American Research Journal of Humanities & Social Science (ARJHSS) is a double blind peer reviewed, open access journal published by (ARJHSS).
The main objective of ARJHSS is to provide an intellectual platform for the international scholars. ARJHSS aims to promote interdisciplinary studies in Humanities & Social Science and become the leading journal in Humanities & Social Science in the world.
The document describes modeling and forecasting of tur production in India using the ARIMA model. Time series data of tur production from 1950-1951 to 2014-2015 was analyzed using time series methods. The autocorrelation and partial autocorrelation functions were calculated and the Box-Jenkins ARIMA methodology was used. The ARIMA(1,1,1) model was found to be appropriate based on diagnostic checking. Forecasts of tur production from 2015-2016 to 2024-2025 were then calculated using the selected ARIMA(1,1,1) model. The forecasts could help policymakers plan for future requirements of tur seed, imports, and exports.
The document analyzes the energy consumption for cucumber greenhouse production in Iran using data envelopment analysis. Data was collected from 20 greenhouses and energy inputs (like diesel, fertilizer, labor) and outputs (cucumber yield) were calculated. Total energy input was 163,994 MJ/ha with diesel fuel as the highest at 45.15%. Output was 62,496 MJ/ha. Technical, pure technical and scale efficiencies were then calculated using DEA to evaluate energy efficiency and identify areas for improvement. The study found DEA to be useful for benchmarking energy use and determining how to reduce waste.
Selecting Best Tractor Ranking Wise by Software using MADM(Multiple –Attribut...IRJET Journal
This document discusses using a multi-attribute decision making (MADM) technique called TOPSIS to select the best tractor among alternatives. It begins with an introduction to MADM problems and the TOPSIS method. The TOPSIS method involves normalizing a decision matrix, determining ideal and negative ideal alternatives, calculating distances from each alternative to the ideals, and ranking alternatives based on relative closeness. The document then applies this process to select the best tractor among 6 models, using attributes like price, power, fuel efficiency, and maintenance cost to evaluate the alternatives. It aims to help buyers select the tractor best suited to their application needs based on technical specifications.
Implementation of AHP and TOPSIS Method to Determine the Priority of Improvi...AM Publications
Many problems of asset management based on audit results is an indicator of weakness implementation of asset management which adversely affects of the Audit Board of the Republic of Indonesia. All evaluation of the implementation of asset management is the first step in improving the quality of asset management. This study aims to build decision support system to help problems solving prioritization improved management of government assets with AHP and TOPSIS methods. Integration of AHP and TOPSIS methods are used to perform weighting and ranking alternatives. Weights are obtained by comparison of the level of interest criteria carried out by experts ranked. While alternative methods produced are based on the calculation method in which the best alternative has the shortest distance from the positive ideal solution and the farthest from the negative ideal solution. Alternative asset management is a low priority in the increasing in asset management. The results of this analysis, a system is used for prioritization based on defined criteria. The test results shows that the system can provide an alternative sequence that has an accuracy rate of 83% and has an average value of 4.91 of an evaluation system 5-point scale with the aspect of effectiveness, efficiency and user satisfaction.
Grant Selection Process Using Simple Additive Weighting Approachijtsrd
Selection of educational grant is a key success factor for student learning and academic performance. Among popular methods, this paper contributes a real problem of selecting educational grant using data of grant application forms by one of the multi criteria decision making model, SAW method. This paper introduces nine criteria that are qualitative and positive for selecting grant for the students amongst fifteen application forms and also ranking them. Finally, the proposed method is demonstrated in a case study on selecting educational grant for students. Kyi Kyi Myint | Tin Tin Soe | Myint Myint Toe ""Grant Selection Process Using Simple Additive Weighting Approach"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25169.pdf
Paper URL: https://www.ijtsrd.com/computer-science/simulation/25169/grant-selection-process-using-simple-additive-weighting-approach/kyi-kyi-myint
ANALYSIS OF ROADWAY FATAL ACCIDENTS USING ENSEMBLE-BASED META-CLASSIFIERSijaia
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.
Analysis of Roadway Fatal Accidents using Ensemble-based Meta-Classifiersgerogepatton
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining,
the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents.
This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques
known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also,
we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu
on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this
regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that
varies between 96% to 99% using 7-15 features instead of 50 original features.
CONTAINER TRAFFIC PROJECTIONS USING AHP MODEL IN SELECTING REGIONAL TRANSHIPM...IAEME Publication
Shipping is a major link between the global economy and international trade. More than 90% of world merchandise trade is carried by sea and over 60% of that volume is containerized. The increasing number of container shipments causes higher demands on the seaport container terminals, container logistics and management as well as on technical equipment. In the Asian region, the existence of ports such as Singapore and trade evolving from developing countries makes it one of the busiest container sea routes in the world. The average vessel size registered in 2008 was approx 3400 TEU’s as against 2500 TEU’s in 2001.
The document describes modeling and forecasting of tur production in India using the ARIMA model. Time series data of tur production from 1950-1951 to 2014-2015 was analyzed using time series methods. The autocorrelation and partial autocorrelation functions were calculated and the Box-Jenkins ARIMA methodology was used. The ARIMA(1,1,1) model was found to be appropriate based on diagnostic checking. Forecasts of tur production from 2015-2016 to 2024-2025 were then calculated using the selected ARIMA(1,1,1) model. The forecasts could help policymakers plan for future requirements of tur seed, imports, and exports.
The document analyzes the energy consumption for cucumber greenhouse production in Iran using data envelopment analysis. Data was collected from 20 greenhouses and energy inputs (like diesel, fertilizer, labor) and outputs (cucumber yield) were calculated. Total energy input was 163,994 MJ/ha with diesel fuel as the highest at 45.15%. Output was 62,496 MJ/ha. Technical, pure technical and scale efficiencies were then calculated using DEA to evaluate energy efficiency and identify areas for improvement. The study found DEA to be useful for benchmarking energy use and determining how to reduce waste.
Selecting Best Tractor Ranking Wise by Software using MADM(Multiple –Attribut...IRJET Journal
This document discusses using a multi-attribute decision making (MADM) technique called TOPSIS to select the best tractor among alternatives. It begins with an introduction to MADM problems and the TOPSIS method. The TOPSIS method involves normalizing a decision matrix, determining ideal and negative ideal alternatives, calculating distances from each alternative to the ideals, and ranking alternatives based on relative closeness. The document then applies this process to select the best tractor among 6 models, using attributes like price, power, fuel efficiency, and maintenance cost to evaluate the alternatives. It aims to help buyers select the tractor best suited to their application needs based on technical specifications.
Implementation of AHP and TOPSIS Method to Determine the Priority of Improvi...AM Publications
Many problems of asset management based on audit results is an indicator of weakness implementation of asset management which adversely affects of the Audit Board of the Republic of Indonesia. All evaluation of the implementation of asset management is the first step in improving the quality of asset management. This study aims to build decision support system to help problems solving prioritization improved management of government assets with AHP and TOPSIS methods. Integration of AHP and TOPSIS methods are used to perform weighting and ranking alternatives. Weights are obtained by comparison of the level of interest criteria carried out by experts ranked. While alternative methods produced are based on the calculation method in which the best alternative has the shortest distance from the positive ideal solution and the farthest from the negative ideal solution. Alternative asset management is a low priority in the increasing in asset management. The results of this analysis, a system is used for prioritization based on defined criteria. The test results shows that the system can provide an alternative sequence that has an accuracy rate of 83% and has an average value of 4.91 of an evaluation system 5-point scale with the aspect of effectiveness, efficiency and user satisfaction.
Grant Selection Process Using Simple Additive Weighting Approachijtsrd
Selection of educational grant is a key success factor for student learning and academic performance. Among popular methods, this paper contributes a real problem of selecting educational grant using data of grant application forms by one of the multi criteria decision making model, SAW method. This paper introduces nine criteria that are qualitative and positive for selecting grant for the students amongst fifteen application forms and also ranking them. Finally, the proposed method is demonstrated in a case study on selecting educational grant for students. Kyi Kyi Myint | Tin Tin Soe | Myint Myint Toe ""Grant Selection Process Using Simple Additive Weighting Approach"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-4 , June 2019, URL: https://www.ijtsrd.com/papers/ijtsrd25169.pdf
Paper URL: https://www.ijtsrd.com/computer-science/simulation/25169/grant-selection-process-using-simple-additive-weighting-approach/kyi-kyi-myint
ANALYSIS OF ROADWAY FATAL ACCIDENTS USING ENSEMBLE-BASED META-CLASSIFIERSijaia
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining, the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents. This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also, we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that varies between 96% to 99% using 7-15 features instead of 50 original features.
Analysis of Roadway Fatal Accidents using Ensemble-based Meta-Classifiersgerogepatton
In the past decades, a lot of effort has been put into roadway traffic safety. With the help of data mining,
the analysis of roadway traffic data is much needed to understand the factors related to fatal accidents.
