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Evaluating Airline Network Robustness Using Relative Total Cost Indices

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Industrial Engineering in the Industry 4.0 Era

Part of the book series: Lecture Notes in Management and Industrial Engineering ((LNMIE))

Abstract

This research to the best of our knowledge is the first paper to quantify airline network robustness in the presence reversible capacity of legs and alternative flights. In this study, we try to recognize the critical legs via changing the functional capacity of flights. Besides, we attempt to gauge the behavior of the flight network via shifting of leg capacities proposing a new leg cost function. In addition, we indicate how to capture the robustness of airline network in the case of variable flight capacities. Relative Total Cost Indices have been used to assess air network robustness in the case of behavior associated with both User-Optimization and System-Optimization. In this article from the different point of view, the variability of passenger’s route preferences is the main subject. This paper may shed light on the robustness of networks in real life not only for the particular case of airlines but also for systems sharing similar topological properties. The paper presents a numerical case study with real data from an airline in Turkey for illustration purposes.

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References

  • Abdelghany KF, Abdelghany AF, Ekollu G (2008) An integrated decision support tool for airlines schedule recovery during irregular operations. Eur J Oper Res 185:825–848

    Article  MATH  Google Scholar 

  • Bazaraa MS, Sherali HD, Shetty CM (2006) Nonlinear programming: theory and algorithms 853

    Google Scholar 

  • Bazargan M (2010) Airline operations and scheduling, 2nd edn. Ashgate Publishing Company

    Google Scholar 

  • Boccaletti S, Bianconi G, Criado R, del Genio CII, Gomez-Gardenes J, Romance M, Sendina-Nadal I, Wang Z, Zanin M (2014) The structure and dynamics of multilayer networks. Phys Rep 544(1):1–122

    Article  MathSciNet  Google Scholar 

  • Boyce DE, Mahmassani Charles HS, John AN (2004) A retrospective on Beckmann, McGuire and Winsten’s. Wall Str J 1–27

    Google Scholar 

  • Buldyrev SV, Parshani R, Paul G, Stanley HE, Havlin S (2010) Catastrophic cascade of failures in interdependent networks. Nature 464(7291):1025–1028

    Article  Google Scholar 

  • Campbell JF (2009) Hub location for time definite transportation. Comput Oper Res 36(12):3107–3116

    Article  MATH  Google Scholar 

  • Dafermos S (1980) Traffic equilibrium and variational inequalities. Transp Sci 14(1):42–54

    Article  MathSciNet  Google Scholar 

  • de Dios OJ, Willumsen LG (2001) Modelling transport, 3rd edn. Wiley, Chichester, UK

    Google Scholar 

  • Du W-B, Zhou X-L, Lordan O, Wang Z, Zhao C, Zhu Y-B (2016) Analysis of the Chinese airline network as multi-layer networks. Transp Res Part E Logist Transp Rev 89:108–116

    Article  Google Scholar 

  • Eskandarpour M, Dejax P, Miemczyk J, Peton O (2015) Sustainable supply chain network design: an optimization-oriented review. Omega (United Kingdom) 54:11–32

    Google Scholar 

  • Hong C, He N, Lordan O, Liang B-Y, Yin N-Y (2017) Efficient calculation of the robustness measure R for complex networks Chen. Phys A Stat Mech Appl 478:63–68

    Article  Google Scholar 

  • Jindal A, Sangwan KS, Saxena S (2015) Network design and optimization for multi-product, multi-time, multi-echelon closed-loop supply chain under uncertainty. Procedia CIRP 29:656–661

    Article  Google Scholar 

  • Konnov I (2007) Equilibrium models and variational inequalities. Elsevier B.V

    Google Scholar 

  • Lordan O, Sallan JM, Simo P, Gonzalez-prieto D (2015) Robustness of airline alliance route networks. Commun Nonlinear Sci Numer Simul 22(1–3):587–595

    Article  Google Scholar 

  • Nagurney A (2003) Influence of Beckmann, McGuire, and Winsten’s studies in the economics of transportation on innovations in modeling, methodological developments, and applications. Studies in the Economics of Transportation

    Google Scholar 

  • Nagurney A (2010) Identifying vulnerabilities and synergies in a uncertain age. 1–53

    Google Scholar 

  • Nagurney A, Qiang Q (2008a) A network efficiency measure with application to critical infrastructure networks. J Glob Optim 40(1–3):261–275

    Article  MathSciNet  MATH  Google Scholar 

  • Nagurney A, Qiang Q (2008) Identification of critical nodes and links in financial networks with intermediation and electronic transactions. Comput Methods Financ Eng 1758:273–297

