Genetic Algorithm for Biobjective Urban Transit Routing Problem

Genetic algorithm for biobjective urban transit routing problem. The Nottingham ePrints service makes this work by researchers of the University of Nottingham available open access under the following conditions. · To the extent reasonable and practicable the material made available in Nottingham ePrints has been checked for eligibility before being made available. · Copies of full items can be used for personal research or study, educational, or not-for-profit purposes without prior permission or charge provided that the authors, title and full bibliographic details are credited, a hyperlink and/or URL is given for the original metadata page and the content is not changed in any way. · Quotations or similar reproductions must be sufficiently acknowledged. A note on versions: The version presented here may differ from the published version or from the version of record. If you wish to cite this item you are advised to consult the publisher's version. Please see the repository url above for details on accessing the published version and note that access may require a subscription. his paper considers solving a biobjective urban transit routing problem with a genetic algorithm approach. he objectives are to minimize the passengers' and operators' costs where the quality of the route sets is evaluated by a set of parameters. he proposed algorithm employs an adding-node procedure which helps in converting an infeasible solution to a feasible solution. A simple yet efective route crossover operator is proposed by utilizing a set of feasibility criteria to reduce the possibility of producing an infeasible network. he computational results from Mandl's benchmark problems are compared with other published results in the literature and the computational experiments show that the proposed algorithm performs better than the previous best published results in most cases.