases and deaths from the disease for a period of the next 15 days at a lower error rate.

One of the most attractive areas for decision-making in the face of uncertainty is optimal stock portfolio. In decision making for investment, two factors are very important and are the basis of investment. These two factors are risk and return, and in this regard, the study and study of investors to select the best investment portfolio is done according to the amount of risk and its return. A portfolio is a combination of assets formed by an investor to invest. The process of selecting a stock portfolio is one of the issues that have been the focus of many researchers. The aim of this study is to create an optimal stock portfolio using the predicted data. The statistical sample of the research includes the financial data of Iranian stock exchange companies during the years 1390 to 1397. In this study, using stock regression algorithm to predict stock returns, and finally using Markov method and spectral clustering algorithm, the necessary filter to select the appropriate initial data was performed and cultural meta-processing method with prediction data, It provided the optimal portfolio of stocks for the investor group with risk-taking as well as risk-averse. The research results show that the cultural transcendental algorithm, according to Sharp's method, has the ability to create an optimal stock portfolio using predicted data using the Marquis method for venture capitalists and risk averse investors.

Asset- Liability Management (ALM) is an important activity in strategic planning of banks and it can be seen as an optimization issue where banks want to achieve their specific goals. The main purpose of this study is to provide a mathematical model for optimizing the assets and liabilities of Bank Melli Iran under conditions of uncertainty with fuzzy fractional programming model over a period of 10 years (2009-2018). In order to achieve the above goal, 14 items in the assets and liabilities of the bank's balance sheet have been extracted to calculate the 9 variables used in the model and finally, the results obtained from solving the fuzzy fractional programming model in Lingo software environment indicate that the proposed model of the article is able to provide the optimal values ​​of each of balance sheet items for future years according to the conditions of previous years and the value of the objective function for Bank Melli can achieve the desired capital adequacy ratio by considering optimization in asset and liability management decisions.

Many organizational or even day-to-day interactions of individuals are shaped by their decisions, and these decisions sometimes have implications for different stakeholders. In this regard, it is worthwhile to study and analyze the factors affecting ethical decision making. This study aimed to investigate the factors affecting ethical decision making. After identifying and extracting the factors affecting ethical decision making from the literature, the variables were screened by compiling a Delphi-fuzzy questionnaire and the cooperation of eight experts familiar with the field of management and ethics. After performing the necessary repetitions and the saturation limit of the answers, the indicators were finally confirmed. Then, the indicators were analyzed using fuzzy DEMATEL method. Finally, the technique presented the relative importance and relationships between experts by systematically analyzing the opinions of experts. The analysis showed that the variables of "senior managers 'influence", "co-workers' influence" and "emotions / mood" are of great importance among the variables and affect other variables. In contrast, the "spirituality", "business competition" and "decision style" of the decision-maker are less important than other factors. In general, several factors influence the making of a moral decision, but the results indicate that in the individual dimension. The personality, fortifications and values of the decision-maker and in the situational dimension. Reference groups play an important role in making an ethical decision and can guide it. Therefore, more reflection is needed to inform the person who is in the position of decision, more control and more careful monitoring of the outcome of the decision.

Small and medium-sized businesses are a symbol of business development in developed and developing countries, which, if they do not succeed in their careers, require serious attention to solving problems. The aim of this study is to design a model for restarting small and medium-sized entrepreneurial businesses. Research is fundamental in terms of compositional methods and in terms of purpose. The statistical population of the research in the qualitative phase includes experts in the field of entrepreneurship and faculty members of the university, 20 of whom were selected by targeted sampling method and snowball approach to theoretical saturation and in a small section the owners of entrepreneurial businesses failed. 207 companies were selected using the available sampling method. The data collection tool was a semi-structured interview in the qualitative section and a researcher-made questionnaire in the quantitative section. MAXQDA11 quality software was used for qualitative data analysis and SPSS23 and Smart PLS3 statistical software were used for quantitative analysis. Findings showed that lack of experience, lack of reform and developmental attitude, frustration and lack of reinvestment, lack of government financial support, turbulent economic situation, consumer value changes, instability of government laws, lack of motivation and Lack of willingness to re-enter the business and market, insufficient training and lack of irrational advice and targeting, lack of knowledge of the target market, lack of modern technology and lack of value of domestic production, are the most important failure factors identified in this study.

The Qualitative Content Analysis (QCA) as one of the qualitative research methods is a set of techniques used to analyze and clarify textual data. In the subject literature, several processes have been introduced to implement this approach, since the variety and multiplicity of used steps, have made it difficult to select the appropriate process. Due to the lack of observation of a study that explores and summarizes various processes, here, along with introducing and reviewing some of the most important presented processes, by summarizing and refining the steps used, an improved process framework for implementing it is presented. In order to provide an improved process framework, the Meta Synthesis method (MSM) has been used. The proposed process framework that consists of six main steps and sixteen sub-steps, as compared with other common processes, takes advantages such as having the helpful steps and sub-steps used in previous processes, eliminating unnecessary sub-steps, more specific sequencing between steps and sub-steps, and finally, creating two-way relationships and giving feedback between steps that enhance the simplification, flexibility and reliability of the process and its findings. Subsequently, as a case study, in the process of developing strategic planning of Payame Noor University, in the development of directional pillars, the related upstream documents were analyzed using the proposed process and the results were approved by the team of related experts.

The Internet of Things (IoT) is a system of computers, computing machines, digital and mechanical devices, humans and animals, each with an independent code and as a distinct device, in the network and a significant share of information exchange and behavior and the relationship between them is done without direct human intervention and supervision. Businesses are no exception and try to use the IoT to make business smarter. The supply chain is one of the most vital parts of the business cycle and its smartening creates a dramatic change in the optimization of business processes. However, since this technology is based on the Internet and the use of the Internet can always be associated with different risks, so recognizing the cyber risks that can be faced by businesses in the face of these technologies is always significant IoT-based supply chain cyber to be identified and analyzed. In order to rank these risks, nonlinear mathematical modeling method has been used. The results show that privacy in interaction with suppliers as well as customers is the most important and effective measures should be taken to address these risks.

Due to the complex and multifaceted nature of supply chain resilience, hasn't yet been proposed a comprehensive, concrete, and aggregative model that includes the prevailing consensus of researchers in this field. In order to try to achieve that, the present study was conducted with the aim of forming a comprehensive model for supply chain resilience assessment using an integrated approach based on scientometrics and various artificial intelligence methods based on knowledge extraction from the text. The statistical population includes all indexed articles related to supply chain resilience from 2002 to 2020 in the two scientific databases Scopus and WOS. During the three stages of document refinement with a systematic review approach, scientometric information, and the full text of 346 articles were extracted and used in the analysis process. Utilizing an integrated approach based on the fusion of scientometrics of related metadata, and artificial intelligence tools to extraction of supply chain resilience assessment tool obtain the main innovation of this research which makes feasible establishing an evaluation model without interfering with researcher source biases. Finally, the supply chain resilience evaluation model including 4 main structures and 25 sub-structures was extracted from related scientific documents.