Bridging the gap between multi-objective optimization and spatial planning: a new post-processing methodology capturing the optimum allocation of land uses against established transportation infrastructure

The optimal allocation of multiple land uses constitutes a complex multi-objective optimization problem with unknown feasible objective space and optimal planning alternatives. Despite the effectiveness of evolutionary algorithms to capture the underlying Pareto set of optimum maps, land use planners are bound to pursue the best possible spatial allocation of each use within an enormous population of non-dominated solutions. This article presents a novel post-processing methodology enhancing the comparative evaluation of alternative planning approaches without making any assumptions about the (relative) importance of each objective function. The proposed consolidated post-processing module is applied in a land use planning paradigm, revealing: (a) the existence of substantial planning guidelines whose validity is not affected by the relative significance of each criterion and (b) the variable planning component emerging from the (varying) relative importance of objective functions. Such planning feedback could not be extracted by the exhaustive review of non-dominated maps.