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

Wen-Min Lu (), Qian Long Kweh (), Mohammad Nourani and Hsiu-Fei Wang ()
Additional contact information
Wen-Min Lu: Chinese Culture University
Qian Long Kweh: Canadian University Dubai
Hsiu-Fei Wang: National Taichung University of Education

A chapter in Data-Enabled Analytics, 2021, pp 327-356 from Springer

Abstract: 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.

Keywords: Network data envelopment analysis; Grey prediction model; Second order cone programming; Big data; Airline energy efficiency; Undesirable output; Forecasting; Data enabled analytics (search for similar items in EconPapers)
Date: 2021
References: Add references at CitEc
Citations:

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:isochp:978-3-030-75162-3_12

Ordering information: This item can be ordered from
http://www.springer.com/9783030751623

DOI: 10.1007/978-3-030-75162-3_12

Access Statistics for this chapter

More chapters in International Series in Operations Research & Management Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().