Optimizing Park Locations with Imperfect Information

This paper asks how public planners should make park-location decisions when the model used to measure need is itself uncertain. Rather than assuming a single accessibility index is correct, the project studies how different formulations can produce different recommendations and how optimization can be used more carefully in that setting.

Contribution: my main role was formulating and implementing the optimization model in gurobipy, writing the methods section, and editing the full paper text.

Method: applied mixed-integer and bi-level optimization ideas to compare park-location recommendations across alternative index formulations in Greenville County, South Carolina, with attention to how disagreement between measures of need changes the decision itself.

Result and impact: the paper shows why public-sector optimization models should account for modeling disagreement directly instead of presenting a single index-driven answer as definitive.

Recommended citation: Griffin, G. L., Fletcher, A., Goto, D. N., Sabogal De La Pava, M., Jahan Beikloo, M., White, D., and Tucker, E. L. (2026). "Optimizing Park Locations with Imperfect Information." IISE Annual Conference & Expo.
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