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Optimal fleet replacement: A case study on a Spanish urban transport fleet

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Optimal fleet replacement: A case study on a Spanish urban transport fleet

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De Sa-Riechi, JL.; Macian Martinez, V.; Tormos, B.; Avila, C. (2017). Optimal fleet replacement: A case study on a Spanish urban transport fleet. Journal of the Operational Research Society. 68(8):886-894. doi:10.1057/s41274-017-0236-1

Por favor, use este identificador para citar o enlazar este ítem: http://hdl.handle.net/10251/104132

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Título: Optimal fleet replacement: A case study on a Spanish urban transport fleet
Autor: De Sa-Riechi, Jorge Luiz Macian Martinez, Vicente Tormos, B. Avila, Claudio
Entidad UPV: Universitat Politècnica de València. Departamento de Máquinas y Motores Térmicos - Departament de Màquines i Motors Tèrmics
Fecha difusión:
Fecha de fin de embargo: 2018-08-01
Resumen:
[EN] Optimizing the average annual cost of a bus fleet has become an increasing concern in transport companies management around the world. Nowadays, there are many tools available to assist managerial decisions, and one of ...[+]
Palabras clave: Fleet replacement , Optimization , Operation and maintenance costs , Transport , Life cycle cost analysis , Monte Carlo simulation
Derechos de uso: Reserva de todos los derechos
Fuente:
Journal of the Operational Research Society. (issn: 0160-5682 )
DOI: 10.1057/s41274-017-0236-1
Editorial:
Nature Publishing Group - Macmillan Publishers
Versión del editor: http://doi.org/10.1057/s41274-017-0236-1
Tipo: Artículo

References

Avila da S., C. R., & Beck, A. T. (2015). New method for efficient Monte Carlo–Neumann solution of linear stochastic systems. Probabilistic Engineering Mechanics, 40, 90-96. doi:10.1016/j.probengmech.2015.02.006

Boudart, J., & Figliozzi, M. (2012). Key Variables Affecting Decisions of Bus Replacement Age and Total Costs. Transportation Research Record: Journal of the Transportation Research Board, 2274(1), 109-113. doi:10.3141/2274-12

Collan, M., & Liu, S. (2003). Fuzzy logic and intelligent agents: towards the next step of capital budgeting decision support. Industrial Management & Data Systems, 103(6), 410-422. doi:10.1108/02635570310479981 [+]
Avila da S., C. R., & Beck, A. T. (2015). New method for efficient Monte Carlo–Neumann solution of linear stochastic systems. Probabilistic Engineering Mechanics, 40, 90-96. doi:10.1016/j.probengmech.2015.02.006

Boudart, J., & Figliozzi, M. (2012). Key Variables Affecting Decisions of Bus Replacement Age and Total Costs. Transportation Research Record: Journal of the Transportation Research Board, 2274(1), 109-113. doi:10.3141/2274-12

Collan, M., & Liu, S. (2003). Fuzzy logic and intelligent agents: towards the next step of capital budgeting decision support. Industrial Management & Data Systems, 103(6), 410-422. doi:10.1108/02635570310479981

Erol, I., & Ferrell, W. G. (2003). A methodology for selection problems with multiple, conflicting objectives and both qualitative and quantitative criteria. International Journal of Production Economics, 86(3), 187-199. doi:10.1016/s0925-5273(03)00049-5

Feng, W., & Figliozzi, M. (2013). An economic and technological analysis of the key factors affecting the competitiveness of electric commercial vehicles: A case study from the USA market. Transportation Research Part C: Emerging Technologies, 26, 135-145. doi:10.1016/j.trc.2012.06.007

Khasnabis, S., Alsaidi, E., & Ellis, R. D. (2002). Optimal Allocation of Resources To Meet Transit Fleet Requirements. Journal of Transportation Engineering, 128(6), 509-518. doi:10.1061/(asce)0733-947x(2002)128:6(509)

Keles, P., & Hartman, J. C. (2004). CASE STUDY: BUS FLEET REPLACEMENT. The Engineering Economist, 49(3), 253-278. doi:10.1080/00137910490498951

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