A new hybrid simulation-based assignment approach for evaluating airlines with multiple service quality criteria

Evaluation of airlines based on service quality criteria can help to improve the processes of airlines, and also can give guidance to travel agencies to provide better choices for passengers and tourists. In this study, a hybrid simulation-based assignment approach is proposed to deal with multi-criteria decision-making problems with a group of decision-makers. A probability distribution is used to model decision-makers’ opinions and constructing a stochastic decision matrix. Then some efficient multi-criteria decision-making methods are utilized for evaluating alternatives in a simulation process. The proposed approach is applied to a problem of evaluation of five airlines with respect to opinions of 58 experts on 28 criteria. The results show the efficiency of the proposed to handle decision-making problems with a large number of experts. Moreover, the evaluation results are more reliable than the other decision-making approaches because of simulating decision-makers’ opinions, using multiple methods and evaluating based on aggregative results.

[1]  Sarfaraz Hashemkhani Zolfani,et al.  Planning the priority of high tech industries based on SWARA-WASPAS methodology: The case of the nanotechnology industry in Iran , 2015 .

[2]  Miroslavas Pavlovskis,et al.  Application of MCDM and BIM for Evaluation of Asset Redevelopment Solutions , 2016 .

[3]  Edmundas Kazimieras Zavadskas,et al.  Extended EDAS Method for Fuzzy Multi-criteria Decision-making: An Application to Supplier Selection , 2016, Int. J. Comput. Commun. Control.

[4]  Chong Liu,et al.  Algorithms for neutrosophic soft decision making based on EDAS, new similarity measure and level soft set , 2018, J. Intell. Fuzzy Syst..

[5]  I-Shuo Chen,et al.  A combined MCDM model based on DEMATEL and ANP for the selection of airline service quality improvement criteria: A study based on the Taiwanese airline industry , 2016 .

[6]  Anjali Awasthi,et al.  A hybrid approach based on SERVQUAL and fuzzy TOPSIS for evaluating transportation service quality , 2011, Comput. Ind. Eng..

[7]  Edmundas Kazimieras Zavadskas,et al.  Multi-criteria group decision-making using an extended EDAS method with interval type-2 fuzzy sets , 2017 .

[8]  Ying-Ming Wang,et al.  Fuzzy TOPSIS method based on alpha level sets with an application to bridge risk assessment , 2006, Expert Syst. Appl..

[9]  Edmundas Kazimieras Zavadskas,et al.  Multi-criteria evaluation of green suppliers using an extended WASPAS method with interval type-2 fuzzy sets , 2016 .

[10]  David Baker,et al.  Service Quality and Customer Satisfaction in the Airline Industry: A Comparison between Legacy Airlines and Low-Cost Airlines , 2013 .

[11]  Jin-woo Park,et al.  The effect of airline service quality on passengers' behavioural intentions: a Korean case study , 2004 .

[12]  Roland T. Rust,et al.  Service Quality: New Directions in Theory and Practice , 1993 .

[13]  Abbas Mardani,et al.  Multiple criteria decision-making techniques and their applications – a review of the literature from 2000 to 2014 , 2015 .

[14]  Noel Murray,et al.  Handbook On Import Risk Analysis for animals and animal products: quantitative risk assessment , 2004 .

[15]  Sheng-Hshiung Tsaur,et al.  The evaluation of airline service quality by fuzzy MCDM. , 2002 .

[16]  Ching-Lai Hwang,et al.  Multiple Attribute Decision Making: Methods and Applications - A State-of-the-Art Survey , 1981, Lecture Notes in Economics and Mathematical Systems.

[17]  J. Kacprzyk.  Group decision making with a fuzzy linguistic majority , 1986 .

[18]  Fei Ye,et al.  An extended TOPSIS method with interval-valued intuitionistic fuzzy numbers for virtual enterprise partner selection , 2010, Expert Syst. Appl..

[19]  David L. Olson,et al.  Service quality and productivity in the U.S. airline industry: a service quality-adjusted DEA model , 2015 .

