Mixed integer linear programming approaches for land use planning that limit urban sprawl

A mixed integer programming model is proposed for land use optimization.The mixed integer programming model maximizes suitability as well as manage sprawl.Benders' decomposition is employed to solve large-scale quadratic MIP.Results are presented for a case study for Leander, Texas.Relationships between factors that affect sprawl and land suitability are analyzed. Sprawl has a detrimental effect on quality of life and the environment. With dwindling resources and increasing populations, we must manage sprawl. Ewing et al. (2000) defined factors to measure sprawl in the present urban structure. The measures are divided into four broad categories, which are density factors, mixed use factors, street factors, and center factors, and can be used in future planning of metro areas. In this research, we develop a mixed integer programming model to optimize land usage subject to sprawl constraints, which are based upon the aforementioned sprawl measures. Due to the large size of the problem, we employ a combination of heuristics and Benders' decomposition similar to one described by Bazaraa and Sherali (1982) to provide an urban planner with suitable land use assignments. We show examples demonstrating how the planner can use this approach to analyze how various factors that affect land use and sprawl measures. Finally, we discuss topics of future research.

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