Application of the ANP in the interview phase of air traffic controller candidate selection process

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mustafa Özdemir ◽  
Mujgan Sagir
Keyword(s):  
Selection Process ◽  
Current System ◽  
Critical Issue ◽  
Analytic Network Process ◽  
Small Error ◽  
Air Traffic ◽  
Air Traffic Controller ◽  
Content Type ◽  
Interview Process ◽  
Rating Method

Purpose This paper investigates the interview examination applied during the admission process of air traffic controller (ATCO) candidates. This paper aims to select the most appropriate candidates by minimizing any shortcomings of the process. Design/methodology/approach ATCOs have a very important role in the air traffic system. They are responsible for ensuring the safe, regular and rapid flow of aircraft traffic. They carry out this challenging task by monitoring the aircraft under their responsibility and by giving instructions to the pilots when necessary. So the selection of ATCO candidates is of critical importance. This process is usually conducted through multistage examinations. It is a critical issue to use correct methods in the selection process to identify the most suitable candidates. Besides, the application of subjective examination in a standard way and standardization of criteria can assist in selecting the right candidates. Within this context, the analytic network process (ANP) and the rating method are used. The weights of the selection criteria are calculated by the ANP, and the candidates are evaluated by the rating method according to the defined criteria. Findings 39 candidates were ranked using the ANP, and the rankings obtained by the ANP and the current system were compared. The ANP rankings were also compared with the results of a previous study conducted by analytic hierarchy process (AHP). The results indicate that using the ANP yields consistent results with those obtained from the AHP in terms of the rankings. Research limitations/implications ATCOs are one of the most important operators in air transport system. They undertake critical tasks where even a small error may cause serious consequences. Therefore, selecting the appropriate candidate is an important first step in training qualified staff. The authors believe that this study will contribute to the training of more qualified ATCOs. On the other hand, the approach about the use of ANP method on the interview examination and the standardization of the criteria can provide insight into improving interview process in different area. Originality/value To the best of the authors’ knowledge, this is the first study that presents an ANP-based methodology for the selection process of ATCOs and the interview process.

Predictive Validity of a Selection Procedure for Air Traffic Controller Trainees

2013 ◽  
Vol 3 (1) ◽  
pp. 19-27 ◽  
Author(s):  
Yvonne Pecena ◽  
Doris Keye ◽  
Kristin Conzelmann ◽  
Dietrich Grasshoff ◽  
Peter Maschke ◽  
...  
Keyword(s):  
Predictive Validity ◽  
Selection Procedure ◽  
Air Traffic ◽  
Performance Criteria ◽  
Training Phase ◽  
Air Traffic Controller ◽  
Training Performance ◽  
Navigation Service ◽  
German Aerospace ◽  
Training Success

The job of an air traffic controller (ATCO) is very specific and demanding. The assessment of potential suitable candidates requires a customized and efficient selection procedure. The German Aerospace Center DLR conducts a highly selective, multiple-stage selection procedure for ab initio ATCO applicants for the German Air Navigation Service Provider DFS. Successful applicants start their training with a training phase at the DFS Academy and then continue with a unit training phase in live traffic. ATCO validity studies are scarcely reported in the international scientific literature and have mainly been conducted in a military context with only small and male samples. This validation study encompasses the data from 430 DFS ATCO trainees, starting with candidate selection and extending to the completion of their training. Validity analyses involved the prediction of training success and several training performance criteria derived from initial training. The final training success rate of about 79% was highly satisfactory and higher than that of other countries. The findings demonstrated that all stages of the selection procedure showed predictive validity toward training performance. Among the best predictors were scores measuring attention and multitasking ability, and ratings on general motivation from the interview.