This paper analyses Fatality Analysis Reporting System (FARS) dataset using several data mining
algorithms. Here, we compare the performance of four meta-classifiers and four data-oriented techniques
known for their ability to handle imbalanced datasets, entirely based on Random Forest classifier. Also,
we study the effect of applying several feature selection algorithms including PSO, Cuckoo, Bat and Tabu
on improving the accuracy and efficiency of classification. The empirical results show that the Threshold
selector meta-classifier combined with over-sampling techniques results were very satisfactory. In this
regard, the proposed technique has gained a mean overall Accuracy of 91% and a Balanced Accuracy that
varies between 96% to 99% using 7-15 features instead of 50 original features.
CONTAINER TRAFFIC PROJECTIONS USING AHP MODEL IN SELECTING REGIONAL TRANSHIPM...IAEME Publication
Shipping is a major link between the global economy and international trade. More than 90% of world merchandise trade is carried by sea and over 60% of that volume is containerized. The increasing number of container shipments causes higher demands on the seaport container terminals, container logistics and management as well as on technical equipment. In the Asian region, the existence of ports such as Singapore and trade evolving from developing countries makes it one of the busiest container sea routes in the world. The average vessel size registered in 2008 was approx 3400 TEU’s as against 2500 TEU’s in 2001.
Linear modeling in Transportation problem: Need of reliable Public transporta...IOSR Journals
1) The document discusses assessing the efficiency of public transportation systems in Madhya Pradesh, India using data envelopment analysis. It examines inputs like employees, buses, and bus kilometers, and output like total revenue.
2) Results from the data envelopment analysis indicate need for improvement in the public transportation system. Efforts should be made to improve productivity and efficiency of operators to enhance quality and quantity of services.
3) The document also discusses using discrete choice modeling to analyze socioeconomic factors influencing mobility and evaluating approaches to reduce air pollution from road transportation.
This document proposes a decision support system to help a production company determine the best country for a new investment abroad. It involves 4 phases: 1) determining criteria for evaluating candidate countries, 2) using AHP to assign weights to the criteria, 3) evaluating 50 candidate countries based on the criteria, selecting the top 10, and 4) further evaluating the top 10 using another AHP model to select the top 5 investment alternatives. The system aims to provide a comprehensive, scientific approach for solving the investment location problem.
The factors determining the profitability of low cost airlines, march 2020basirpm
This document discusses a study examining the financial factors that affect the profitability of low-cost airlines. The study analyzes 16 low-cost airlines from 2004-2017 using panel data analysis. Two models are used, with return on assets and return on equity as the dependent variables. The findings show that growth opportunities and asset structure affect profitability in the first model. In the second model, growth opportunities, asset structure, and leverage level impact profitability. The document provides context on previous related studies and outlines the methodology and contributions of this particular study.
Profitability analysis of indigo airlinesRupesh Yadav
The civil aviation industry in India has emerged as one of the fastest growing industries in the country during the last few years. India has become the third largest domestic aviation market in the world and is expected to overtake UK to become the third largest air passenger market by 2024. Aviation industry is one of the most important industries of any country because of its economic and social viability. The aviation industry not only contributes to Gross Domestic Product (GDP) of the country but it also improves employment statistics of the country. Aviation industry also plays a vital role in execution of domestic and international trade. In India nearly 25 airlines companies operating out of which major portion of market share were captured by InterGlobe Aviation Ltd. i.e Indigo Airlines, approximately 48% of market share hold by Indigo Airlines as of November, 2019. It started its business operations on 4th August, 2006. In the financial year 2018-19, Indigo Airlines has generated revenue of Rs. 298.2 billion and earned profit of Rs.1.6 billion by carrying 64 million passenger. In the present paper at attempt was made to analyse the profitability of Indigo Airlines during the last decade using various profitability ratios and statistical measures at appropriate places.
IRJET- Algorithms for the Prediction of Traffic AccidentsIRJET Journal
This document discusses algorithms for predicting traffic accidents using association rules. It first provides background on studying factors that influence traffic accidents and using data mining approaches like association rules. It then reviews related literature applying decision trees and other algorithms to injury severity prediction. The document proposes a method to calculate minimum support and automatically extract strong association rules from accident data to predict patterns and promote applications in intelligent transportation systems.
Prediction of Car Price using Linear Regressionijtsrd
In this paper, we look at how supervised machine learning techniques can be used to forecast car prices in India. Data from the online marketplace quikr was used to make the predictions. The predictions were made using a variety of methods, including multiple linear regression analysis, Random forest regressor and Randomized search CV. The predictions are then analyzed and compared to determine which ones provide the best results. A seemingly simple problem turned out to be extremely difficult to solve accurately. All of the strategies yielded similar results. In the future, we want to make predictions using more advanced technologies. Ravi Shastri | Dr. A Rengarajan "Prediction of Car Price using Linear Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42421.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42421/prediction-of-car-price-using-linear-regression/ravi-shastri
This document discusses developing a standardized set of indicators and metrics to assess the sustainability of aircraft designs. It proposes indicators within three pillars: environmental, economic, and societal. Potential indicators are identified through literature review. A survey of industry experts is used to validate the indicator set. The survey employs a Delphi method, sending multiple rounds of questionnaires to determine the relative importance of indicators and gather feedback to refine the set. The goal is to allow aircraft designers to quantitatively evaluate sustainability impacts during conceptual design.
This document outlines a proposed study on the relationship between strategic agility and performance at Kenya Airways. It discusses how strategic agility, comprising responsiveness, competency, flexibility, and speed, can help firms adapt to changing business environments. The study aims to assess how these dimensions of strategic agility influence Kenya Airways' performance, and whether competitive advantage mediates this relationship. It presents a conceptual framework and hypotheses relating strategic agility and competitive advantage to performance. The proposed methodology is a cross-sectional survey of 398 Kenya Airways employees, supplemented by 5 manager interviews. Data will be analyzed quantitatively and qualitatively to understand the relationships between these variables.
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICScscpconf
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to
study the impacts of public records and firmographics and predict the bankruptcy in a 12-
month-ahead period with using different classification models and adding values to traditionally
used financial ratios. Univariate analysis shows the statistical association and significance of
public records and firmographics indicators with the bankruptcy. Further, seven statistical
models and machine learning methods were developed, including Logistic Regression, Decision
Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. The performance of models were evaluated and compared based on
classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset.
Moreover, an experiment was set up to show the importance of oversampling for rare event
prediction. The result also shows that Bayesian Network is comparatively more robust than
other models without oversampling.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWSIJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWSIJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWS IJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
Decision Support System Using FUCOM-MARCOS for Airline Selection in IndonesiaGede Surya Mahendra
Abstract— Since deregulation in 1999, the development of the Indonesian aviation industry has continued to develop. However, many airlines still face various problems before and during flights. Problems in the plane, ranging from engine problems, technical problems, tire damage, cockpit problems to air pressure problems. Airlines customers have personal considerations and preferences when choosing an airline. The many choices and many considerations of airlines often confuse customers. To solve this problem, a decision support system (DSS) can be used to provide advice in selecting airlines based on customer preferences. This study uses the FUCOM-MARCOS method, using 8 criteria and 6 testing alternatives. When using FUCOM to calculate criterion weights, it appears that the factor price (C5) is the factor that counts most. Calculations using FUCOM-MARCOS show that Garuda Indonesia is the favorite airline in Indonesia with a preference value of 0.7390, followed by Citilink in second place, and Batik Air in third. Testing using consistency analysis shows that Garuda Indonesia remains stable and is the first choice by being ranked first 15 times out of 17 tests, with an average ranking distribution reaching 1.23466.
Intisari— Sejak deregulasi pada 1999, perkembangan industri penerbangan Indonesia semakin berkembang. Namun, banyak maskapai penerbangan yang masih menghadapi berbagai masalah sebelum dan selama penerbangan. Terdapat masalah di pesawat, mulai dari masalah mesin, masalah teknis, kerusakan ban, masalah kokpit hingga masalah tekanan udara. Pelanggan maskapai penerbangan memiliki pertimbangan dan preferensi pribadi saat memilih maskapai. Banyaknya pilihan dan banyaknya pertimbangan maskapai seringkali membingungkan pelanggan. Untuk mengatasi masalah tersebut, dapat digunakan sistem pendukung keputusan (SPK) untuk memberikan saran dalam memilih maskapai penerbangan berdasarkan preferensi pelanggan. Penelitian ini menggunakan metode FUCOM-MARCOS, menggunakan 8 kriteria dan 6 alternatif untuk dilakukan pengujian. Ketika menggunakan FUCOM untuk menghitung bobot kriteria, terlihat bahwa faktor harga (C5) adalah faktor yang paling diperhitungkan. Perhitungan menggunakan FUCOM- MARCOS menunjukkan bahwa Garuda Indonesia merupakan maskapai terfavorit di Indonesia dengan nilai preferensi 0,7390, disusul Citilink, sebagai peringkat kedua, dan Batik Air menduduki peringkat ketiga. Pengujian menggunakan analisis konsistensi menunjukkan bahwa Garuda Indonesia tetap stabil dan menjadi pilihan pertama, menduduki peringkat pertama sebanyak 15 kali dari 17 pengujian, dengan rata-rata sebaran peringkat mencapai 1.23466.