    Google Scholar 

  • Newman MEJ (2003) The structure and function of complex networks, vol 45, no 2, pp 167–256. E-Print Cond-Mat/0303516

    Google Scholar 

  • Sarvari PA, Erol S (2013) Airline network: critical leg assessment via variation in practical capacity. J Traffic Logist Eng 1(2):228–232

    Article  Google Scholar 

  • Sarvari PA, Ustundag A, Takci H (2016) Performance evaluation of different customer segmentation approaches based on RFM and demographics analysis. Kybernetes 45(7):1129–1157

    Article  Google Scholar 

  • Schneider CM, Moreira AA, Andrade JS, Havlin S, Herrmann HJ (2011) Mitigation of malicious attacks on networks. Proc Natl Acad Sci USA 108(10):3838–3841

    Article  Google Scholar 

  • Science N, Phenomena C, Hong C, Zhang J, Cao X, Du W (2016) Chaos, solitons and fractals structural properties of the Chinese air transportation multilayer network. Chaos, Solitons Fractals 86:28–34

    Article  Google Scholar 

  • Soysal M, Bloemhof-Ruwaard JM, van der Vorst JGAJ (2014) Modelling food logistics networks with emission considerations: the case of an international beef supply chain. Int J Prod Econ 152:57–70

    Article  Google Scholar 

  • Storn R (1996) On the usage of differential evolution for function optimization 519–523

    Google Scholar 

  • Tan F, Xia Y, Wei Z (2015) Robust-yet-fragile nature of interdependent networks. Phys Rev E Stat Nonlinear, Soft Matter Phys 91(5):1–7

    Google Scholar 

  • Trajanovski S, Scellato S, Leontiadis I (2012) Error and attack vulnerability of temporal networks. Phys Rev E Stat Nonlinear, Soft Matter Phys 85(6):1–10

    Google Scholar 

  • Wardrop JG, Whitehead JI (1952) Correspondence some theoretical aspects of road traffic research. ICE Proc Eng Div 1(5):767–768

    Google Scholar 

  • Wei D, Deng X, Zhang X, Deng Y, Mahadevan S (2013) Identifying influential nodes in weighted networks based on evidence theory. Phys Stat Mech Appl 392(10):2564–2575

    Article  Google Scholar 

  • Wu CL (2006) Improving airline network robustness and operational reliability by sequential optimisation algorithms. Netw Spat Econ 6(3–4):235–251

    Article  MathSciNet  MATH  Google Scholar 

  • Wu B, Yan XP, Wang Y, Wei XY (2016) Quantitative method to human reliability assessment for maritime accident 16(4):24–30

    Google Scholar 

  • Yan G, Zhou T, Hu B, Fu Z-Q, Wang B-H (2006) Efficient routing on complex networks. Phys Rev E Stat Nonlin Soft Matter Phys 73:46108

    Google Scholar 

  • Zhang J, Bin Cao X, Du WB, Cai KQ (2010) Evolution of Chinese airport network. Phys A Stat Mech Appl 389(18):3922–3931

    Article  Google Scholar 

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Author information

Authors and Affiliations

  1. Luxembourg Institute of Science and Technology, Esch-Sur-Alzette, Luxembourg

    Peiman Alipour Sarvari

  2. Industrial Engineering Department, Management Faculty, Istanbul Technical University, Istanbul, Turkey

    Fethi Calisir

Authors
  1. Peiman Alipour Sarvari
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  2. Fethi Calisir
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Corresponding author

Correspondence to Peiman Alipour Sarvari .

Editor information

Editors and Affiliations

  1. Industrial Engineering Department, Istanbul Technical University, Macka/Istanbul, Turkey

    Fethi Calisir

  2. Management Engineering Department, Istanbul Technical University, Macka/Istanbul, Turkey

    Hatice Camgoz Akdag

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Sarvari, P.A., Calisir, F. (2018). Evaluating Airline Network Robustness Using Relative Total Cost Indices. In: Calisir, F., Camgoz Akdag, H. (eds) Industrial Engineering in the Industry 4.0 Era. Lecture Notes in Management and Industrial Engineering. Springer, Cham. https://doi.org/10.1007/978-3-319-71225-3_5

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  • DOI: https://doi.org/10.1007/978-3-319-71225-3_5

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-71224-6

  • Online ISBN: 978-3-319-71225-3

  • eBook Packages: EngineeringEngineering (R0)

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