A Multiobjective Optimization Model for Urban Renewal Projects Selection with Uncertainty Considerations

Abstract

While facing with limited resources, the managers or decision makers of a city for urban renewal must determine which projects to fund at what levels from a pool of potential ones. This problem of project selection is inherently multiobjective since various factors, such as the available budget, the chance of success, and the efficient allocation of the project team, must be considered simultaneously. The uncertainty of the data at the time decisions are made further complicates urban renewal project selection. In this research, a multiobjective, integer-constrained optimization model with competing objectives for urban renewal project selection is formulated using probability distributions to describe costs. The subjective rank is determined via the Analytic Network Process which is able to reflect the interdependencies among criteria and candidate projects. Our proposed model is unique since it integrates multiobjective optimization, Monte Carlo simulation, and the Analytic Network Process. The application of the proposed methodology is illustrated through an example.