Using Network DEA and Grey Prediction Model for Big Data Analysis: An Application in the Global Airline Efficiency

dc.contributor.author Lu, Wen-Min
dc.contributor.author Kweh, Qian Long
dc.contributor.author Nourani, Mohammad
dc.contributor.author Wang, Hsiu-Fei
dc.date.accessioned 2022-02-07T09:01:17Z
dc.date.available 2022-02-07T09:01:17Z
dc.date.copyright © 2021
dc.date.issued 2021
dc.description This book chapter is not available at CUD collection. The version of scholarly record of this book chapter is published in International Series in Operations Research and Management Science (2021), available online at: https://doi.org/10.1007/978-3-030-75162-3_12 en_US
dc.description.abstract This study proposes a big data enabled analytics approach to extract the valuable information through network Data Envelopment Analysis (DEA) integrated with multiplicative efficiency aggregation (MEA) and Grey Prediction Model. That is, when dealing with large volumes of data, network DEA can uncover hidden information that are valuable for decision making. To illustrate the application, we develop an airline operational framework in a network structure, whose operation is often time too complex. With a two-stage network DEA model in the form of second-order cone programming (SOCP) that solves issues related to nonlinearity, we also address the undesirable output of carbon dioxide emissions and solve potentially nonconvex optimization problem in revealing the energy efficiency and revenue efficiency of 23 global airlines over the period of 2013–2017. In this evaluation, carbon dioxide emissions exit the first stage without being inputted in the second stage with the assumption of variable returns to scale. This study also estimates and predicts airline efficiency by integrating the network DEA with grey prediction model, which assesses the impacts of all ratio combinations of inputs, intermediates, and outputs on overall efficiency. Overall, this study proposes an approach to transform large volumes of data into multiple useful information, and hence, extracts the value dimension of big data hidden in the airline operation. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG. en_US
dc.identifier.citation Lu, W. -., Kweh, Q. L., Nourani, M., & Wang, H. -. (2021). Using network DEA and grey prediction model for big data analysis: An application in the global airline efficiency, International Series in Operations Research & Management Science, vol 312. Springer, Cham, 312, https://doi.org/10.1007/978-3-030-75162-3_12 en_US
dc.identifier.issn 08848289
dc.identifier.uri https://doi.org/10.1007/978-3-030-75162-3_12
dc.identifier.uri http://hdl.handle.net/20.500.12519/509
dc.language.iso en en_US
dc.publisher Springer en_US
dc.relation Authors Affiliations : Lu, W.-M., Department of International Business Administration, Chinese Culture University, Taipei, Taiwan; Kweh, Q.L., Faculty of Management, Canadian University Dubai, Dubai, United Arab Emirates; Nourani, M., The University of Waikato Joint Institute at Zhejiang University City College, University of Waikato, Hangzhou, China; Wang, H.-F., Graduate Institute of Educational Information and Measurement, National Taichung University of Education, Taichung, Taiwan
dc.relation.ispartofseries International Series in Operations Research and Management Science;Volume 312
dc.rights License to reuse the abstract has been secured from Springer Nature and Copyright Clearance Center.
dc.rights.holder Copyright : © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.
dc.rights.uri https://s100.copyright.com/CustomerAdmin/PLF.jsp?ref=2aa39d5e-9646-454c-9485-773bbb0238a2
dc.subject Airline energy efficiency en_US
dc.subject Big data en_US
dc.subject Data enabled analytics en_US
dc.subject Forecasting en_US
dc.subject Grey prediction model en_US
dc.subject Network data envelopment analysis en_US
dc.subject Second order cone programming en_US
dc.subject Undesirable output en_US
dc.title Using Network DEA and Grey Prediction Model for Big Data Analysis: An Application in the Global Airline Efficiency en_US
dc.type Book chapter en_US
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