An Estimation Model For Replicating The Rankings Of The World Competitiveness...Holly Fisher
The document describes the methodology used in the World Competitiveness Report (WCR) to rank countries based on their competitiveness. The WCR ranks countries at four levels of aggregation: composite indicators, subfactors, factors, and an overall competitiveness ranking. It groups 287 indicators into 82 composite indicators, which are then grouped into 32 subfactors. The subfactors are grouped into 8 factors, which are then combined into an overall competitiveness ranking. However, the exact formulas used at each level of aggregation are not disclosed. The objective of the paper is to uncover these formulas by developing a model that can replicate the WCR rankings at all levels of aggregation using mathematical programming.
IHP 525 Milestone Five (Final) TemplateMOST OF THIS TEMPLATE S.docxwilcockiris
IHP 525 Milestone Five (Final) Template
MOST OF THIS TEMPLATE SHOULD BE COPIED AND PASTED FROM PRIOR MILESTONES IF YOU RECEIVED FULL CREDIT FOR THOSE ELEMENTS.
DO NOT DELETE ANYTHING IN THIS TEMPLATE.
Student Name:
State the question you will pursue. (This should be copied and pasted from the list of questions and should be the same question submitted in week 7 unless you have changed your question.)
Question of interest (Copy and Paste question here):
Restate this question in your own words:
Directions for following table:
· Fill out the table below for EACH variable of interest.
· Include ONLY the variables that are relevant to your question of interest. (If it’s not mentioned in your question directly, it’s not relevant.)
· Each variable should take up ONE row.
Variable name (one variable per row)
Note: gender is a variable, and 0 and 1 are values of the variable gender. Gender =0 and gender =1 are NOT two separate variables.
Variable type (categorical, ordinal, or quantitative, etc.)
Descriptive statistics
Include:
· the statistic names (the mean, median, range, and standard deviation, at minimum)
· final calculations (e.g., mean = 10)
· an explanation/definition of each statistic used (what each statistic SAYS about the data)
*Do not subdivide the data for the variable in each row based on any other variable. For example, do NOT find the mean length of stay separately for males and females.
**The above statistics can be found for binary variables. (For example, gender is coded as binary; therefore, the above descriptive statistics can be found for the variable gender.)
Key features
· Histogram symmetric?
· Histogram bell shaped?
· Any outliers?
· Skew?
· Unimodal?
· Any other special features?
DO NOT list or discuss descriptive statistics in this space. Use the table above, as directed.
Analyze the limitations of the data set you were provided and how those limitations might affect your findings.
Limit your response to the data relevant to your question of interest. (For example, only using two variables is NOT a limitation of the data in your question of interest. It may be a limitation of the study or question of interest, but it is NOT a limitation of the data you have been provided for your question of interest.)
Limitations:
Provide ONE graph that is useful in explaining your results.
You may copy and paste this from another program, take a screen shot, etc.
LABEL EVERYTHING!!!
Explain why you chose this graph above any others to explain the situation.
What test/analysis technique did you perform?
(It is highly recommended that you perform ONLY ONE test or technique. Some examples include a t-test, regression, etc.)
There is a hypothesis test associated with your test/technique (even if you are not doing a t-test).
What is your null hypothesis?
What is your alternative hypothesis?
Provide all relevant calculations for your hypothesis test/ statistical technique.
Make sure your final ans.
This document discusses methods for selecting third-party logistics (TPL) providers using the analytic hierarchy process (AHP). It begins by outlining various criteria that have been used to evaluate TPL providers, such as cost, quality of service, and operational efficiency. It then reviews common methods that have been used, including AHP, fuzzy AHP, and fuzzy linear assignment. The document applies the AHP method to a case study of selecting a TPL provider for laboratory services for an emergency department in a Tunisian hospital. It establishes a hierarchy of criteria and uses pairwise comparisons to calculate weights for the criteria and rank potential TPL provider alternatives.
Predicting Air Transport Industry - 2018 Mohammed Awad
Predicting Air Transport Industry based two input parameters - RPKs and ASKs, then using their forecasted results to predicted the Air Transport Industry Performance i.e Load Factor which is simply RPKs/ASKs
A Systematic Literature Review Of Revenue Management In Passenger TransportationLaurie Smith
This document presents a systematic literature review of revenue management in passenger transportation. It analyzed 521 academic papers on the topic using latent Dirichlet allocation to identify 10 key topics that the literature falls within. These topics were further analyzed based on a subset of 29 high-quality papers. The topics included product inventory and seat allocation, monitoring of industry data and events, and optimization models and techniques. The review aims to provide a more structured and systemic view of the revenue management literature within a managerial perspective.
Industrial Benchmarking through Information Visualization and Data Envelopmen...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/industrial-benchmarking-through-information-visualization-and-data-envelopment-analysis-a-new-framework/
We present a benchmarking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.
Industrial Benchmarking through Information Visualization and Data Envelopmen...Gurdal Ertek
We present a bench marking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive bench marking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.Finally, other relevant data, apart from the financial data, is introduced to the analysis through information visualization to discover new insights into DEA results. This study uses information visualization to both explore and reveal the relationships between the different methodologies of financial benchmarking and gain practical insights on the Turkish food industry. The results show that the framework developed is a comprehensive and effective strategy for benchmarking; it can be applied in other industries as well. As a result, our study contributes to the DEA benchmarking literature with a novel methodology that integrates the various bench marking methods from the fields of operations research, machine learning, and financial analysis.
http://research.sabanciuniv.edu/
Linear modeling in Transportation problem: Need of reliable Public transporta...IOSR Journals
1) The document discusses assessing the efficiency of public transportation systems in Madhya Pradesh, India using data envelopment analysis. It examines inputs like employees, buses, and bus kilometers, and output like total revenue.
2) Results from the data envelopment analysis indicate need for improvement in the public transportation system. Efforts should be made to improve productivity and efficiency of operators to enhance quality and quantity of services.
3) The document also discusses using discrete choice modeling to analyze socioeconomic factors influencing mobility and evaluating approaches to reduce air pollution from road transportation.
This document proposes a decision support system to help a production company determine the best country for a new investment abroad. It involves 4 phases: 1) determining criteria for evaluating candidate countries, 2) using AHP to assign weights to the criteria, 3) evaluating 50 candidate countries based on the criteria, selecting the top 10, and 4) further evaluating the top 10 using another AHP model to select the top 5 investment alternatives. The system aims to provide a comprehensive, scientific approach for solving the investment location problem.
The factors determining the profitability of low cost airlines, march 2020basirpm
This document discusses a study examining the financial factors that affect the profitability of low-cost airlines. The study analyzes 16 low-cost airlines from 2004-2017 using panel data analysis. Two models are used, with return on assets and return on equity as the dependent variables. The findings show that growth opportunities and asset structure affect profitability in the first model. In the second model, growth opportunities, asset structure, and leverage level impact profitability. The document provides context on previous related studies and outlines the methodology and contributions of this particular study.
Profitability analysis of indigo airlinesRupesh Yadav
The civil aviation industry in India has emerged as one of the fastest growing industries in the country during the last few years. India has become the third largest domestic aviation market in the world and is expected to overtake UK to become the third largest air passenger market by 2024. Aviation industry is one of the most important industries of any country because of its economic and social viability. The aviation industry not only contributes to Gross Domestic Product (GDP) of the country but it also improves employment statistics of the country. Aviation industry also plays a vital role in execution of domestic and international trade. In India nearly 25 airlines companies operating out of which major portion of market share were captured by InterGlobe Aviation Ltd. i.e Indigo Airlines, approximately 48% of market share hold by Indigo Airlines as of November, 2019. It started its business operations on 4th August, 2006. In the financial year 2018-19, Indigo Airlines has generated revenue of Rs. 298.2 billion and earned profit of Rs.1.6 billion by carrying 64 million passenger. In the present paper at attempt was made to analyse the profitability of Indigo Airlines during the last decade using various profitability ratios and statistical measures at appropriate places.
IRJET- Algorithms for the Prediction of Traffic AccidentsIRJET Journal
This document discusses algorithms for predicting traffic accidents using association rules. It first provides background on studying factors that influence traffic accidents and using data mining approaches like association rules. It then reviews related literature applying decision trees and other algorithms to injury severity prediction. The document proposes a method to calculate minimum support and automatically extract strong association rules from accident data to predict patterns and promote applications in intelligent transportation systems.
Prediction of Car Price using Linear Regressionijtsrd
In this paper, we look at how supervised machine learning techniques can be used to forecast car prices in India. Data from the online marketplace quikr was used to make the predictions. The predictions were made using a variety of methods, including multiple linear regression analysis, Random forest regressor and Randomized search CV. The predictions are then analyzed and compared to determine which ones provide the best results. A seemingly simple problem turned out to be extremely difficult to solve accurately. All of the strategies yielded similar results. In the future, we want to make predictions using more advanced technologies. Ravi Shastri | Dr. A Rengarajan "Prediction of Car Price using Linear Regression" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-5 | Issue-4 , June 2021, URL: https://www.ijtsrd.compapers/ijtsrd42421.pdf Paper URL: https://www.ijtsrd.comcomputer-science/data-processing/42421/prediction-of-car-price-using-linear-regression/ravi-shastri
This document discusses developing a standardized set of indicators and metrics to assess the sustainability of aircraft designs. It proposes indicators within three pillars: environmental, economic, and societal. Potential indicators are identified through literature review. A survey of industry experts is used to validate the indicator set. The survey employs a Delphi method, sending multiple rounds of questionnaires to determine the relative importance of indicators and gather feedback to refine the set. The goal is to allow aircraft designers to quantitatively evaluate sustainability impacts during conceptual design.