Air Traffic Controller Situation Awareness Is Embodied

2014 ◽  
Author(s):  
Dan Chiappe ◽  
Thomas Strybel ◽  
Kim-Phuong Vu ◽  
Lindsay Sturre
Keyword(s):  
Situation Awareness ◽  
Air Traffic ◽  
Air Traffic Controller

Estimating Fatigue From Simulated Air Traffic Controller Communication

2011 ◽  
Author(s):  
Jarek Krajewski ◽  
David Sommer ◽  
Sebastian Schnieder ◽  
Martin Golz
Keyword(s):  
Air Traffic ◽  
Air Traffic Controller

Perceived value of internship experience: a try before you leap

2018 ◽  
Vol 8 (4) ◽  
pp. 376-394 ◽  
Author(s):  
Sonali Bhattacharya ◽  
Netra Neelam
Keyword(s):  
Experiential Learning ◽  
Performance Feedback ◽  
Work Experience ◽  
Selection Process ◽  
Learning Orientation ◽  
Research Approach ◽  
Content Type ◽  
Internship Experience ◽  
And Performance ◽  
Task Variety

Purpose The purpose of this paper is to examine how internship value is manifested in the context of a business school. The authors have examined the internship experience in terms of experiential learning and employability. Specifically, the authors investigate the factors that determine internship at four phases: design, conduct, evaluation and feedback. Design/methodology/approach The authors have applied a mixed method approach. In all, 110 students of a busines school were first surveyed on their expectation, motivation and level of preparation through a self-administered questionnaire before internship. Based on the survey result, eight of these students were interviewed in details about internship expectations from industry, the selection process for internship, communications or exchanges between intern and companies prior to internship and perceived industry expectation from interns. At the next phase, authors used a qualitative research approach by conducting semi-structured, in-depth interviews with 14 interns and their mentors after internship period. They were interviewed on design, conduct, evaluation and feedback process of the internship. Interviews tried capture what kind of leader-member exchange led to satisfactory internship experience and outcome from view of both inter and mentor. Findings The authors find that at various stages of internship program quality of mentor – intern exchanges (as defined by leadership exchange theory), and task characteristics as indicated by autonomy, task variety, task significance and performance feedback determine intern’s performance. An intern’s performance is antecedent to an intern’s and a mentor’s satisfaction and overall internship value. The authors also found that intrinsic capability of intern such as critical thinking ability and learning orientation result in enhanced value of internship experience. The proposed models, postulate that at designing stage, lower the level of communication from employers, higher the feeling of ambiguity and lower the perceived internship value in terms of experiential learning and perceived employability. Feeling of ambiguity is moderated by existence of prior work experience of interns. At conduction stage, mentor-intern exchange is directly related to flexibility in structure of the program and inversely related to dependency on peer learning. Mentor-intern exchange also related to mentor and intern’s learning value. However, the learning value is moderated by learning orientation of the intern. Originality/value The authors have tried the summer internship experience from the perspective of interns and mentors. This is the uniqueness of the research.

Adaptive neuro-fuzzy fusion of multi-sensor data for monitoring of CNC machining

Sensor Review ◽  
2017 ◽  
Vol 37 (1) ◽  
pp. 78-81 ◽  
Author(s):  
Srdjan Jovic ◽  
Obrad Anicic ◽  
Milivoje Jovanovic
Keyword(s):  
Selection Process ◽  
Spindle Speed ◽  
Cnc Machining ◽  
Numerical Control ◽  
Periodic Component ◽  
Sensor Data ◽  
Depth Of Cut ◽  
Inference System ◽  
Content Type ◽  
Neuro Fuzzy

Purpose Acoustic emission (AE) could be used for prevention and detection of tool errors in Computer Numerical Control (CNC) machining. The purpose of this study is to analyze the AE form of CNC machining operations. Design/methodology/approach Experimental measurements were performed with three sensors on the CNC lathe to collect the data of the CNC machining. Adaptive neuro-fuzzy inference system (ANFIS) was applied for the fusion from the sensors’ signals to determine the strength of the signal periodic component among the sensors. Findings There were three inputs, namely, spindle speed, feed rate and depth of cut. ANFIS was also used to determine the inputs’ influence on the prediction of strength of the signal periodic component. Variable selection process was used to select the most dominant factors which affect the prediction of strength of the signal periodic component. Originality/value Results were shown that the spindle speed has the most dominant effect on the strength of the signal periodic component.

