An integrated method for airline company supplier selection based on the entropy and vikor methods: a real case study

All certified airlines require to implement a safety and quality management system. Therefore, the quality of all services and products with critical operational domains have been challenging issues in the aviation industry. In this regard, supplier selection plays an important role to acquire competitive benefits. Flight operations is critical scope in an airline and their outputs have a direct impact on flight safety consequences. Therefore, the quality of supplier’s product and services play the main role in their flight operations process. In this research, a new decision-making framework is developed to evaluate the performance of the suppliers based on the Entropy and VIKOR approaches. At the outset, the main criteria and sub-criteria are identified based on the literature and expert\u27s viewpoint and then their weights are calculated using the Entropy method. Afterward, the potential suppliers are ranked using the VIKOR method. The airline supplier’s assessment through expert judgment and integrated criteria are the new approaches that are developed in this paper. The obtained results show that economic, quality and safety, and reputation respectively are the main criteria to select suppliers

Identification and Prioritization of Factors Affecting E-teacher’s Performance based on Fuzzy Analytic Hierarchy Process (AHP)

Introduction: Information and communication technology has changed the traditional role of teachers and learners. It’s important to detect factors influencing e-teachers’ role to improve their performance more than ever. So in this study we identified and prioritized factors influencing e-teacher’s effective performance. Methods: In this descriptive study, 15 e-learning experts from Tehran University of Medical Sciences were selected through purposeful sampling. Having focus group sessions held, their viewpoints about main factors affecting e-teacher’s performance were collected. In order to collect the views toward the significance of each criterion, a questionnaire was developed regarding the main criterion and its subordinates in which there was a comparison table for each criterion. Then Fuzzy Analytic Hierarchy Process decision making was used for prioritizing and weighting factors (ie., criteria). Results: Totally 5 factors influencing e-teachers’ performance were extracted. The factor of professional knowledge had the highest weight (0.28) was recognized as the most important factor. This factor did not have any subordinate. Technology with the lowest weight (0.137) was identified as the least important factors affecting e-teacher’s performance. Factors (and their most important subordinates with the highest coefficients) were as follows: skillfulness in e-teacher role playing (mastery in pedagogy), technology (mastery in information technology), management (management of virtual classroom), and personality factor (interest in virtual training). Conclusion: In order to select an e-teacher and develop his capabilities, it must be considered that IT expertise is of the least importance and teachers who encompass factors such as professional knowledge and are able to act as an effective teacher, are also able to act as a successful e-teacher

A dynamic risk assessment modeling based on fuzzy ANP for safety management systems

Risk assessment in large organizations with extensive operational domains has been a challenging issue. Employing an efficient method along with realistic pair comparisons, applying subjective inferences of organization experts, and purging the intrinsic ambiguity of inferences, are not reflected in current airlines' safety management. Traditional two-dimensional risk assessment for risk management of safety hazards, however, is no longer sufficient to comply with this complexity. A new model for risk management and a novel formula for risk index calculation, based on a fuzzy approach, are presented in this study. In this new model, unlike in the traditional approach, the latent aftermath of safety reports, especially those which affect the continuity of the business, is also taken into account. In this model, along with the definition of a new structure for risk management, risk analysis should be restructured. To that end, a two-dimensional classic risk formula was replaced with three-dimensional (nonlinear) exponential ones, considering “the impact on the business” as a source of risk and hazard. For measuring the safety risk using the Fuzzy hierarchical evaluation method, considering experts' opinions, three criteria in four different operational fields were developed. This method employs a Fuzzy ANP to help quantify judgments, make qualitative judgments in the traditional method, and weigh the priority of elements contributing to risk. Also, it provides a tool for top-level as well as expert level management to monitor safety more precisely, monitor the safety level within their departments or organizations, set quantitative safety goals and provide feedback for improvement as well as find the most critical areas with the least cost. In this study, an airline has been selected as a case study for the risk assessment of reports based on the new model

Cockpit crew safety performance prediction based on the integrated machine learning multi-class classification models and Markov chain

The main tool of cockpit crew performance evaluation is the recorded flight data used for flight operations safety improvement since all certified airlines require implementation of a safety and quality management system. The safety performance of a flight has been a challenging issue in the aviation industry and plays an important role to acquire competitive benefits. In this study, an integrated multi-class classification machine learning models and Markov chain were developed for cockpit crew performance evaluation during their flights. At the outset, the main features related to a flight are identified based on the literature review, flight operations expert’s statements, and the case study dataset (as numerical example). Afterwards, the flights’ performance is evaluated as a target column based on four multi-class classification models (Decision Tree, Support Vector Machine, Neural Network, and Random Forest). The results showed that the random forest classifier has the greatest value in all evaluation metrics (i.e., accuracy = 0.90, precision = 0.91, recall = 0.97, and F1-score = 0.93). Therefore, this model can be used by the airline companies to predict flight crew performance before the flight in order to prevent or decrease flight safety risks