Visually-driven parallel solving of multi-objective land-use allocation problems: a case study in Chelan, Washington

Abstract

Many geospatial optimization models can be formulated as multi-objective linear integer programming (LIP) models. Because geospatial optimization models are much more complicated than regular LIP models, solving large-scale geospatial LIP models may be facilitated through parallel computing. In this paper, we explore the possibility of applying geovisual analytics to promote the search of exact optimal solutions in parallel computing environments. By integrating the potential of visual analytics and high-performance computing, we developed a suite of interactive geovisual tools to dynamically steer the optimization search in an interactive manner. Using a sustainable land use design as a case study, we demonstrate the potential of our approach in solving multi-objective land use allocation problems.