This document outlines a proposed study on the relationship between strategic agility and performance at Kenya Airways. It discusses how strategic agility, comprising responsiveness, competency, flexibility, and speed, can help firms adapt to changing business environments. The study aims to assess how these dimensions of strategic agility influence Kenya Airways' performance, and whether competitive advantage mediates this relationship. It presents a conceptual framework and hypotheses relating strategic agility and competitive advantage to performance. The proposed methodology is a cross-sectional survey of 398 Kenya Airways employees, supplemented by 5 manager interviews. Data will be analyzed quantitatively and qualitatively to understand the relationships between these variables.
COMPARISON OF BANKRUPTCY PREDICTION MODELS WITH PUBLIC RECORDS AND FIRMOGRAPHICScscpconf
Many business operations and strategies rely on bankruptcy prediction. In this paper, we aim to
study the impacts of public records and firmographics and predict the bankruptcy in a 12-
month-ahead period with using different classification models and adding values to traditionally
used financial ratios. Univariate analysis shows the statistical association and significance of
public records and firmographics indicators with the bankruptcy. Further, seven statistical
models and machine learning methods were developed, including Logistic Regression, Decision
Tree, Random Forest, Gradient Boosting, Support Vector Machine, Bayesian Network, and
Neural Network. The performance of models were evaluated and compared based on
classification accuracy, Type I error, Type II error, and ROC curves on the hold-out dataset.
Moreover, an experiment was set up to show the importance of oversampling for rare event
prediction. The result also shows that Bayesian Network is comparatively more robust than
other models without oversampling.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWSIJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWSIJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
UNDERSTANDING CUSTOMERS' EVALUATIONS THROUGH MINING AIRLINE REVIEWS IJDKP
Data mining can be evaluated as a strategic tool to determine the customer profiles in order to learn
customer expectations and requirements. Airline customers have different characteristics and if passenger
reviews about their trip experiences are correctly analyzed, companies can increase customer satisfaction
by improving provided services. In this study, we investigate customer review data for in-flight services of
airline companies and draw customer models with respect to such data. In this sense, we apply two
approaches as feature-based and clustering-based modelling. In feature-based modelling, customers are
grouped into categories based on features such as cabin flown types, experienced airline companies. In
clustering-based modelling, customers are first clustered via k-means clustering and then modeled. We
apply multivariate regression analysis to model customer groups in both cases. During this, we try to
understand how customers evaluate the given services and what dominant characteristics of in-flight
services can be from the customer viewpoint.
Decision Support System Using FUCOM-MARCOS for Airline Selection in IndonesiaGede Surya Mahendra
Abstract— Since deregulation in 1999, the development of the Indonesian aviation industry has continued to develop. However, many airlines still face various problems before and during flights. Problems in the plane, ranging from engine problems, technical problems, tire damage, cockpit problems to air pressure problems. Airlines customers have personal considerations and preferences when choosing an airline. The many choices and many considerations of airlines often confuse customers. To solve this problem, a decision support system (DSS) can be used to provide advice in selecting airlines based on customer preferences. This study uses the FUCOM-MARCOS method, using 8 criteria and 6 testing alternatives. When using FUCOM to calculate criterion weights, it appears that the factor price (C5) is the factor that counts most. Calculations using FUCOM-MARCOS show that Garuda Indonesia is the favorite airline in Indonesia with a preference value of 0.7390, followed by Citilink in second place, and Batik Air in third. Testing using consistency analysis shows that Garuda Indonesia remains stable and is the first choice by being ranked first 15 times out of 17 tests, with an average ranking distribution reaching 1.23466.
Intisari— Sejak deregulasi pada 1999, perkembangan industri penerbangan Indonesia semakin berkembang. Namun, banyak maskapai penerbangan yang masih menghadapi berbagai masalah sebelum dan selama penerbangan. Terdapat masalah di pesawat, mulai dari masalah mesin, masalah teknis, kerusakan ban, masalah kokpit hingga masalah tekanan udara. Pelanggan maskapai penerbangan memiliki pertimbangan dan preferensi pribadi saat memilih maskapai. Banyaknya pilihan dan banyaknya pertimbangan maskapai seringkali membingungkan pelanggan. Untuk mengatasi masalah tersebut, dapat digunakan sistem pendukung keputusan (SPK) untuk memberikan saran dalam memilih maskapai penerbangan berdasarkan preferensi pelanggan. Penelitian ini menggunakan metode FUCOM-MARCOS, menggunakan 8 kriteria dan 6 alternatif untuk dilakukan pengujian. Ketika menggunakan FUCOM untuk menghitung bobot kriteria, terlihat bahwa faktor harga (C5) adalah faktor yang paling diperhitungkan. Perhitungan menggunakan FUCOM- MARCOS menunjukkan bahwa Garuda Indonesia merupakan maskapai terfavorit di Indonesia dengan nilai preferensi 0,7390, disusul Citilink, sebagai peringkat kedua, dan Batik Air menduduki peringkat ketiga. Pengujian menggunakan analisis konsistensi menunjukkan bahwa Garuda Indonesia tetap stabil dan menjadi pilihan pertama, menduduki peringkat pertama sebanyak 15 kali dari 17 pengujian, dengan rata-rata sebaran peringkat mencapai 1.23466.
An Estimation Model For Replicating The Rankings Of The World Competitiveness...Holly Fisher
The document describes the methodology used in the World Competitiveness Report (WCR) to rank countries based on their competitiveness. The WCR ranks countries at four levels of aggregation: composite indicators, subfactors, factors, and an overall competitiveness ranking. It groups 287 indicators into 82 composite indicators, which are then grouped into 32 subfactors. The subfactors are grouped into 8 factors, which are then combined into an overall competitiveness ranking. However, the exact formulas used at each level of aggregation are not disclosed. The objective of the paper is to uncover these formulas by developing a model that can replicate the WCR rankings at all levels of aggregation using mathematical programming.
IHP 525 Milestone Five (Final) TemplateMOST OF THIS TEMPLATE S.docxwilcockiris
IHP 525 Milestone Five (Final) Template
MOST OF THIS TEMPLATE SHOULD BE COPIED AND PASTED FROM PRIOR MILESTONES IF YOU RECEIVED FULL CREDIT FOR THOSE ELEMENTS.
DO NOT DELETE ANYTHING IN THIS TEMPLATE.
Student Name:
State the question you will pursue. (This should be copied and pasted from the list of questions and should be the same question submitted in week 7 unless you have changed your question.)
Question of interest (Copy and Paste question here):
Restate this question in your own words:
Directions for following table:
· Fill out the table below for EACH variable of interest.
· Include ONLY the variables that are relevant to your question of interest. (If it’s not mentioned in your question directly, it’s not relevant.)
· Each variable should take up ONE row.
Variable name (one variable per row)
Note: gender is a variable, and 0 and 1 are values of the variable gender. Gender =0 and gender =1 are NOT two separate variables.
Variable type (categorical, ordinal, or quantitative, etc.)
Descriptive statistics
Include:
· the statistic names (the mean, median, range, and standard deviation, at minimum)
· final calculations (e.g., mean = 10)
· an explanation/definition of each statistic used (what each statistic SAYS about the data)
*Do not subdivide the data for the variable in each row based on any other variable. For example, do NOT find the mean length of stay separately for males and females.
**The above statistics can be found for binary variables. (For example, gender is coded as binary; therefore, the above descriptive statistics can be found for the variable gender.)
Key features
· Histogram symmetric?
· Histogram bell shaped?
· Any outliers?
· Skew?
· Unimodal?
· Any other special features?
DO NOT list or discuss descriptive statistics in this space. Use the table above, as directed.
Analyze the limitations of the data set you were provided and how those limitations might affect your findings.
Limit your response to the data relevant to your question of interest. (For example, only using two variables is NOT a limitation of the data in your question of interest. It may be a limitation of the study or question of interest, but it is NOT a limitation of the data you have been provided for your question of interest.)
Limitations:
Provide ONE graph that is useful in explaining your results.
You may copy and paste this from another program, take a screen shot, etc.
LABEL EVERYTHING!!!
Explain why you chose this graph above any others to explain the situation.
What test/analysis technique did you perform?
(It is highly recommended that you perform ONLY ONE test or technique. Some examples include a t-test, regression, etc.)
There is a hypothesis test associated with your test/technique (even if you are not doing a t-test).
What is your null hypothesis?
What is your alternative hypothesis?
Provide all relevant calculations for your hypothesis test/ statistical technique.
Make sure your final ans.
This document discusses methods for selecting third-party logistics (TPL) providers using the analytic hierarchy process (AHP). It begins by outlining various criteria that have been used to evaluate TPL providers, such as cost, quality of service, and operational efficiency. It then reviews common methods that have been used, including AHP, fuzzy AHP, and fuzzy linear assignment. The document applies the AHP method to a case study of selecting a TPL provider for laboratory services for an emergency department in a Tunisian hospital. It establishes a hierarchy of criteria and uses pairwise comparisons to calculate weights for the criteria and rank potential TPL provider alternatives.
