Data analytics in managing aircraft routing and maintenance staffing with price competition by a Stackelberg-Nash game model

被引：30

作者：

Eltoukhy, Abdelrahman E. E. ^{[1
]}

Wang, Z. X. ^{[2
]}

Chan, Felix T. S. ^{[1
]}

Fu, X. ^{[3
]}

机构：

[1] Hong Kong Polytech Univ, Dept Ind & Syst Engn, Hung Hun, Hong Kong, Peoples R China

[2] Dongbei Univ Finance & Econ, Sch Business Adm, Dalian, Peoples R China

[3] Univ Sydney, Inst Transport & Logist Studies, Sydney, NSW, Australia

来源：

TRANSPORTATION RESEARCH PART E-LOGISTICS AND TRANSPORTATION REVIEW | 2019年 / 122卷

关键词：

Data analytics; Aircraft routing problem; Maintenance staffing problem; Game theory; ARTIFICIAL NEURAL-NETWORKS; LINEAR-REGRESSION MODEL; BIG DATA ANALYTICS; CHANNEL STRUCTURE; SARIMA MODEL; SUPPLY CHAIN; OPTIMIZATION; CONFIGURATION; ALGORITHM; DEMAND;

D O I：

10.1016/j.tre.2018.12.002

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This study develops a Stackelberg-Nash game model (SNGM) to capture the interdependence between aircraft routing of airlines and maintenance staffing of maintenance providers, and to consider the price competition among maintenance providers. The SNGM's overall Nash equilibrium is obtained using an iterative game algorithm. The SNGM effectiveness is demonstrated with a case study, in which a neural network-based algorithm is developed to forecast accurate non-propagated delays, and a multiple linear regression algorithm is adopted to predict demand price relationship for each maintenance provider. The results reveal cost savings of about 26% and 22% for the airline and the maintenance providers, respectively.

引用

页码：143 / 168

页数：26

共 8 条

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