Multiobjective bilevel optimization for transportation planning and management problems

Many previous studies have formulated the decision-making problems in transportation system planning and management as single-objective bilevel optimization models. However, real-world decision-making processes always have several social concerns and thus multiple objectives need to be achieved simultaneously. In most cases, these objective functions conflict with each other and are also not simple enough to be combined into a single one. Therefore it is necessary to apply multiobjective optimization to generate non-dominated or Pareto optimal alternatives. It can be foreseen that the multiobjective bilevel modeling approach can become a powerful, and possibly interactive, decision tool, allowing the decision-makers to learn more about the problem before committing to a final decision. Such multiobjective bilevel models are difficult to solve due to their intrinsic nonconvexity and multiple objectives. This paper consequently proposes a solution algorithm for the multiobjective bilevel models using genetic algorithms. The proposed algorithm is illustrated, using the numerical example taken from the previous study. It is found that the proposed algorithm is efficient to search simultaneously the Pareto optimal solutions.