The focus of this paper is to develop a solution framework to study equilibrium transportation network design problems with multiple objectives that are mutually commensurate. Objective parameterization, or scalarization, forms the core idea of this solution approach, by which a multi-objective problem can be equivalently addressed by tackling a series of single-objective problems. In particular, we develop a parameterization-based heuristic that resembles an iterative divide-and-conquer strategy to locate a Pareto-optimal solution in each divided range of commensurate parameters. Unlike its previous counterparts, the heuristic is capable of asymptotically exhausting the complete Pareto-optimal solution set and identifying parameter ranges that exclude any Pareto-optimal solution. Its algorithmic effectiveness and solution characteristics are justified by a set of numerical examples, from which we also gain additional insights about its solution generation behavior and the tradeoff between the computation cost and solution quality.
All Science Journal Classification (ASJC) codes
- Computer Networks and Communications
- Artificial Intelligence