Predicting Air Transport Industry - 2018 Mohammed Awad
Predicting Air Transport Industry based two input parameters - RPKs and ASKs, then using their forecasted results to predicted the Air Transport Industry Performance i.e Load Factor which is simply RPKs/ASKs
A Systematic Literature Review Of Revenue Management In Passenger TransportationLaurie Smith
This document presents a systematic literature review of revenue management in passenger transportation. It analyzed 521 academic papers on the topic using latent Dirichlet allocation to identify 10 key topics that the literature falls within. These topics were further analyzed based on a subset of 29 high-quality papers. The topics included product inventory and seat allocation, monitoring of industry data and events, and optimization models and techniques. The review aims to provide a more structured and systemic view of the revenue management literature within a managerial perspective.
Industrial Benchmarking through Information Visualization and Data Envelopmen...ertekg
Download Link > https://ertekprojects.com/gurdal-ertek-publications/blog/industrial-benchmarking-through-information-visualization-and-data-envelopment-analysis-a-new-framework/
We present a benchmarking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive benchmarking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.
Industrial Benchmarking through Information Visualization and Data Envelopmen...Gurdal Ertek
We present a bench marking study on the companies in the Turkish food industry based on their financial data. Our aim is to develop a comprehensive bench marking framework using Data Envelopment Analysis (DEA) and information visualization. Besides DEA, a traditional tool for financial benchmarking based on financial ratios is also incorporated. The consistency/inconsistency between the two methodologies is investigated using information visualization tools. In addition, k-means clustering, a fundamental method from machine learning, is applied to understand the relationship between k-means clustering and DEA.Finally, other relevant data, apart from the financial data, is introduced to the analysis through information visualization to discover new insights into DEA results. This study uses information visualization to both explore and reveal the relationships between the different methodologies of financial benchmarking and gain practical insights on the Turkish food industry. The results show that the framework developed is a comprehensive and effective strategy for benchmarking; it can be applied in other industries as well. As a result, our study contributes to the DEA benchmarking literature with a novel methodology that integrates the various bench marking methods from the fields of operations research, machine learning, and financial analysis.
http://research.sabanciuniv.edu/
This document discusses education inequality in Ivory Coast in the context of the COVID-19 pandemic. It identifies types of conventional education inequalities related to school attainment, distribution, completion, and learning outcomes. While efforts have been made to promote equality and equity, inequalities persist. The use of media education during COVID-19 in Ivory Coast revealed gender and regional inequalities. There were both similarities and differences between conventional and media-based education inequalities. Media education helped reduce some inequalities but also created new ones related to access and resources. Addressing issues like school locations, inequity, and rural-urban divides could help promote more equal education opportunities.
This document summarizes a research paper that examines how a lack of local legitimacy has undermined the effectiveness of UN peacekeeping missions in the Horn of Africa region. It finds that host governments and conflicting parties often perceived missions as inappropriate or partisan, reducing cooperation. When local actors see missions as illegitimate, they are less likely to comply or support them. The document analyzes how legitimacy deficits influenced specific missions, like UNOSOM in Somalia in the 1990s, which failed in part due to a lack of consent from powerful Somali warlords. Overall, it concludes that a lack of local legitimacy has made missions unable to resolve conflicts and promote sustainable peace in the region.
17062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
Shark Tank Jargon | Operational ProfitabilityTheUnitedIndian
Don't let fancy business words confuse you! This blog is your cheat sheet to understanding the Shark Tank Jargon. We'll translate all the confusing terms like "valuation" (how much the company is worth) and "royalty" (a fee for using someone's idea). You'll be swimming with the Sharks like a pro in no time!
16062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
केरल उच्च न्यायालय ने 11 जून, 2024 को मंडला पूजा में भाग लेने की अनुमति मांगने वाली 10 वर्षीय लड़की की रिट याचिका को खारिज कर दिया, जिसमें सर्वोच्च न्यायालय की एक बड़ी पीठ के समक्ष इस मुद्दे की लंबित प्रकृति पर जोर दिया गया। यह आदेश न्यायमूर्ति अनिल के. नरेंद्रन और न्यायमूर्ति हरिशंकर वी. मेनन की खंडपीठ द्वारा पारित किया गया
#WenguiGuo#WashingtonFarm Guo Wengui Wolf son ambition exposed to open a far...rittaajmal71
Since fleeing to the United States in 2014, Guo Wengui has founded a number of projects in the United States, such as GTV Media Group, GTV private equity, farm loan project, G Club Operations Co., LTD., and Himalaya Exchange.
15062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
19 जून को बॉम्बे हाई कोर्ट ने विवादित फिल्म ‘हमारे बारह’ को 21 जून को थिएटर में रिलीज करने का रास्ता साफ कर दिया, हालांकि यह सुनिश्चित करने के बाद कि फिल्म निर्माता कुछ आपत्तिजनक अंशों को हटा दें।
ग्रेटर मुंबई के नगर आयुक्त को एक खुले पत्र में याचिका दायर कर 540 से अधिक मुंबईकरों ने सभी अवैध और अस्थिर होर्डिंग्स, साइनबोर्ड और इलेक्ट्रिक साइनेज को तत्काल हटाने और 13 मई, 2024 की शाम को घाटकोपर में अवैध होर्डिंग के गिरने की विनाशकारी घटना के बाद अपराधियों के खिलाफ सख्त कार्रवाई की मांग की है, जिसमें 17 लोगों की जान चली गई और कई निर्दोष लोग गंभीर रूप से घायल हो गए।
Christian persecution in Islamic countries has intensified, with alarming incidents of violence, discrimination, and intolerance. This article highlights recent attacks in Nigeria, Pakistan, Egypt, Iran, and Iraq, exposing the multifaceted challenges faced by Christian communities. Despite the severity of these atrocities, the Western world's response remains muted due to political, economic, and social considerations. The urgent need for international intervention is underscored, emphasizing that without substantial support, the future of Christianity in these regions is at grave risk.
https://ecspe.org/the-rise-of-christian-persecution-in-islamic-countries/
13062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
projet de traité négocié à Istanbul (anglais).pdfEdouardHusson
Ceci est le projet de traité qui avait été négocié entre Russes et Ukrainiens à Istanbul en mars 2022, avant que les Etats-Unis et la Grande-Bretagne ne détournent Kiev de signer.
18062024_First India Newspaper Jaipur.pdfFIRST INDIA
Find Latest India News and Breaking News these days from India on Politics, Business, Entertainment, Technology, Sports, Lifestyle and Coronavirus News in India and the world over that you can't miss. For real time update Visit our social media handle. Read First India NewsPaper in your morning replace. Visit First India.
CLICK:- https://firstindia.co.in/
#First_India_NewsPaper
Slide deck with charts from our Digital News Report 2024, the most comprehensive exploration of news consumption habits around the world, based on survey data from more than 95,000 respondents across 47 countries.
The Impact of Imperial Mode of Living on Migration.pdf
D233846
1. American Research Journal of Humanities Social Science (ARJHSS)R) 2019
ARJHSS Journal www.arjhss.com Page |38
American Research Journal of Humanities Social Science (ARJHSS)
E-ISSN: 2378-702X
Volume-02, Issue-03, pp-38-46
March-2019
www.arjhss.com
Research Paper Open Access
Comparison of World Airline Rankings with Different Criteria:
Best Airline Ranking and EVAMIX Method Rank
Assoc. Prof. Dr. Ozlem Deniz Basar,
Istanbul Commerce UniversityDepartment of Statistics
Sutluce Mah., Imrahor Cad., No :90, Beyoglu / Istanbul / Turkey Phone : +90-212-444-0413/4613
ABSTRACT:- Competition in the transportation sector is in constant rise as in all sectors today. It is crucial
for airlines, which are one of the most important shareholders in the transportation sector, to be recognized and
reliable in order to have a place in the market. For this reason, different companies prepare and share a world
airline ranking with public each year. These rankings are prepared using different criteria and methodologies.
Hence, each of these rankings differs from each other. This study considers the Best Airline Ranking prepared
by Airhelp in 2018. The first 15 airlines in this ranking are re-ranked with the EVAMIX method using unused
criteria and the results are compared with the Best Airline Ranking. In the conclusion of the study, it is found
out that rankings changed significantly; hence, it is greatly important to determine what is desired to measure
when the airlines are compared.
Keywords: Airline, Ranking, EVAMIX, Critic Method
I. INTRODUCTION
It is of importance to meet the changing consumer demands, reach and serve more customers in the
aviation sector as in all other sectors. For this reason, airline companies measure customer satisfaction both
globally and locally and endeavor to increase customer capacity.
In the aviation sector that is rapidly developing today, the indices and scores calculated to compare
companies in this sector are considered as significant tools to increase prefer ability and reputation of these
companies. There are many such indices prepared around the worldwide and shared with the public.
Skytrax ranks 100 airline companies every year since 1999 with the help of questionnaires applied on
flight passengers that include subjects of cabin services, ground handling services, airline, and flight
products(Skytrax , 2018). With the first ranking in 1991, Airline Quality Rating (Airline Quality Rating, 2018)
focused on four main areas in order to measure the performances of airlines and ranked American local airline
companies with the help of multiple performance criteria in 2018. Jacdec ranks best airlines in the world since
2006 using criteria of accident/incident history, environmental factors, and airline operation risk factors(Jacdec,
2018).
