Engineering and Applied Sciences

| Peer-Reviewed |

Economic Life Cycle versus Lifespan – A Case Study of an Urban Bus Fleet

Received: 17 October 2018    Accepted: 07 November 2018    Published: 10 June 2019
Views:       Downloads:

Share This Article

Abstract

The maintenance policy and the quality of service throughout the bus’s life cycle can be measured through their costs along time that, when evaluated using Lifespan or Economic Life Cycle methods, allow to determine the renewal or the replacement time. The paper discusses these two models, using real data from an urban bus fleet company. The maths that supports the models are presented. They are considered the functioning and maintenance costs, and also the apparent rate. The Life Cycle Cost of an urban transport bus is strongly dependent on the policy and quality of its maintenance, from which it depends on its reliability and availability. The final result is reflected on its Life Cycle Cost, that can be evaluated through the Lifespan or the Economic Life Cycle methods. Other aspects that can be considered are the fuel costs and the type of terrain, because they are intrinsically interrelated and have a strong effect on costs, namely because they imply strong variation in the bus’s consumption and in their maintenance costs. As the company considered in the case study has a poor maintenance policy, it makes the analysis challenging, making difficult to compare the economic life cycle with the lifespan method in this situation. However, the results and conclusions that are taken from them are obvious, what demonstrates the models’ utility and robustness.

DOI 10.11648/j.eas.20190402.12
Published in Engineering and Applied Sciences (Volume 4, Issue 2, April 2019)
Page(s) 30-43
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Equipment Removal, LCC, Lifespan, Assignment, Condition Monitoring, Scheduled Maintenance