[20]  Jeng-Ming Yih,et al.  An evaluation of airline service quality using the fuzzy weighted SERVQUAL method , 2011, Appl. Soft Comput..

[21]  Nick T. Thomopoulos,et al.  Essentials of Monte Carlo Simulation: Statistical Methods for Building Simulation Models , 2012 .

[22]  Manik Chandra Das,et al.  A framework to measure relative performance of Indian technical institutions using integrated fuzzy AHP and COPRAS methodology , 2012 .

[23]  Ching-Fu Chen.  Investigating structural relationships between service quality, perceived value, satisfaction, and behavioral intentions for air passengers: Evidence from Taiwan , 2008 .

[24]  N. Suki.  Passenger satisfaction with airline service quality in Malaysia: A structural equation modeling approach , 2014 .

[25]  Morteza Yazdani,et al.  Intuitionistic fuzzy edas method: an application to solid waste disposal site selection , 2017 .

[26]  Chao-Che Hsu,et al.  Improving airline service quality based on rough set theory and flow graphs , 2016 .

[27]  N. Malys,et al.  Comparative analysis of MCDM methods for the assessment of sustainable housing affordability , 2016 .

[28]  Edmundas Kazimieras Zavadskas,et al.  Multi-Criteria Inventory Classification Using a New Method of Evaluation Based on Distance from Average Solution (EDAS) , 2015, Informatica.

[29]  Dragisa Stanujkic,et al.  An Extension of the EDAS Method Based on the Use of Interval Grey Numbers , 2017 .

[30]  Edmundas Kazimieras Zavadskas,et al.  Application of multiple criteria decision making techniques in tourism and hospitality industry: a systematic review , 2016 .

[31]  Alireza Sotoudeh-Anvari,et al.  A comprehensive MCDM-based approach using TOPSIS, COPRAS and DEA as an auxiliary tool for material selection problems , 2017 .

[32]  Martyn D Kirk,et al.  Proportion of illness acquired by foodborne transmission for nine enteric pathogens in Australia: an expert elicitation. , 2014, Foodborne pathogens and disease.

[33]  Hideki Aoyama,et al.  An Integrated Approach of Fuzzy Linguistic Preference Based AHP and Fuzzy COPRAS for Machine Tool Evaluation , 2015, PloS one.

[34]  Morteza Yazdani,et al.  New integration of MCDM methods and QFD in the selection of green suppliers , 2016 .

[35]  Mohammad Izadikhah,et al.  Extension of the TOPSIS method for decision-making problems with fuzzy data , 2006, Appl. Math. Comput..

[36]  Mark Chang,et al.  Monte Carlo Simulation for the Pharmaceutical Industry: Concepts, Algorithms, and Case Studies , 2010 .

[37]  Snežana Urošević,et al.  An approach to personnel selection based on Swara and Waspas methods , 2016 .

[38]  Miloš Madić,et al.  Selection Of Cutting Inserts For Aluminum Alloys Machining By Using MCDM Method , 2015 .

[39]  Malcolm Anderson,et al.  Use of the Beta Distribution To Determine Well-Log Shale Parameters , 2009 .

[40]  F. Pakdil,et al.  Expectations and perceptions in airline services: An analysis using weighted SERVQUAL scores , 2007 .

[41]  E. Zavadskas,et al.  Optimization of Weighted Aggregated Sum Product Assessment , 2012 .

[42]  Gwo-Hshiung Tzeng,et al.  A modified VIKOR multiple-criteria decision method for improving domestic airlines service quality , 2011 .

[43]  Khalil Md Nor,et al.  Development of TOPSIS Method to Solve Complicated Decision-Making Problems - An Overview on Developments from 2000 to 2015 , 2016, Int. J. Inf. Technol. Decis. Mak..

[44]  K. Smallbone,et al.  An assessment of sustainable housing affordability using a multiple criteria decision making method , 2013 .

