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The main purpose of this paper is to investigate the effects of COVID-19 regarding the efficiency of industries based on data in the Tehran stock market. A hybrid model of Data Envelopment Analysis (DEA) and data mining techniques is used to analyze the investment behavior in Tehran stock market. Particularly during the COVID-19 pandemic, many companies face financial crises. That is why companies with inferior performance must be benchmarked with efficient companies. First, the financial data of investments on selective companies are analyzed using data mining approaches to recognize the behavioral patterns of investors and securities. Second, customers are clustered into 3 selling and 4 buying groups using data mining techniques. Then, the efficiency of active companies in stock exchange is evaluated using input-oriented DEA. The results indicate that, among 23 industries listed on the stock market in Iran, solely nine were efficient in 2019. Moreover, in 2020, the number of efficient industries further decreased to six industries. Comparing the obtained results with those of another study which was conducted in 2018 by other researchers revealed that COVID-19 strongly affects the performance of an industry and some industries which were efficient in the past such as the bank industry became inefficient in the following year. [ABSTRACT FROM AUTHOR]

The focus of this paper is on selecting suppliers in the Oil and Gas (O&G) industry by developing a comprehensive approach for Multi-Criteria Decision Analysis (MCDA) based on alternative methods capable of assessing different aspects of supplier selection uncertainty in terms of utility functions and criteria related to efficiency. The O&G industry has a key role in the public sector of various countries such as Iran with its revenues being of prime importance to develop infrastructure facilities such as for healthcare, education, and transportation. This comprehensive approach walks through various stages for selecting Critical Success Factors (CSFs), ranking suppliers, and for setting partial weighting alternatives. While CSFs are selected using a traditional Delphi approach, the partial supplier rankings are defined based on Complex Proportional Assessment (COPRAS) utility functions together with criteria weights derived from Step-wise Weight Assessment Ratio Analysis (SWARA) for each CSF. As it concerns information reliability of utility and efficiency functions of both methods obtained via expert preferences or perceptions, Z-numbers are used to address the intrinsic fuzziness level inherent to each analytical stage. Iran's economy depends on revenues from oil and other related production, which means that by earning more income from this industry, most of its economic indicators such as GDP and employment rate should increase significantly, thus leading to economic growth. Various countries put plans in place related to production for increasing their social economics. One of these plans is focused on suppliers since they have a high impact on providing essential items such as equipment, HR, and transportation, so by choosing the best suppliers in all fields, costs will decrease and consequently revenue will increase. This research points out how to rank O&G industry suppliers using MCDA methods in an uncertain environment. An example based on actual data from an Iranian O&G company is provided to show the applicability of the approach proposed. Results suggest that the complexity of O&G operations on selecting suppliers can be adequately handled by information reliability techniques applied to traditional economic concepts such as utility- and efficiency-related factors, particularly in business environments characterized by a trade embargo. • The paper focuses on selecting O&G suppliers using a comprehensive MCDM. • Alternative ranks are defined by COPRAS and criteria weights are defined by SWARA. • Z-numbers are used to address the intrinsic reliability level of each analytical stage. • Results suggest that information reliability of alternative suppliers and weights can represent complex environments. [ABSTRACT FROM AUTHOR]