References
[1] Aoudia, M.; Belmokhtar, O., (2008): Economic impact of maintenance management ineffectiveness of na oil gas company. Journal of Quality in Maintenance Engineering, Vol. 14, N. 3, pp. 237-261.
[2] Assis, R., (2010): Apoio à decisão em manutenção na gestão de activos fisicos. Lisboa: 1ª Edição, Lidel – Edições técnicas, Lda. EAN 978-9727576050. ISBN 9789727576050.
[3] Assis R.; Julião, J. (2009): Gestão da Manutenção ou Gestão de Activos? (custos ao longo do Ciclo de Vida). Comunicação 10º Congresso Nacional Manutenção, APMI, Figueira da Foz, Portugal.
[4] Bescherer, F (2005): Established Life Cycle Concepts in the Business Environment – Introduction and terminology. Laboratory of Industrial Management Report Series, report 1/2005, Helsinki University.
[5] Lindholm, A. and Suomala, P (2004): The possibilities of Life Cycle Costing in Outsourcing Decision Making. Frontiers of E-Business Research. pp226-241. http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.457.8317&rep=rep1&type=pdf, accessed on 2018.10.01.
[6] Eric Korpi, Timo Ala‐Risku, (2008) "Life cycle costing: a review of published case studies", Managerial Auditing Journal, Vol. 23, N.3, pp.240-261, https://doi.org/10.1108/02686900810857703.
[7] Senthil Kumaran Durairaj, S. K. Ong, A. Y. C. Nee and R. B. H. Tan (2002): Evaluation of life cycle cost analysis methodologies. Corporate Environmental Strategy, Vol. 9, N. 1, pp. 30-39.
[8] Emblemsvag, J. (2001): Activity-based life-cycle costing. Managerial Auditing Journal, Vol. 16, N. 1, pp. 17-27. DOI: 10.1108/02686900110363447.
[9] International Electrotechical Commissioning (2004): IEC 60300-3-3: Dependability management – Part 3-3: life cycle cost analysis – Application Guide. Chicago, II.
[10] ASTM International (2002): Standard practice for measuring life-cycle costs of buildings and buildingsystem. Annual Book of ASTM Standards: 2002, Vol. 4, ASTM International West Conshohocken, PA, E 917, N. 11.
[11] BAS PAS 55 (2008): Asset Management: PAS 55-1, Part 1: Specification for the optimized managementof physical assets | PAS 55-2, Part 2: Guidelines for the application of PAS 55-1. British Standards, UK.
[12] Farinha, J. M. T. (2011): Manutenção – A Terologia e as Novas Ferramentas de Gestão. Lisboa: 1ª Edição, Monitor – Projecto e Edições, Lda.
[13] Oliveira, J. A. N. (1982): Engenharia Económica – Uma abordagem às Decisões de Investimento. São Paulo: McGraw-Hill do Brasil.
[14] William, G. S.; Thomas, N. M.; Eileen M. V. A. (2002): Equipment replacement decisions and lean manufacturing. ELSEVIER.
[15] Jennifer, L. R. and Joseph C. H. (2005): Equipment replacement under continuous and discontinuous technological change. IMA Journal of Management Mathematics; Vol. 16, N. 1.
[16] Natali, H.; Yuri Y. (2007): Optimal equipment replacement without paradoxes: A continuous analysis. Operations Research Letters. ELSEVIER, March, Vol. 35, N. 2, pp. 245–250.
[17] Assaf, A. N. (2005): Finanças corporativas e valor. São Paulo: Atlas, Brazil.
[18] Casarotto Filho, N. (2000): Análise de investimentos - matemática financeira, engenharia económica, tomada de decisão, estratégia empresarial. – 9. ed. — São Paulo: Atlas, Brazil. EAN 978-8522425723. ISBN 9788522425723.
[19] Vey, I. H.; Rosa, R. M. (2004): Substituição de frota em empresa de transporte municipal de passageiros: um estudo de caso. Universidade Federal de Santa Maria, Brazil.
[20] Motta, R. R.; Calôba, G. M. (2002): Análise de investimentos: tomada de decisão em projetos industriais. São Paulo: Atlas, Brazil.
[21] Feldens, A. G.; Muller, C. J.; Filomena, T. P.; Neto, F. J. K.; Castro, A. S.; Anzanello, M. J. (2010): Política para Avaliação e Substituição de Frota por Meio da Adoção de Modelo Multicritério. Porto Alegre, Brazil. ISSN 1980-4814.
[22] Pinar, K.; Hartman, J. (2004), Case Study: Bus Fleet Replacement. The Engineering Economist, Vol. 49 N.3, pp. 253-278.
[23] Khasnabis, S.; Alsaidi, E.; Ellis, R. (2002): Optimal allocation of resources to meet transit fleet requirements. Journal of Transportation Engineering, Vol. 128, N. 6, pp. 509-518.
[24] Di, J.; Hauke, L. (2000): Optimal fleet utilization and replacement. Transportation Research Part E, 36E (1): 3.
[25] Campos, L. C. D.; Vellasco, M. M. B. R.; Lazo, J. G. L. (2010): Um modelo estocástico baseado em redes neurais. UFJF, Juiz de Fora, Brazil.
[26] Amaya, E. J.; Tonaco, R.; Souza, R. Q.; Álvares, A. J. (2007): Sistema Inteligente de Manutenção Baseada em Condição para Usina Hidrelética de Balbina. Universidade de Brasília, Departamento de Engenharia Mecânica e Mecatrônica, Grupo de Inovação em Automação Industrial (GIAI), CEP 70910-900, Brasília, DF, Brasil.
[27] Figueiredo, L. M. J. (2009): Modelo multicritério de apoio à substituição de equipamentos médicos hospitalares. IST, Lisboa, Portugal.
[28] Zhao, H. (2009): A chaotic time series prediction based on neural network: Evidence from the shanghai composite index in China. In Test and Measurement, 2009. ICTM 09. International Conference on, Vol. 2, pp. 382–385.
[29] Luna, I., Ballini, R., and Soares, S. (2006): Técnica de identificacão de modelos lineares e não-lineares de séries temporais. Revista Controle e Automacão, Vol. 17, N. 3, pp. 245–256.
