The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework.

Bastiaan Possel (Corresponding Author), Luc J.J. Wismans, Eric C. van Berkum, Michiel C.J. Bliemer

Research output: Contribution to journalArticleAcademicpeer-review

3 Citations (Scopus)
109 Downloads (Pure)

Abstract

Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types which are associated with certain link characteristics are the discrete decision variables. Two distinct solution approaches for this multi-objective optimization problem are compared. The first heuristic is the non-dominated sorting genetic algorithm II (NSGA-II) and the second heuristic is the dominance based multi objective simulated annealing (DBMO-SA). Both heuristics have been applied on a small hypothetical test network as well as a realistic case of the city of Almelo in the Netherlands. The results show that both heuristics are capable of solving the MO NDP. However, the NSGA-II outperforms DBMO-SA, because it is more efficient in finding more non-dominated optimal solutions within the same computation time and maximum number of assessed solutions.
Original languageEnglish
Pages (from-to)545-572
Number of pages28
JournalTransportation
Volume45
Issue number2
DOIs
Publication statusPublished - 1 Mar 2018

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network design
simulated annealing
Simulated annealing
heuristics
Sorting
genetic algorithm
Genetic algorithms
Multiobjective optimization
sorting
externality
comparison
Netherlands

Keywords

  • UT-Hybrid-D
  • Multi-objective network design problem
  • Externalities
  • Genetic algorithm
  • Simulated annealing
  • Accessibility
  • Traffic safety
  • Emission

Cite this

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title = "The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework.",
abstract = "Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types which are associated with certain link characteristics are the discrete decision variables. Two distinct solution approaches for this multi-objective optimization problem are compared. The first heuristic is the non-dominated sorting genetic algorithm II (NSGA-II) and the second heuristic is the dominance based multi objective simulated annealing (DBMO-SA). Both heuristics have been applied on a small hypothetical test network as well as a realistic case of the city of Almelo in the Netherlands. The results show that both heuristics are capable of solving the MO NDP. However, the NSGA-II outperforms DBMO-SA, because it is more efficient in finding more non-dominated optimal solutions within the same computation time and maximum number of assessed solutions.",
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The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework. / Possel, Bastiaan (Corresponding Author); Wismans, Luc J.J.; van Berkum, Eric C.; Bliemer, Michiel C.J.

In: Transportation, Vol. 45, No. 2, 01.03.2018, p. 545-572.

Research output: Contribution to journalArticleAcademicpeer-review

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AU - Wismans, Luc J.J.

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N2 - Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types which are associated with certain link characteristics are the discrete decision variables. Two distinct solution approaches for this multi-objective optimization problem are compared. The first heuristic is the non-dominated sorting genetic algorithm II (NSGA-II) and the second heuristic is the dominance based multi objective simulated annealing (DBMO-SA). Both heuristics have been applied on a small hypothetical test network as well as a realistic case of the city of Almelo in the Netherlands. The results show that both heuristics are capable of solving the MO NDP. However, the NSGA-II outperforms DBMO-SA, because it is more efficient in finding more non-dominated optimal solutions within the same computation time and maximum number of assessed solutions.

AB - Incorporation of externalities in the Multi-Objective Network Design Problem (MO NDP) as objectives is an important step in designing sustainable networks. In this research the problem is defined as a bi-level optimization problem in which minimizing externalities are the objectives and link types which are associated with certain link characteristics are the discrete decision variables. Two distinct solution approaches for this multi-objective optimization problem are compared. The first heuristic is the non-dominated sorting genetic algorithm II (NSGA-II) and the second heuristic is the dominance based multi objective simulated annealing (DBMO-SA). Both heuristics have been applied on a small hypothetical test network as well as a realistic case of the city of Almelo in the Netherlands. The results show that both heuristics are capable of solving the MO NDP. However, the NSGA-II outperforms DBMO-SA, because it is more efficient in finding more non-dominated optimal solutions within the same computation time and maximum number of assessed solutions.

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