While there are not many studies that rank airline companies among academic publications, there are
some comparisons in literature. In their 2013 article, Wu et al. compared and ranked 26 airlines in China using
binary relative evaluation model with 2008 and 2009 data(Wu, Wang, Zhang, Li, & O'Brien, 2013). Klophaus
and Lordan (2018) studied airline companies in the Star Aliance, Sky Team and One World groups, and ranked
these companies based on code-sharing, network, vulnerability metrics(Klophaus & Lordan, 2018). Torlak et al.
(2011) compared and ranked Turkish domestic airline companies using various criteria(Torlak, Sevkli, Sanal, &
Zaim, 2011).
This study examines 2018 worldwide rankings announced yearly by Airhelp. Airhelp uses results of
on-time performance, service quality and claim processing measurements of world airline companies for
ranking them (Airhelp, 2018). It is believed that the airline ranking by Airhelp with the criteria they determined
would change when different criteria and methodologies are used. For this reason, considering different criteria,
2. American Research Journal of Humanities Social Science (ARJHSS)R) 2019
ARJHSS Journal www.arjhss.com Page |39
the airline companies are ranked using the EVAMIX method which is one of the multi-criteria decision-making
methods. The ranking obtained is compared with the ranking by Airhelp and the results are interpreted.
II. METHODOLOGY
The EVAMIX method (Evaluation of Mixed Data) is one of the multi-criteria decision-making
methods and its use is increasing in recent years. It is first used by Voogd (1982) and developed by Martel and
Matarazzo (2005). This method is based on obtaining dominance score as a result of comparing each alternative
on the other alternative on the basis of criteria in case there are both ordinal and cardinal data (Chatterjee,
Mondal, & Chakraborty, 2014). The rankings are prepared with the help of dominance scores obtained. Before
applying the EVAMIX method, it is required to calculate the weights. There are different methods for weight
calculation in literature. This study uses the CRITIC method for calculating weights, which is explained in the
next part.
a. CRITIC Method
The CRITIC method (Criteria Importance through Inter-criteria Correlation) is one of the most
common weight calculation methods used in decision-making methods and is developed by Diakoulaki et al.
(1995). In this method, determination of criteria weights include both the standard deviation and the correlation
of the criterion(Yalçın & Ünlü, 2018). Therefore, it is recommended to use the CRITIC method to calculate
weights in such studies that involve relations among variables (Diakoulaki, Mavrotas, & Papayannakis, 1995).
Jahan et al. (2012) shows the steps of obtaining weights with the CRITIC method as below (Jahan, Mustapha,
Sapuan, Ismail, & Bahraminasab, 2012).
Step 1: The benefit and cost criteria in the decision-matrix formed are determined. These values are normalized
as follows:
𝑟𝑖𝑗 =
𝑥 𝑖𝑗 −𝑥 𝑗
𝑚𝑖𝑛
𝑥 𝑗
𝑚𝑎𝑥 −𝑥 𝑗
𝑚𝑖𝑛 𝑖 = 1, … , 𝑚 ; 𝑗 = 1, … , 𝑛 for benefit criteria (1)
𝑟𝑖𝑗 =
𝑥 𝑗
𝑚𝑎𝑥
−𝑥 𝑖𝑗
𝑥 𝑗
𝑚𝑎𝑥 −𝑥 𝑗
𝑚𝑖𝑛 𝑖 = 1, … , 𝑚 ; 𝑗 = 1, … , 𝑛 for cost criteria (2)
Here xij is the value of ith
alternative at jth
criterion.
Step 2: The correlation among criteria and their standard deviation are calculated.
Step 3: The CRITIC weight values are calculated using equations no. 3 and 4 with the help of the values of
standard deviations and correlation.
𝐶𝑗 = 𝜎𝑗 1 − 𝜌𝑗𝑘
𝑛
𝑘=1 𝑗 = 1, … , 𝑛 (3)
𝑤𝑗 =
𝐶 𝑗
𝐶 𝑘
𝑛
𝑘=1
𝑗 = 1, … , 𝑛 (4)
b. EVAMIX Method
The EVAMIX method is applied with the help of the following steps.
Step 1: Firstly, the criteria are grouped into two as qualitative and quantitative criteria. The process hereafter is
the same as the process in the step 1 of the CRITIC method. First, the decision matrix is formed. The cost and
benefit criteria in this matrix are determined and then normalized with the formulas numbered (1) and (2).
Step 2: Alternative pairs are formed and then compared with each other. This comparison is done through
dominance scores. The dominance score for quantitative variables is calculated using equation (5), and equation
(6) is for qualitative variables (Aytaç Adalı, 2016).
3. American Research Journal of Humanities Social Science (ARJHSS)R) 2019
ARJHSS Journal www.arjhss.com Page |40
𝛼𝑖𝑖′ = 𝑤𝑗 𝑠𝑔𝑛 𝑟𝑖𝑗 − 𝑟𝑖′ 𝑗
𝑐
𝑗=0
1/𝑐
(5)
and
𝑠𝑔𝑛 𝑟𝑖𝑗 − 𝑟𝑖′ 𝑗 =
+1 𝑖𝑓 𝑟𝑖𝑗 > 𝑟𝑖′ 𝑗
0 𝑖𝑓 𝑟𝑖𝑗 = 𝑟𝑖′ 𝑗
−1 𝑖𝑓 𝑟𝑖𝑗 < 𝑟𝑖′ 𝑗
𝛾𝑖𝑖′ = 𝑤𝑗 𝑠𝑔𝑛(𝑟𝑖𝑗 − 𝑟𝑖′ 𝑗 )
𝑐
𝑗𝜖𝑐
1/𝑐
(6)
The value wj in the formula represents the weight value of jth
criterion. c is a random scaling parameter and
recommended to be assigned with 1(Andalecio, 2010).
Step 3: The dominance scores 𝛼𝑖𝑖′ and 𝛾𝑖𝑖′ calculated in the previous step have different units, hence they need to
be standardized. The standardized dominance scores are obtained using equation (7) for ordinal criteria and
equation (8) for cardinal criteria(Darji & Rao, 2013).
𝛿𝑖𝑖′ =
𝛼 𝑖𝑖′ −𝛼−
𝛼+−𝛼− (7)
𝑑𝑖𝑖′ =
𝛾 𝑖𝑖′ −𝛾−
𝛾+−𝛾− (8)
Calculated for each alternative pair in the above equations, the value 𝛼+
(𝛼−
) is the highest (lowest) ordinal
dominance score, and the value 𝛾+
(𝛾−
) is the highest (lowest) cardinal dominance score.
Step 4: 𝑤0 is the sum of weights of ordinal criteria and 𝑤𝑐 is the sum of weights of cardinal criteria. The overall
dominance score is calculated for each alternative pair as shown in equation (9) (Chatterjee, Mondal, &
Chakraborty, 2014).
𝐷𝑖𝑖′ = 𝑤0 𝛿𝑖𝑖′ + 𝑤𝑐 𝑑𝑖𝑖′ (9)
Step 5: Appraisal score for each alternative Si is calculated with equation (10) and the alternatives are ranked
based on this score. A high appraisal score indicates that the alternative is better than others.
𝑆𝑖 =
𝐷 𝑖′ 𝑖
𝐷 𝑖𝑖′
−1
𝑖′ (10)
III. IMPLEMENTATION
The 2018 list of Worldwide Rankings published annually by Airhelp ranks world airline companies
based on on-time performance, quality of service and claim processing. With the help of these rankings, airline
companies can compare themselves with other companies and may work on their preferability by customers.
Hence, these rankings have great importance in the sector. The world airlines ranking for the year 2018 and
scores calculated for the relevant airline (Statista, 2018) are given in Table 1. Air Malta and Etihad Airways
which are on the list prepared by Airhelp are excluded from the alternatives list because no public data could be
found on these companies while preparing the criteria. The criteria used in the ranking by Airhelp are certainly
quite significant. However, with the idea that using different criteria would also change the ranking of
companies, this study evaluated different criteria for airline companies and ranked them with the EVAMIX
method. The criteria used in this research are given in Table 1.