[45]  Edmundas Kazimieras Zavadskas,et al.  Fuzzy multiple criteria decision-making techniques and applications - Two decades review from 1994 to 2014 , 2015, Expert Syst. Appl..

[46]  Zeshui Xu,et al.  Hesitant fuzzy multi-attribute decision making based on TOPSIS with incomplete weight information , 2013, Knowl. Based Syst..

[47]  Omid Bozorg-Haddad,et al.  WASPAS Application and Evolutionary Algorithm Benchmarking in Optimal Reservoir Optimization Problems , 2017 .

[48]  Nara Medianeira Stefano,et al.  COPRAS (Complex Proportional Assessment): state of the art research and its applications , 2015 .

[49]  Yonghwi Noh,et al.  Airline customer satisfaction and loyalty: impact of in-flight service quality , 2009 .

[50]  Jurgita Antucheviciene,et al.  A Hybrid Model Based on Fuzzy AHP and Fuzzy WASPAS for Construction Site Selection , 2015, Int. J. Comput. Commun. Control.

[51]  Tabasam Rashid,et al.  TOPSIS for Hesitant Fuzzy Linguistic Term Sets , 2013, Int. J. Intell. Syst..

[52]  Sunday Ayoola Oke,et al.  A fuzzy-grey-weighted aggregate sum product assessment methodical approach for multi-criteria analysis of maintenance performance systems , 2017, Int. J. Syst. Assur. Eng. Manag..

[53]  Ming-Shin Kuo,et al.  A novel interval-valued fuzzy MCDM method for improving airlines’ service quality in Chinese cross-strait airlines , 2011 .

[54]  D. Vose.  Risk Analysis: A Quantitative Guide , 2000 .

[55]  E. Zavadskas,et al.  Multiple nash equilibriums and evaluation of strategies. New application of MCDM methods , 2014 .

[56]  K. Rathi,et al.  A Mathematical Model for Subjective Evaluation of Alternatives in Fuzzy Multi-Criteria Group Decision Making Using COPRAS Method , 2017, Int. J. Fuzzy Syst..

[57]  A. Parasuraman,et al.  SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. , 1988 .

[58]  Edmundas Kazimieras Zavadskas,et al.  Multiple criteria decision-making techniques in transportation systems: a systematic review of the state of the art literature , 2015 .

[59]  Yu-Hern Chang,et al.  Examining airline service quality from a process perspective , 2005 .

[60]  Edmundas Kazimieras Zavadskas,et al.  Using fuzzy multiple criteria decision making approaches for evaluating energy saving technologies and solutions in five star hotels: a new hierarchical framework , 2016 .

[61]  Edmundas Kazimieras Zavadskas,et al.  Application of multiple-criteria decision-making techniques and approaches to evaluating of service quality: a systematic review of the literature , 2015 .

[62]  Zenonas Turskis,et al.  A novel hybrid multi-criteria decision-making model to assess a stairs shape for dwelling houses , 2016 .

[63]  Chen-Tung Chen,et al.  Extensions of the TOPSIS for group decision-making under fuzzy environment , 2000, Fuzzy Sets Syst..

[64]  Diyar Akay,et al.  A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method , 2009, Expert Syst. Appl..

[65]  Madjid Tavana,et al.  A novel hybrid social media platform selection model using fuzzy ANP and COPRAS-G , 2013, Expert Syst. Appl..

[66]  Shyi-Ming Chen,et al.  Fuzzy multiple attributes group decision-making based on the interval type-2 TOPSIS method , 2010, Expert Syst. Appl..

[67]  Juliet Namukasa.  The influence of airline service quality on passenger satisfaction and loyalty : The case of Uganda airline industry , 2013 .

[68]  Theingi,et al.  Service quality, satisfaction, and behavioural intentions , 2009 .

[69]  C. Hwang.  Multiple Objective Decision Making - Methods and Applications: A State-of-the-Art Survey , 1979 .

[70]  Ting-Yu Chen,et al.  The interval-valued fuzzy TOPSIS method and experimental analysis , 2008, Fuzzy Sets Syst..