Multi-objective transportation network design: Accelerating search by applying ε-NSGAII

The optimization of infrastructure planning in a multimodal passenger transportation network is formulated as a multi-objective network design problem, with accessibility, use of urban space by parking, operating deficit and climate impact as objectives. Decision variables are the location of park and ride facilities, train stations and the frequency of public transport lines. For a real life case study the Pareto set is estimated by the Epsilon Non-dominated Sorting Genetic Algorithm (ε-NSGAII), since due to high computation time a high performance within a limited number of evaluated solutions is desired. As a benchmark, the NSGAII is used. In this paper Pareto sets from runs of both algorithms are analyzed and compared. The results show that after a reasonable computation time, ε-NSGAII outperforms NSGAII for the most important indicators, especially in the early stages of algorithm executions.