4. American Research Journal of Humanities Social Science (ARJHSS)R) 2019
ARJHSS Journal www.arjhss.com Page |41
Table 1. Variables used in the Research and Alternatives Ranking within Best Airlines Scores
AlternativeNo
Airlines Criteria
AirlineScore(2018)
Rank
C1 C2 C3 C4 C5 C6
Average
price per
passenger
Number
of
people
Retail
Value
Fleet Ordered
Plane
Number
Average
Age of
Fleet
A1 Aegean Airlines 8,19 6 3,133 6,095 8,000 49 30 10
A2 Austrian Airlines 8,29 5 5,539 7,181 2,826 83 0 15
A3 Flybe 7,94 12 6,745 4,935 40,909 77 4 10
A4 KLM Royal Dutch Airlines 8,01 9 3,827 6,043 -0,732 117 7 11
A5 Lufthansa 8,57 2 1,416 3,990 1,277 280 167 12
A6 Norwegian 8 10 3,792 28,591 3,178 157 214 4
A7 Qantas 8,12 7 6,548 1,109 0,000 132 14 11
A8 Qatar Airways 9,08 1 7,305 15,808 6,832 225 245 6
A9 Singapore Airlines 8,33 3 0,063 2,694 -1,935 118 100 7
A10 South African Airways 8,31 4 -13,274 0,829 -22,35 50 0 11
A11 Turkish Airlines 7,94 13 4,933 9,817 0,239 299 213 8
A12 Virgin Atlantic 8,04 8 2,670 -0,332 -5,882 44 18 10
A13 Wizz Air 7,95 11 6,444 10,431 2,331 97 264 5
The variables used in ranking the airline companies are obtained from Euromonitor and the corporate websites
of relevant airline companies. These variables can be explained as follows:
Average Price per Passenger (US$): It is the average price the passengers pay to fly with the airline
company. The price unit is selected as US Dollar for this study. This variable is edited in order to exhibit the
average increase in price of the airline companies considering the years 2017 and 2018.
Number of People: It is the number of passengers in domestic and international flights without taking
into account transit passengers. This variable is formed by calculating the increase considering the number of
passengers for the years 2017 and 2018.
Retail Value: It shows the increase in the retail value of the airline company between the years 2017
and 2018.
Fleet: It shows the number of fleets of the airline company.
Ordered Plane Number: It is the number of airplanes that are included in the order process of airline
companies as of 11.30.2018.
Average Age of Fleet: It shows the average age of fleet of the airline company as of 11.30.2018.
In the study, the airline companies will be referred to as alternatives, and the variables used for
comparison will be referred to as criteria. After creating the decision making matrix, which includes values of
criteria of alternatives, a normalized version of this decision matrix, is calculated in order to remove the effect of
units. In forming this matrix, it is important to pay attention to that criteria should be either benefit or cost
criteria.The study is conducted with the determination that fleet age is a cost, and remaining variables are benefit
variables. Based on this, the normalized decision matrix is formed using equation (1) and (2) and given in Table
2.
5. American Research Journal of Humanities Social Science (ARJHSS)R) 2019
ARJHSS Journal www.arjhss.com Page |42
Table 2. Normalized Decision Matrix
Alternative C1 C2 C3 C4 C5 C6
A1 0,797 0,222 0,480 0,020 0,114 0,411
A2 0,914 0,260 0,398 0,153 0,000 0,000
A3 0,973 0,182 1,000 0,129 0,015 0,449
A4 0,831 0,220 0,342 0,286 0,027 0,318
A5 0,714 0,149 0,373 0,925 0,633 0,252
A6 0,829 1,000 0,404 0,443 0,811 1,000
A7 0,963 0,050 0,353 0,345 0,053 0,374
A8 1,000 0,558 0,461 0,710 0,928 0,813
A9 0,648 0,105 0,323 0,290 0,379 0,720
A10 0,000 0,040 0,000 0,024 0,000 0,318
A11 0,885 0,351 0,357 1,000 0,807 0,654
A12 0,775 0,000 0,260 0,000 0,068 0,467
A13 0,958 0,372 0,390 0,208 1,000 0,888
In the next step, the correlation and standard deviations among criteria are calculated. It was stated that
this study will calculate weights with the CRITIC method in order to apply the EVAMIX method. Weights of
criteria are calculated using equations 3 and 4 and shown in Table 3.
Table 3. Objective Weight of Criteria
Criteria C1 C2 C3 C4 C5 C6
Cj 0,845 0,764 0,888 1,169 1,052 0,867
wj 0,151 0,137 0,159 0,209 0,188 0,155
The weights obtained will be used to rank companies. However, it is first required to compare
alternative pairs in this process of applying the EVAMIX method. To this purpose, the dominance scores will be
calculated using either equation 5 or equation 6 depending on whether the criteria are qualitative or quantitative.
However, the values calculated need to be compatible and hence need to be standardized. Using equation 7 for
ordinal criteria and equation 8 for cardinal criteria, the dominance scores are calculated and presented in Table
4.
Table 4. Standardized Dominance Scores of Alternative Pairs
Pairs 𝛿𝑖𝑖′ 𝑑𝑖𝑖′ Pairs 𝛿𝑖𝑖′ 𝑑𝑖𝑖′ Pairs 𝛿𝑖𝑖′ 𝑑𝑖𝑖′
(1,2) 0,4738 0,5633 (5,6) 0,526 0,202 (9,10) 1,000 0,758
(1,3) 0,4738 0,3716 (5,7) 1,000 0,453 (9,11) 0,000 0,424
(1,4) 0,4738 0,5370 (5,8) 0,526 0,265 (9,12) 1,000 0,552
(1,5) 0,0000 0,5751 (5,9) 1,000 0,443 (9,13) 0,526 0,359
(1,6) 0,0000 0,2768 (5,10) 1,000 0,702 (10,1) 0,526 0,223
(1,7) 0,4738 0,5286 (5,11) 0,000 0,367 (10,2) 0,237 0,286
(1,8) 0,0000 0,3406 (5,12) 1,000 0,495 (10,3) 0,000 0,095
(1,9) 0,0000 0,5185 (5,13) 0,526 0,302 (10,4) 0,000 0,260
(1,10) 0,4738 0,7768 (6,1) 1,000 0,723 (10,5) 0,000 0,298
(1,11) 0,0000 0,4426 (6,2) 1,000 0,786 (10,6) 0,000 0,000
(1,12) 1,0000 0,5703 (6,3) 1,000 0,595 (10,7) 0,000 0,252
7. American Research Journal of Humanities Social Science (ARJHSS)R) 2019
ARJHSS Journal www.arjhss.com Page |44
In this stage of the study, the overall dominance scores of alternative pairs are calculated considering
the sum of weights of ordinal and cardinal criteria. These scores are given in Table 5.
Table 5. Overall Dominance Scores of Alternative Pairs
Pairs Dii’ Pairs Dii’ Pairs Dii’ Pairs Dii’ Pairs Dii’
(1,2) 0,528 (3,10) 0,943 (6,4) 0,856 (8,12) 0,837 (11,6) 0,411
(1,3) 0,412 (3,11) 0,344 (6,5) 0,669 (8,13) 0,533 (11,7) 0,751
(1,4) 0,512 (3,12) 0,630 (6,7) 0,850 (9,1) 0,688 (11,8) 0,449
(1,5) 0,346 (3,13) 0,305 (6,8) 0,340 (9,2) 0,726 (11,9) 0,745
(1,6) 0,167 (4,1) 0,488 (6,9) 0,844 (9,3) 0,610 (11,10) 0,900
(1,7) 0,507 (4,2) 0,715 (6,10) 1,000 (9,4) 0,710 (11,12) 0,776
(1,8) 0,205 (4,3) 0,599 (6,11) 0,589 (9,5) 0,335 (11,13) 0,471
(1,9) 0,312 (4,5) 0,324 (6,12) 0,876 (9,6) 0,156 (12,1) 0,259
(1,10) 0,656 (4,6) 0,144 (6,13) 0,571 (9,7) 0,496 (12,2) 0,485
(1,11) 0,267 (4,7) 0,296 (7,1) 0,493 (9,8) 0,194 (12,3) 0,370
(1,12) 0,741 (4,8) 0,183 (7,2) 0,720 (9,10) 0,854 (12,4) 0,470
(1,13) 0,227 (4,9) 0,290 (7,3) 0,604 (9,11) 0,255 (12,5) 0,304
(2,1) 0,472 (4,10) 0,843 (7,4) 0,704 (9,12) 0,730 (12,6) 0,124
(2,3) 0,395 (4,11) 0,244 (7,5) 0,329 (9,13) 0,426 (12,7) 0,464
(2,4) 0,285 (4,12) 0,530 (7,6) 0,150 (10,1) 0,344 (12,8) 0,163
(2,5) 0,308 (4,13) 0,414 (7,8) 0,188 (10,2) 0,267 (12,9) 0,270
(2,6) 0,129 (5,1) 0,654 (7,9) 0,504 (10,3) 0,057 (12,10) 0,614
(2,7) 0,280 (5,2) 0,692 (7,10) 0,848 (10,4) 0,157 (12,11) 0,224
(2,8) 0,167 (5,3) 0,576 (7,11) 0,249 (10,5) 0,180 (12,13) 0,185
(2,9) 0,274 (5,4) 0,676 (7,12) 0,536 (10,6) 0,001 (13,1) 0,773
(2,10) 0,733 (5,6) 0,331 (7,13) 0,419 (10,7) 0,152 (13,2) 0,811
(2,11) 0,228 (5,7) 0,671 (8,1) 0,795 (10,8) 0,038 (13,3) 0,695
(2,12) 0,515 (5,8) 0,369 (8,2) 0,833 (10,9) 0,146 (13,4) 0,586
(2,13) 0,189 (5,9) 0,665 (8,3) 0,718 (10,11) 0,100 (13,5) 0,609
(3,1) 0,588 (5,10) 0,820 (8,4) 0,817 (10,12) 0,386 (13,6) 0,429
(3,2) 0,605 (5,11) 0,221 (8,5) 0,631 (10,13) 0,061 (13,7) 0,581
(3,4) 0,401 (5,12) 0,696 (8,6) 0,660 (11,1) 0,733 (13,8) 0,467
(3,5) 0,424 (5,13) 0,391 (8,7) 0,812 (11,2) 0,772 (13,9) 0,574
(3,6) 0,244 (6,1) 0,833 (8,9) 0,806 (11,3) 0,656 (13,10) 0,939
(3,7) 0,396 (6,2) 0,871 (8,10) 0,962 (11,4) 0,756 (13,11) 0,529
(3,8) 0,282 (6,3) 0,756 (8,11) 0,551 (11,5) 0,779 (13,12) 0,815
(3,9) 0,390
The overall dominance scores obtained are used in calculating the appraisal scores used to rank
alternatives. The appraisal scores calculated and ranking of the alternatives based on these scores are presented
in Table 6, along with best airline rankings shared with public by Airhelp to enable an easier comparison.