[30] Múller, D. (2007): Processos Estocásticos e Aplicacões. Volume Coleccão Económicas - 2.ª Série of Direito Financeiro E Tributário. Almedina.
[31] Marco, A. R.; Angelo, A. D.; Leizer, S.; Silvio, A. B. V. (2010): A utilização de redes Bayesianas no processo decisório de de intervenções em equipamentos. Programa de Engenharia Industrial, Universidade Federal da Bahia, Escola Politécnica, Federação, 40.210-630, Salvador, Brasil.
[32] Araujo, M. S.; Bezerra, C. A. (2004): Desenvolvimento de componentes para sistemas estocásticos de apoio à decisão. PUCPR, Congresso Brasileiro de Computação, Engenharia Software, Brasil.
[33] Huang, Jia-Yen, Yao, Ming-Jong (2008): On the coordination of maintenance scheduling for transportation fleets of many branches of a logistic service provider. Ling Tung University1 Ling Tung Road, Nantun, Taichung 408, Taiwan, ROC. doi: 10.1016/j.camwa.2008.01.037.
[34] Vujanovic, D.; Momcˇilovic, V.; Bojovic, N.; Papic, V. (2012): Evaluation of vehicle fleet maintenance management indicators by application of DEMATEL and ANP. University of Belgrade, Faculty of Transport and Traffic Engineering, Vojvode Stepe 305, Belgrade, Serbia. http://dx.doi.org/10.1016/j.eswa.2012.02.159.
[35] Gurney, K. (1997): An introduction to neural networks, London: UCL Press, ISBN 1857285034.
[36] Tsoukalas, L. H.; Uhrig, R. E. (1996): Fuzzy and neural approaches in engineering, New York: John Wiley ISBN 0471160032.
[37] Ed. by Ronald; Yager R. (1992): Introduction to fuzzy logic applications - An in intelligent systems Boston. Kluwer Academic Publ. Cop.
[38] Campello, R. J. G. B.; Amaral, W. C. (2001): Modeling And Linguistic Knowledge Extration From Systems Using Fuzzy Relation Models, Fuzzy Sets and Systems, N. 121, pp. 113-126.
[39] Couellan, N.; Jana, S.; Jorquera T.; George J. P. (2015): Self-adaptive Support Vector Machine: A multi-agent optimization perspective. Université de Toulouse, UPS IMT, F-31062 Toulouse Cedex 9, France. http://dx.doi.org/10.1016/j.eswa.2015.01.028.
[40] Chena, D.; Wanga, L.; Li, L. (2015): Position computation models for high-speed train based on supportvector machine approach. Control and Safety, Beijing Jiaotong University, Beijing 100044, China, http://dx.doi.org/10.1016/j.asoc.2015.01.017.
[41] Pooyan N;, Shahbazian M., Salahshoor K.; Hadian M. (2015): Simultaneous Fault Diagnosis using multi class support vector machine in a Dew Point process. Department of Instrumentation and Automation, Petroleum University of Technology, Ahwaz, Iran, http://dx.doi.org/10.1016/j.jngse.2015.01.043.
[42] Cury, M. V. Q.; Veiga, F. J. P. (2003): Método Para Avaliação do Desempenho de Rodovias Concessionadas Sob a Ótica do Usuário. Instituto Militar de Engenharia, Rio de Janeiro, Brazil.
[43] Hugo Raposo, José Torres Farinha, Luís Ferreira, Diego Galar (2017): Dimensioning Reserve Bus Fleet using Life Cycle Cost Models and Condition Based / Predictive Maintenance - a Case Study. Public Transport. Vol. 10 N. 1, pp. 1–22. Springer Berlin Heidelberg. Print ISSN 1866-749X. Online ISSN 1613-7159. DOI https://doi.org/10.1007/s12469-017-0167-x.
[44] Hugo Raposo, José Torres Farinha, Luís Ferreira, Diego Galar (2017): An integrated econometric model for bus replacement and spare reserve based on a condition predictive maintenance model. Maintenance and Reliability. Eksploatacja i Niezawodnosc - Maintenance and Reliability 2017; Vol. 19, N. 3, pp. 358-368. ISSN 1507-2711. http://dx.doi.org/10.17531/ein.2017.3.6.
[45] Galar Pascual, D.; Berges Muro, L.; Lambán Castillo, MP.; Huertas Talón, JL.; Tormos Martínez, BV. (2013):Cálculo de la vida útil remanente mediante trayectorias móviles entre hiperplanos de máquinas de soporte vectorial. Interciencia: journal of science and technology of the Americas. Vol. 38, N.8, pp.556-562. http://hdl.handle.net/10251/77486.
[46] CTRE (2018): “Importance of Maturity in Implementing Asset Management”. CTRE, Iowa State University center, administered by the Institute for Transportation. http://www.ctre.iastate.edu/piarc/, accessed on 2018.10.26.
[47] Diego Galar, Peter Sandborn, Uday Kumar (2017): Maintenance Costs and Life Cycle Cost Analysis. Publisher: CRC Press Taylor & Francis; 1 edition (September 18, 2017); Language: English. ISBN-10: 9781498769549, ISBN-13: 978-1498769549, ASIN: 1498769543.
[48] Jorge Luiz Riechi, Bernardo Tormos, Marcos Vinicius Jacometo Hillebrand (2017): Otimização dos custos de frota urbana com uso de modelo combinado de life cycle cost e simulação de Monte Carlo. Revista Produção Online, Florianópolis, Vol. 17, N. 2, pp. 667-91. ISSN 1676-1901.
[49] Vicente Macián, B. Tormos, Jorge Riechi (2017): Time replacement optimization model: comparative analysis of urban transport fleets using Monte Carlo Simulation. March 2017, Eksploatacja i Niezawodnosc - Maintenance and Reliability; Vol. 19, N. 2 pp. 151-157. DOI: 10.17531/ein.2017.2.1.
[50] Jorge Riechi, Vicente Macián, B. Tormos, C. Avila (2017): Optimal fleet replacement: A case study on a Spanish urban transport fleet. June 2017, Journal of the Operational Research Society; Vol. 68, N. 6, pp. 1-9. DOI: 10.1057/s41274-017-0236-1.
Author Information
  • Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes, Coimbra University, Coimbra, Portugal;Department of Mechanical Engineering, Coimbra Institute of Engineering, Coimbra, Portugal

  • Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes, Coimbra University, Coimbra, Portugal;Department of Mechanical Engineering, Coimbra Institute of Engineering, Coimbra, Portugal

  • Department of Mechanical Engineering, Centre for Mechanical Engineering, Materials and Processes, Coimbra University, Coimbra, Portugal;Department of Mechanical Engineering, Coimbra Institute of Engineering, Coimbra, Portugal

  • Department of Civil, Environmental and Natural Resources Engineering, Lulea University of Technology, Lulea, Sweden

Cite This Article
  • APA Style

    Hugo Raposo, José Torres Farinha, Inácio Fonseca, Diego Galar. (2019). Economic Life Cycle versus Lifespan – A Case Study of an Urban Bus Fleet. Engineering and Applied Sciences, 4(2), 30-43. https://doi.org/10.11648/j.eas.20190402.12

    Copy | Download

    ACS Style

    Hugo Raposo; José Torres Farinha; Inácio Fonseca; Diego Galar. Economic Life Cycle versus Lifespan – A Case Study of an Urban Bus Fleet. Eng. Appl. Sci. 2019, 4(2), 30-43. doi: 10.11648/j.eas.20190402.12

    Copy | Download

    AMA Style

    Hugo Raposo, José Torres Farinha, Inácio Fonseca, Diego Galar. Economic Life Cycle versus Lifespan – A Case Study of an Urban Bus Fleet. Eng Appl Sci. 2019;4(2):30-43. doi: 10.11648/j.eas.20190402.12

    Copy | Download

  • @article{10.11648/j.eas.20190402.12,
      author = {Hugo Raposo and José Torres Farinha and Inácio Fonseca and Diego Galar},
      title = {Economic Life Cycle versus Lifespan – A Case Study of an Urban Bus Fleet},
      journal = {Engineering and Applied Sciences},
      volume = {4},
      number = {2},
      pages = {30-43},
      doi = {10.11648/j.eas.20190402.12},
      url = {https://doi.org/10.11648/j.eas.20190402.12},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.eas.20190402.12},
      abstract = {The maintenance policy and the quality of service throughout the bus’s life cycle can be measured through their costs along time that, when evaluated using Lifespan or Economic Life Cycle methods, allow to determine the renewal or the replacement time. The paper discusses these two models, using real data from an urban bus fleet company. The maths that supports the models are presented. They are considered the functioning and maintenance costs, and also the apparent rate. The Life Cycle Cost of an urban transport bus is strongly dependent on the policy and quality of its maintenance, from which it depends on its reliability and availability. The final result is reflected on its Life Cycle Cost, that can be evaluated through the Lifespan or the Economic Life Cycle methods. Other aspects that can be considered are the fuel costs and the type of terrain, because they are intrinsically interrelated and have a strong effect on costs, namely because they imply strong variation in the bus’s consumption and in their maintenance costs. As the company considered in the case study has a poor maintenance policy, it makes the analysis challenging, making difficult to compare the economic life cycle with the lifespan method in this situation. However, the results and conclusions that are taken from them are obvious, what demonstrates the models’ utility and robustness.},
     year = {2019}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Economic Life Cycle versus Lifespan – A Case Study of an Urban Bus Fleet
    AU  - Hugo Raposo
    AU  - José Torres Farinha
    AU  - Inácio Fonseca
    AU  - Diego Galar
    Y1  - 2019/06/10
    PY  - 2019
    N1  - https://doi.org/10.11648/j.eas.20190402.12
    DO  - 10.11648/j.eas.20190402.12
    T2  - Engineering and Applied Sciences
    JF  - Engineering and Applied Sciences
    JO  - Engineering and Applied Sciences
    SP  - 30
    EP  - 43
    PB  - Science Publishing Group
    SN  - 2575-1468
    UR  - https://doi.org/10.11648/j.eas.20190402.12
    AB  - The maintenance policy and the quality of service throughout the bus’s life cycle can be measured through their costs along time that, when evaluated using Lifespan or Economic Life Cycle methods, allow to determine the renewal or the replacement time. The paper discusses these two models, using real data from an urban bus fleet company. The maths that supports the models are presented. They are considered the functioning and maintenance costs, and also the apparent rate. The Life Cycle Cost of an urban transport bus is strongly dependent on the policy and quality of its maintenance, from which it depends on its reliability and availability. The final result is reflected on its Life Cycle Cost, that can be evaluated through the Lifespan or the Economic Life Cycle methods. Other aspects that can be considered are the fuel costs and the type of terrain, because they are intrinsically interrelated and have a strong effect on costs, namely because they imply strong variation in the bus’s consumption and in their maintenance costs. As the company considered in the case study has a poor maintenance policy, it makes the analysis challenging, making difficult to compare the economic life cycle with the lifespan method in this situation. However, the results and conclusions that are taken from them are obvious, what demonstrates the models’ utility and robustness.
    VL  - 4
    IS  - 2
    ER  - 

    Copy | Download

  • Sections