ERP software selection using intuitionistic fuzzy and interval grey number-based MACBETH method

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Mustafa Said Yurtyapan ◽  
Erdal Aydemir
Keyword(s):  
Decision Making ◽  
Fuzzy Set ◽  
Selection Process ◽  
Intuitionistic Fuzzy Set ◽  
Decision Makers ◽  
Software Selection ◽  
Content Type ◽  
Intuitionistic Fuzzy ◽  
Expert Opinions ◽  
Erp Software

PurposeEnterprise Resource Planning (ERP) software which is a knowledge-based design on the interconnective communication of business units and information share, ensures that business processes such as finance, production, purchasing, sales, logistics and human resources, are integrated and gathered under one roof. This integrated system allows the company to make fast and accurate decisions and increases its competitiveness. Therefore, for an enterprise, choosing the suitable ERP software is extremely important. The aim of this study is to present new research on the ERP software selection process by clarifying the uncertainties and find suitable software in a computational way.Design/methodology/approachERP selection problem design includes uncertainties on the expert opinions and the criteria values using intuitionistic fuzzy set theory and interval grey-numbers to MACBETH multi criteria decision making method. In this paper, a new interval grey MACBETH method approach is proposed, and the degree of greyness approach is used for clarifying the uncertainties. Using this new approach in which grey numbers are used, it is aimed to observe the changes in the importance of the alternatives. Moreover, the intuitionistic fuzzy set method is applied by considering the importance of expert opinions separately.FindingsThe proposed method is based on quantitative decision making derived from qualitative judgments. The results given under uncertain conditions are compared with the results obtained under crisp conditions of the same methods. With the qualitative levels of experts reflected in the decision process, it is clearly seen that ERP software selection problem area has more effective alternative decision solutions to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during ERP software selection process.Originality/valueThis study contributes to the relevant literature by (1) utilizing the MACBETH method in the selection of the ERP software by optimization, and (2) validating the importance of expert opinions with uncertainties on a proper ERP software selection procedure. So, the findings of this study can help the decision-makers to evaluate the ERP selection in uncertain conditions.

A systematic review of machine learning-based missing value imputation techniques

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Tressy Thomas ◽  
Enayat Rajabi
Keyword(s):  
Machine Learning ◽  
Selection Process ◽  
Evaluation Metrics ◽  
Correct Prediction ◽  
Data Sets ◽  
Data Set ◽  
Missing Value ◽  
Content Type ◽  
Missing Value Imputation ◽  
Literature Reviews

PurposeThe primary aim of this study is to review the studies from different dimensions including type of methods, experimentation setup and evaluation metrics used in the novel approaches proposed for data imputation, particularly in the machine learning (ML) area. This ultimately provides an understanding about how well the proposed framework is evaluated and what type and ratio of missingness are addressed in the proposals. The review questions in this study are (1) what are the ML-based imputation methods studied and proposed during 2010–2020? (2) How the experimentation setup, characteristics of data sets and missingness are employed in these studies? (3) What metrics were used for the evaluation of imputation method?Design/methodology/approachThe review process went through the standard identification, screening and selection process. The initial search on electronic databases for missing value imputation (MVI) based on ML algorithms returned a large number of papers totaling at 2,883. Most of the papers at this stage were not exactly an MVI technique relevant to this study. The literature reviews are first scanned in the title for relevancy, and 306 literature reviews were identified as appropriate. Upon reviewing the abstract text, 151 literature reviews that are not eligible for this study are dropped. This resulted in 155 research papers suitable for full-text review. From this, 117 papers are used in assessment of the review questions.FindingsThis study shows that clustering- and instance-based algorithms are the most proposed MVI methods. Percentage of correct prediction (PCP) and root mean square error (RMSE) are most used evaluation metrics in these studies. For experimentation, majority of the studies sourced the data sets from publicly available data set repositories. A common approach is that the complete data set is set as baseline to evaluate the effectiveness of imputation on the test data sets with artificially induced missingness. The data set size and missingness ratio varied across the experimentations, while missing datatype and mechanism are pertaining to the capability of imputation. Computational expense is a concern, and experimentation using large data sets appears to be a challenge.Originality/valueIt is understood from the review that there is no single universal solution to missing data problem. Variants of ML approaches work well with the missingness based on the characteristics of the data set. Most of the methods reviewed lack generalization with regard to applicability. Another concern related to applicability is the complexity of the formulation and implementation of the algorithm. Imputations based on k-nearest neighbors (kNN) and clustering algorithms which are simple and easy to implement make it popular across various domains.

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