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

Author

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  • Bastiaan Possel

    (Goudappel Coffeng)

  • Luc J. J. Wismans

    (Goudappel Coffeng
    University of Twente)

  • Eric C. Berkum

    (University of Twente)

  • Michiel C. J. Bliemer

    (The University of Sydney Business School)

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.

Suggested Citation

  • Bastiaan Possel & Luc J. J. Wismans & Eric C. Berkum & Michiel C. J. Bliemer, 2018. "The multi-objective network design problem using minimizing externalities as objectives: comparison of a genetic algorithm and simulated annealing framework," Transportation, Springer, vol. 45(2), pages 545-572, March.
    • Handle: RePEc:kap:transp:v:45:y:2018:i:2:d:10.1007_s11116-016-9738-y
    DOI: 10.1007/s11116-016-9738-y
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    Cited by:

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    3. Di Pace, Roberta & Storani, Facundo & Guarnaccia, Claudio & de Luca, Stefano, 2023. "Signal setting design to reduce noise emissions in a connected environment," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 632(P2).
    4. Siying Zhu & Feng Zhu, 2020. "Multi-objective bike-way network design problem with space–time accessibility constraint," Transportation, Springer, vol. 47(5), pages 2479-2503, October.
    5. Zhaoqi Zang & Xiangdong Xu & Anthony Chen & Chao Yang, 2022. "Modeling the α-max capacity of transportation networks: a single-level mathematical programming formulation," Transportation, Springer, vol. 49(4), pages 1211-1243, August.
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    7. Hong Ki An & Muhammad Awais Javeed & Gimok Bae & Nimra Zubair & Ahmed Sayed M. Metwally & Patrizia Bocchetta & Fan Na & Muhammad Sufyan Javed, 2022. "Optimized Intersection Signal Timing: An Intelligent Approach-Based Study for Sustainable Models," Sustainability, MDPI, vol. 14(18), pages 1-19, September.

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