Bus Fleet Management Optimization Using the Augmented Weighted Tchebycheff Method

  • William Emiliano
  • Lino Costa
  • Maria do Sameiro Carvalho
  • José Telhada
Conference paper
Part of the Springer Proceedings in Mathematics & Statistics book series (PROMS, volume 223)

Abstract

This paper presents a multi-objective optimization model for the buses fleet management problem. This model is solved using the Augmented Weighted Tchebycheff method. The aim is to minimize three objective functions, \(Z_1\) (CO\(_2\) emissions), \(Z_2\) (other types of emissions) and \(Z_3\) (total costs), for a bus fleet that uses four types of buses: diesel, electric bus, electric bus of fast charging, and Compressed Natural Gas (CNG). A public transport (PT) company of Joinville, Brazil, where it operates three different PT lines, owns the fleet. The respective data was modelled and optimized using the MS Excel solver. Results provided interesting insights concerning the most adequate strategy for bus selection according with public transport line characteristics and taking into account trade-off between costs and emissions. The results indicate that optimal solutions include the diesel in the Itinga line and the CNG in the South line. The electric bus is more adequate in the South-North line due to the large number of stops and low average speed. However, when the costs are disregarded, in some scenarios, the best option is the electric bus for all lines.

Keywords

Augmented weighted tchebycheff Buses fleet management problem Ghg emissions 

Notes

Acknowledgements

This work has been supported by CNPq (National Counsel of Technological and Scientific Development, Brazil) and COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundação para a Ciência e Tecnologia within the Project Scope: UID/CEC/00319/2013.

References

  1. 1.
    J. Aber, Electric bus analysis for new york city transit. Master’s thesis, Columbia University (2016)Google Scholar
  2. 2.
    U.-D. Choi, H.-K. Jeong, S.-K. Jeong, Commercial operation of ultra low floor electric bus for seoul city route, in 2012 IEEE Vehicle Power and Propulsion Conference, 1128–1133 Oct 2012.  https://doi.org/10.1109/VPPC.2012.6422619
  3. 3.
    K. Dächert, J. Gorski, K. Klamroth, An augmented weighted Tchebycheff method with adaptively chosen parameters for discrete bicriteria optimization problems. Comput. Oper. Res. 39(12), 2929–2943 (2012), ISSN 0305-0548.  https://doi.org/10.1016/j.cor.2012.02.021. http://www.sciencedirect.com/science/article/pii/S0305054812000470
  4. 4.
    C. Desai, F. Berthold, S.S. Williamson, Optimal drivetrain component sizing for a plug-in hybrid electric transit bus using multi-objective genetic algorithm, in 2010 IEEE Electrical Power Energy Conference, 1–5 Aug 2010.  https://doi.org/10.1109/EPEC.2010.5697242
  5. 5.
    W.M. Emiliano, P. Afonso, J. Telhada. Income-weighted life-cycle cost model: an application to the case of electric versus diesel buses state of the art, in APMS International Conference, (Springer, Berlin, 2016), pp. 1–8Google Scholar
  6. 6.
    T. Ercan, Y. Zhao, O. Tatari, J.A. Pazour, Optimization of transit bus fleet’s life cycle assessment impacts with alternative fuel options. Energy 93(1), 323–334 (2015). ISSN 0360-5442,  https://doi.org/10.1016/j.energy.2015.09.018. http://www.sciencedirect.com/science/article/pii/S0360544215012104
  7. 7.
    L. Eudy, R. Prohaska, K. Kelly, M. Post, Foothill Transit Battery Electric Bus Demonstration Results, NREL (2016).  https://doi.org/10.2172/1237304. http://www.osti.gov/scitech/servlets/purl/1237304
  8. 8.
    W. Feng, M. Figliozzi, An economic and technological analysis of the key factors affecting the competitiveness of electric commercial vehicles: a case study from the usa market. Transp. Res. Part C: Emerg. Technol. 26, 135–145 (2013). ISSN 1613-7159,  https://doi.org/10.1016/j.trc.2012.06.007. http://www.sciencedirect.com/science/article/pii/S0968090X12000897
  9. 9.
    W. Feng, M. Figliozzi, Vehicle technologies and bus fleet replacement optimization: problem properties and sensitivity analysis utilizing real-world data. Public Transport 6(1), 137–157 (2014). ISSN 1613-7159,  https://doi.org/10.1007/s12469-014-0086-z
  10. 10.
    C.L. Hwang, Multiple Objective Decision Making - Methods and Applications, vol. 164, LNEMS (Springer, Berlin, 1979)Google Scholar
  11. 11.
    Instituto de Pesquisa e Planejamento para o Desenvolvimento Sustentável de Joinville (IPPUJ). https://ippuj.joinville.sc.gov.br/. Accessed 26 Feb 2017
  12. 12.
    K. Miettinen, Nonlinear multiobjective optimization (Kluwer Academic Publishers, Boston, 1999)Google Scholar
  13. 13.
    S. Mishra, S. Sharma, T. Mathew, S. Khasnabis, Multiobjective optimization model for transit fleet resource allocation. Transp. Res. Rec. J. Transp. Res. Board 2351, 1–13 (2013).  https://doi.org/10.3141/2351-01
  14. 14.
    K.P. Nurjanni, M.S. Carvalho, L. Costa, Green supply chain design: a mathematical modeling approach based on a multi-objective optimization model. Int. J. Prod. Econ. 183(Part B), 421–432 (2017), ISSN 0925-5273.  https://doi.org/10.1016/j.ijpe.2016.08.028. http://www.sciencedirect.com/science/article/pii/S0925527316302237
  15. 15.
    Z. Qiang, D. Lianbo, G. Wai, Z. Wenliang, Optimization method for train plan of urban rail transit with elastic demands, in CICTP 2012 (American Society of Civil Engineers, 2012), pp. 1189–1199.  https://doi.org/10.1061/9780784412442.180
  16. 16.
    J.P. Ribau, J.M.C. Sousa, C.M. Silva, Reducing the carbon footprint of urban bus fleets using multi-objective optimization. Energy 93(Part 1), 1089–1104 (2015). ISSN 0360-5442,  https://doi.org/10.1016/j.energy.2015.09.112. http://www.sciencedirect.com/science/article/pii/S0360544215013225
  17. 17.
    U.F. Siddiqi, Y. Shiraishi, S.M. Sait, Multi-objective optimal path selection in electric vehicles. Artif. Life Robot. 17(1), 113–122 (2012). ISSN 1614-7456,  https://doi.org/10.1007/s10015-012-0025-5
  18. 18.
    R.E. Steuer, Multiple Criteria Optimization: Theory (Computation and Application. John Wiley, New York, 1986)Google Scholar

Copyright information

© Springer International Publishing AG 2018

Authors and Affiliations

  • William Emiliano
    • 1
  • Lino Costa
    • 1
  • Maria do Sameiro Carvalho
    • 1
  • José Telhada
    • 1
  1. 1.Algoritmi Research Center, Universidade do MinhoBragaPortugal

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