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Technology transfer plays an essential role in developing an organization's capabilities to perform better in the market. Several protocols are defined for technology transfer. One of the main techniques in technology transfer is licensing, which significantly impacts profit and income. This study intends to develop a decision framework that integrates both a Fuzzy Inference System (FIS) and a two steps Fuzzy Quality Function Deployment (F-QFD) to assist an organization in selecting a licensor. To illustrate the decision framework's performance, it has been implemented in an Iranian lubricant producer to select the best licensor among the 13 targeted companies. A complete product portfolio, brand image enhancement, increasing the market share of the high-value products, and improving the technical knowledge of manufacturing products were identified as the most important expectations of the licensees. A sensitivity analysis for the recommended framework has been conducted. For doing so, 27 rules of the FIS were categorized into four group and then changed. The results are compared using the Pearson correlation coefficient. Inference rules detect unconventional changes, while logical changes are appropriately considered. [ABSTRACT FROM AUTHOR]

Evaluation of suppliers is essential to increasing competitive power, customer satisfaction, and profitability. Oil and gas companies can use this research to evaluate suppliers and map the potential path forward for future collaborations. Six supply chain managers in Iran designed HFIS for the oil and gas industry. Shannon Entropy was used to determine the relative weights of suppliers concerning overall uncertainty because the Oil and Gas industry uses many unstructured Key Performance Indicators (KPIs). Using Matlab Toolbox FIS, a future cooperation roadmap was developed. Experts suggested future collaboration with certain suppliers based on the HFIS results. The future cooperation strategy proposed by the framework is highly in line with their expectations. FIS results indicate that the proposed can help select the most appropriate suppliers for cooperation while providing a roadmap for weaker suppliers to improve their performance. [ABSTRACT FROM AUTHOR]

The aim of this research is to find and prioritize a multicriteria performance measurement based on the balanced scorecard (BSC) for oil and gas (O & G) companies in an uncertain environment using the hesitant fuzzy best-worst method (HFBWM). The O & G industry has a key role in the economies of many countries. Hence, the evaluation of the performance of the O & G industry plays an important role. We utilize BSC for this purpose, which usually considers the financial, customer-oriented, internal, learning-oriented, and growth perspectives. In our research, the social responsibility perspective will be added. After finding multiple performance measurements, many companies cannot implement all of them because of limited resources. Therefore, multicriteria decision-making (MCDM) methods can be applied for prioritizing and selecting the most important measurement criteria. One of the MCDM methods is the best-worst method (BWM). This approach has several advantages compared to other MCDM methods. Due to uncertainties in decision-making, a suitable method for decision-making in an uncertain environment is necessary. Hesitant fuzzy approaches are applied as one such uncertainty-based method in this research. Our results indicate that among the five perspectives of BSC that we considered, the customer and internal process perspectives are the most important ones, and the cost of the R & D indicator is the most important subcriterion among these. [ABSTRACT FROM AUTHOR]

Oil and gas (O&G) projects have a high social and economic impact in many countries. This study aims to identify the success factors in the O&G industry and to present a framework for evaluating the projects in an uncertain environment. In this study, 197 indicators are identified from a literature review and summarized to 34 indicators. Exploratory factor analysis (EFA) was performed, and seven main factors have been identified as success factors of O&G projects. Confirmatory factor analysis (CFA) confirmed the validity of the measurement model. A two-step fuzzy inference system is proposed to present an evaluation framework for assessing the performance of O&G projects. The hybrid framework was applied for the assessment of five Iranian O&G projects. [ABSTRACT FROM AUTHOR]

Cross-docking is the main operation of unloading products from incoming trucks, regrouping products in relation to their destination, and loading directly onto shipping trucks, reducing warehousing, picking, transportation costs, and delivery times. This is the intended logistics technology. In this paper, we present a new bi-objective mixed-integer mathematical model for truck scheduling problems in cross-docking systems. The goal of the proposed mixed-integer mathematical model is to minimize the total operation time (makespan) and cost of moving cargo within the terminal. The performance of the proposed model is compared with that of the available model to solve small instances. The results showed that in solving small size of problem, the proposed model in this study is more efficient and we found better solutions. An evolutionary algorithm called the nondominated sorting genetic algorithm (NSGA-II) has been proposed to solve larger instances due to computational complexity. To evaluate the proposed algorithm, a comparative analysis of benchmark instances was performed and the efficiency of the above algorithm was compared to the nondominated ranked algorithm (NRGA) based on the index designed in the literature. The statistical hypothesis testing (t-test) is used for determining the best algorithm based on the average runtime and average number of Pareto solutions. Using the Taguchi method, the proposed algorithms are tuned. Considering a temporary storage space and the multiple receiving and shipping docks is the main contribution of the paper. Finally, for evaluating algorithms, multicriteria decision-making (MCDM) technique and statistical method are used. The results show the suitable performance of presented model. [ABSTRACT FROM AUTHOR]

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]