Analytic Network Process Prioritization with Goal Programming for Transportation Resource Allocation I

Transportation Infrastructure (TI) project selection is identification of alternative projects in order to maximize the net benefit to the organization and allocate limited resources among those alternatives. It is difficult to select the optimum set of proposed TI projects in an organization due to factors such as project risk, corporate goals and limited availability of TI resources for the candidate projects. TI project selection is based on Multi-Criteria Decision Making (MCDM) problems, as opposed to other project selection techniques, which have restricted applications as they generally depend on the assumption of independence among the candidate projects and criteria. In this paper, an improved TI project selection methodology is proposed that reflects interdependencies among evaluation criteria and projects, using an analytic network process (ANP) within a zero–one goal programming (ZOGP) model. To provide a systematic approach to set priorities among the multi-criteria and trade-off among the objectives, it is recommended that ANP is applied prior to GP formulation. Although GP incorporates multiple objectives and determines an optimal solution, its major drawback is that the decision maker(s) must specify goals and priorities a priori. To overcome this, group discussion is needed, and this research has proposed an integrated approach for interdependent TI project selection problems using Fuzzy Delphi, the Analytic Network Process concept and Zero-One Goal Programming. In project selection it is important to consider interdependencies among projects or criteria, to reflect the characteristics of real problems. Developing a methodology for considering interdependent factors is complex, so this paper presents a solution method through a real-world TI empirical example on an ongoing decision-making project in Taichung City, Taiwan using ANP/ZOGP. In this way, problems with multiple criteria, interdependence and resource feasibility are solved.