A method is presented for the concept design of railway passenger vehicles. The method requires adequate mathematical modelling for describing quantitatively the many relationships between vehicle parameters and vehicle performance indexes. The main purpose of the method is defining the layout of vehicles (i.e. length, number of wheelsets, etc.) in order to obtain the lowest possible life cycle cost (LCC). The method is based on multiobjective programming (MOP), a branch of operations research. By the proposed method, the optimal design of railway passenger vehicles can be performed in a theoretically correct and rigorous way. Genetic algorithms (GAs) are used to find the numerical solution to the problem. The optimal design of urban, suburban and intercity (IC) passenger vehicles is performed in order to obtain the best compromise between conflicting requirements such as maximum payload, minimum tare weight and axle load, minimum track deterioration, maximum ride comfort, etc. It is shown that, with respect to modern vehicles, a major improvement in LCC would be gained if shorter vehicles and newly designed wheelsets and/or bogies could be adopted. Should this happen, new families of passenger vehicles with two or three axles would be the optimal solutions to be built.


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    Title :

    On the optimal design of railway passenger vehicles


    Contributors:


    Publication date :

    2001-03-01


    Size :

    14 pages




    Type of media :

    Article (Journal)


    Type of material :

    Electronic Resource


    Language :

    English




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    Mastinu, G.R.M. | Online Contents | 2001


    On the optimal design of railway passenger vehicles

    Mastinu, G. R. M. / Gobbi, M. | British Library Online Contents | 2001


    On the optimal design of railway passenger vehicles

    Mastinu, G.R.M. / Gobbi, M. | Tema Archive | 2001


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    Evans, J. R. | Taylor & Francis Verlag | 1992


    The modelling of railway passenger vehicles

    Evans,J.R. / British Rail Research,GB | Automotive engineering | 1992