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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

2 Citations (Scopus)

Abstract

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.
Original languageEnglish
Title of host publication2014 IEEE Congress on Evolutionary Computation (CEC 2014)
EditorsC.C. Coello
PublisherIEEE
Pages405-412
ISBN (Print)978-1-4799-1483-8
DOIs
Publication statusPublished - 6 Jul 2014
Event2014 IEEE Congress on Evolutionary Computation, CEC 2014 - Beijing International Convention Center, Beijing, China
Duration: 6 Jul 201411 Jul 2014

Publication series

Name
PublisherIEEE press

Conference

Conference2014 IEEE Congress on Evolutionary Computation, CEC 2014
Abbreviated titleCEC
CountryChina
CityBeijing
Period6/07/1411/07/14

Fingerprint

Parking
Sorting
Genetic algorithms
Planning

Keywords

  • IR-101317
  • METIS-302376

Cite this

Brands, T., Wismans, L. J. J., & van Berkum, E. C. (2014). Multi-objective transportation network design: Accelerating search by applying ε-NSGAII. In C. C. Coello (Ed.), 2014 IEEE Congress on Evolutionary Computation (CEC 2014) (pp. 405-412). IEEE. https://doi.org/10.1109/CEC.2014.6900486
Brands, Ties ; Wismans, Luc Johannes Josephus ; van Berkum, Eric C. / Multi-objective transportation network design: Accelerating search by applying ε-NSGAII. 2014 IEEE Congress on Evolutionary Computation (CEC 2014). editor / C.C. Coello. IEEE, 2014. pp. 405-412
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abstract = "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.",
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Brands, T, Wismans, LJJ & van Berkum, EC 2014, Multi-objective transportation network design: Accelerating search by applying ε-NSGAII. in CC Coello (ed.), 2014 IEEE Congress on Evolutionary Computation (CEC 2014). IEEE, pp. 405-412, 2014 IEEE Congress on Evolutionary Computation, CEC 2014, Beijing, China, 6/07/14. https://doi.org/10.1109/CEC.2014.6900486

Multi-objective transportation network design: Accelerating search by applying ε-NSGAII. / Brands, Ties; Wismans, Luc Johannes Josephus; van Berkum, Eric C.

2014 IEEE Congress on Evolutionary Computation (CEC 2014). ed. / C.C. Coello. IEEE, 2014. p. 405-412.

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AB - 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.

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Brands T, Wismans LJJ, van Berkum EC. Multi-objective transportation network design: Accelerating search by applying ε-NSGAII. In Coello CC, editor, 2014 IEEE Congress on Evolutionary Computation (CEC 2014). IEEE. 2014. p. 405-412 https://doi.org/10.1109/CEC.2014.6900486