Balancing the portfolio of urban and public projects with distance-dependent coverage facilities

The portfolio of urban and public projects should be balanced in terms of completion time, districts, and strategic objectives. For this purpose, we suggest a mixedinteger nonlinear programming model based on the goal programming approach. Projects are selected to minimize the squared deviation of urban and regional development indicators from their respective targets. In the proposed model there are two types of indicators: coverage indicators that are measured based on the distance of each neighborhood from the nearest covering facility, and general indicators that are usually measured based on the capacities and capabilities of each district. It is assumed that the location of covering facilities has already been selected, but the construction of these facilities will be prioritized and planned based on budget constraints and competition with other regional development projects. Numerical results show that the proposed genetic algorithm has superior performance compared with the Generalized Algebraic Modeling System (GAMS) solvers. Finally, the application of the model is illustrated by an example.