A pareto artificial fish swarm algorithm for solving a multi-objective electric transit network design problem

Abstract

This study presents a multi-objective optimization model for the urban electric transit network design problem by simultaneously determining the set of transit routes, the service frequency and the location of charging depots with the objectives of minimizing the costs for passengers and operators. The constraints about the bus route, charging depot, vehicle operation and charging schedule are considered to ensure the rational design and operational feasibility of the electric transit network. The solution approach is based on a Pareto artificial fish swarm algorithm (PAFSA) in combination with the crossover and mutation operators. The transit network in an urban region of Beijing is studied in a case study, revealing that the proposed optimization model solved by the PAFSA is able to provide the Pareto optimal solutions to design a relatively large-scaled transit network for the best fits between the passengers and operators.

KEYWORDS:

• Electric transit network design
• service frequency
• charging depot location
• artificial fish swarm algorithm
• multi-objective optimization

Disclosure statement

No potential conflict of interest is reported by the authors.

ORCID

Yi Liu http://orcid.org/0000-0001-7041-694X

Xuesong Feng http://orcid.org/0000-0002-5992-8650

Additional information

Funding

This research is supported by the National Natural Science Foundation of China [grant number 71571011].