Transportation infrastructure decisions that optimize available resources and provide maximum benefits are of tremendous potential advantage to the transportation professional. Decision makers attempt to reach their goals with well-timed and cost-effective decisions that invest limited available resources according to future needs. Thus, it becomes increasingly important for them to use objective tools to make the proper investment choices. This paper presents a multiobjective analysis that uses a set of objectives and constraints to aid investment decisions. Multiobjective decision analysis gives planners technical tools to solve a decision problem with multiple objectives. The effects of each objective might then be evaluated according to selected measures of effectiveness (MOEs). The MOEs derived from these objectives might include a resulting reduction in environmental impacts, a level of improved access to cultural facilities, or a level of comfort with the given alternatives. With complex transportation planning problems, no single option may be considered the best by all measures. Economic, safety, ecological, social, and operational measures may produce varied results. Multiobjective programming does not provide a single solution or alternative, but rather provides a set of best alternatives from all available options. This paper presents a constraint multiobjective programming method for use in analyzing major investment decisions. A case study based on the Ohio River Bridge Project demonstrates the application of the multiobjective analysis tool.


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    Titel :

    Investment Analysis Using the Constraint Multiobjective Programming Method


    Untertitel :

    A Case Study


    Weitere Titelangaben:

    Transportation Research Record: Journal of the Transportation Research Board


    Beteiligte:
    Chowdhury, Mashrur (Autor:in) / Tan, Paulin (Autor:in)


    Erscheinungsdatum :

    01.01.2005




    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch