Genetic Algorithms and the Corridor Location Problem: Multiple Objectives and Alternative Solutions

Abstract

Corridor planning problems are challenging because their solution often requires the participation of multiple stakeholders with different interests and emphases. Though such problems fall into the domain of multiobjective evaluation, existing corridor location models often search for a single global optimum by collapsing multiple objectives into a single one using a weighting method. In multiobjective problems with competing objectives, however, optimality will often have different interpretations among decision makers, and, as a consequence, no single optimal solution will satisfy all participants. This paper describes the design and implementation of a multiobjective genetic algorithm for corridor selection problems (MOGADOR). This new approach generates a large set of Pareto-optimal and near-optimal solutions that can be evaluated with respect to the untargeted or imprecisely modeled characteristics of ill-structured corridor location problems. Experimental results suggest that the MOGADOR approach outperforms traditional shortest-path methods in both computation time and solution quality. An analytical and visualization tool is provided to help decision makers identify good candidates and evaluate trade-offs among alternatives.