8. American Research Journal of Humanities Social Science (ARJHSS)R) 2019
ARJHSS Journal www.arjhss.com Page |45
Table 6. Appraisal Scores and Ranking of Alternatives
Alternatives A1 A2 A3 A4 A5 A6 A7 A8 A9 A10 A11 A12 A13
Scores 0,041 0,030 0,056 0,040 0,079 0,191 0,048 0,219 0,052 0,001 0,150 0,030 0,135
Rank 9 11 6 10 5 2 8 1 7 13 3 12 4
Best Airline
Score 6 5 12 9 2 10 7 1 3 4 13 8 11
When the ranking formed based on scores obtained with the help of the EVAMIX method are
compared to the ranking by Airhelp, some differences can be observed. Qatar Airways is in the first place both
in the best airline score ranking and also in the ranking using the EVAMIX method. The situation that this
airline is in the first place in both rankings based on different criteria can be interpreted such that this company
is in a distinct position in terms of superiority over other companies. The airlines that are ranked in the middle
positions in the best airline scores are ranked in the lowest positions in the ranking based on the EVAMIX
method. The lowest airlines there have moved to upper positions in this ranking. The reason might be that they
have younger fleets and they endeavor to include more airplanes in their fleets. It is obvious that the ranking
would alter when variables are changed.
IV. CONCLUSION
In today’s markets, companies are in a cutthroat struggle with each other. In this struggle, they
continuously work on internal evaluations, collect data and sometimes share this data with public in order to
showcase their innovations, their difference from the likes and their achievements in short term. The airline
companies which are important shareholders of the transportation sector which is one of the most popular
sectors in recent years implement various innovations and regulations in order to compete with other companies
because this industry has a wide range of customers, is prevalent, and is no longer a luxury but a necessity.
Certain tools are used to inform customers about these innovations, set strategies that can increase customer
satisfaction, and ensure customer loyalty. The indices frequently published in recent years are predominant
among these tools.
There are different indices that measure the performance of airlines. This study considers the Best
Airline Index shared with public each year by Airhelp. The calculation of this index takes into account results
obtained from the on-time performance, service quality, and claim processing measurements in order to rank
airline companies. In parallel with this, this study examines the first 15 airline companies in the list, which also
includes Turkish Airlines. However, Air Malta and Etihad Airways are excluded from the study because no
sufficient data on these airlines could be found. The ranking of the remaining 13 airline companies among
themselves are considered in this research.
The criteria used for making the best airline ranking are certainly important in distinguishing airlines
from each other. However, it is an inevitable fact that changing the criteria used in studies would also change the
ranking. Therefore, it is quite significant what perspective to assume in measurement and then present results in
these kinds of studies.
In this study, a new ranking is created using different criteria as an alternative to the ranking by
Airhelp. This alternative ranking is created based on the variables of average price per passenger, number of
people, retail value, fleet, ordered plane number and average age of fleet using the EVAMIX method which is
one of the multi-criteria decision-making methods.
In the conclusion of the study, Qatar Airways is the first in both rankings. This shows that Qatar
Airways surpasses other airlines in terms of the variables examined. However, there are differences in the rest of
the rankings. The companies ranked high in the list of Airhelp index such as Lufthansa and Singapore Airline
are in the middle zone in the ranking created with different criteria and a different method. On the other hand,
more strikingly, airline companies that are ranked low in the Airhelp list such as Turkish Airlines, Norwegian
Airlines, Wizz Air, and Flybe show a significant variation and are placed on the top of the new list.
The results obtained are found to be the indicator that change in criteria taken into account may cause
changes in the ranking obtained. For this reason, it is crucial to determine what is desired to measure when
9. American Research Journal of Humanities Social Science (ARJHSS)R) 2019
ARJHSS Journal www.arjhss.com Page |46
making the comparison. In this case, it gains important to prefer the rankings in which the appropriate criteria
are used in order to extract the information of interest because superiority of companies over each other may
vary substantially.
REFERENCES
[1]. Airhelp. (2018, December 18). Airline Worldwide Rankings 2018. December 18, 2018 Airhelp:
https://www.airhelp.com/en/airhelp-score/airline-ranking/ adresinden alındı
[2]. Airline Quality Rating. (2018, 12 7). AQR2018. 12 7, 2018 Airline Quality Rating:
https://airlinequalityrating.com/ adresinden alındı
[3]. Andalecio, M. (2010). Multi-criteria decision models for management of tropical coastal fisheries. A
review. Agronomy for Sustainable Development, 30 (3), 557-580.https://doi.org/10.1051/agro/2009051
[4]. Aytaç Adalı, E. (2016). EVAMIX ve TODIM Yöntemleri ile Sağlık Sektöründe Personel Seçimi.
Alphanumeric Journal, 4 (2), 71-83.http://dx.doi.org/10.17093/aj.2016.4.2.5000194528
[5]. Chatterjee, P., Mondal, S., & Chakraborty, S. (2014, March). A comparative study of preference
dominance-based approaches for selection of industrial robots. Advances in Production Engineering &
Management , 5-20.https://doi.org/10.14743/apem2014.1.172
[6]. Darji, V., & Rao, R. (2013). Application of AHP/EVAMIX Method for Decision Making in the
Industrial Environment. American Journal of Operations Research, 3, 542-
569.https://doi.org/10.4236/ajor.2013.36053
[7]. Diakoulaki, D., Mavrotas, G., & Papayannakis, L. (1995). Determining Objective Weights in Multiple
Criteria Problems: The CRITIC Method. Computers and Operations Research, 22 (7), 763-
770.https://doi.org/10.1016/0305-0548(94)00059-H
[8]. Jacdec. (2018, 12 11). The Jacdec Safety Index. 12 11, 2018 Jacdec: http://www.jacdec.de/about-
safety-ranking/ adresinden alındı
[9]. Jahan, A., Mustapha, F., Sapuan, S., Ismail, M., & Bahraminasab, M. (2012). A framework for
weighting of criteria in ranking stage of material selection process. The International Journal of
Advanced Manufacturing Technology, 58 (411), 420.https://doi.org/10.1007/s00170-011-3366-7
[10]. Klophaus, R., & Lordan, O. (2018). Codesharing Network Vulnerability of Global Airline Alliances.
Transportation Research Part A: Policy and Practice (111), 1-
10.https://doi.org/10.1016/j.tra.2018.02.010
[11]. Martel, J., & Matarazzo, B. (2005). Other Outranking Approaches. F. Salvatore, & G. Ehrgott içinde,
Multiple Criteria Decision Analysis: State of the Art Surveys (s. 197-259). New York:
Springer.https://doi.org/10.1007/0-387-23081-5_6
[12]. Skytrax . (2018, 12 7). World’s Top 100 Airlines 2018. 12 7, 2018 Skytrax World Airlines Awards:
https://www.worldairlineawards.com/worlds-top-100-airlines-2018/ adresinden alındı
[13]. Torlak, G., Sevkli, M., Sanal, M., & Zaim, S. (2011). Analyzing business competition by using fuzzy
TOPSIS method: An example of Turkish domestic airline industry. Expert Systems with Applications
(38), 3396-3406.https://doi.org/10.1016/j.eswa.2010.08.125
[14]. Voogd, H. (1982). Multicriteria Evaluation with Mixed Qualitative and Quantitative Data. Environment
and Planning B: Urban Analytics and City Science, 9 (2), 221-236.https://doi.org/10.1068/b090221
[15]. Wu, C., Wang, X., Zhang, X., Li, Y., & O'Brien, B. (2013). Chinese airline competitiveness evaluation
based on extended binary relative evaluation(BRE) model. Journal of Business Economics &
Management, 1 (14), 227-256.https://doi.org/10.3846/16111699.2012.721391
[16]. Yalçın, N., & Ünlü, U. (2018). A Multi-Criteria Performance Analysis Of Initial Public Offering (IPO)
Firms Using CRITIC And VIKOR Methods. Technological And Economic Development Of Economy,
24 (2), 534-560.https://doi.org/10.3846/20294913.2016.1213201
Assoc. Prof. Dr. Ozlem Deniz Basar,
Istanbul Commerce University Department of Statistics
Sutluce Mah., Imrahor Cad., No : 90, Beyoglu / Istanbul / Turkey Phone : +90-212-444-0413/4613