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An integer multi-objective optimization model and an enhanced non-dominated sorting genetic algorithm for contraflow scheduling problem

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Abstract

To determine the onset and duration of contraflow evacuation, a multi-objective optimization (MOO) model is proposed to explicitly consider both the total system evacuation time and the operation cost. A solution algorithm that enhances the popular evolutionary algorithm NSGA-II is proposed to solve the model. The algorithm incorporates preliminary results as prior information and includes a meta-model as an alternative to evaluation by simulation. Numerical analysis of a case study suggests that the proposed formulation and solution algorithm are valid, and the enhanced NSGA-II outperforms the original algorithm in both convergence to the true Pareto-optimal set and solution diversity.

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Authors and Affiliations

  1. School of Sustainable Engineering and the Built Environment, Arizona State University, Tempe AZ, 85287-3005, USA

    Pei-heng Li  (李沛恒) & Ying-yan Lou  (楼颖燕)

Authors
  1. Pei-heng Li  (李沛恒)
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  2. Ying-yan Lou  (楼颖燕)
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Corresponding author

Correspondence to Ying-yan Lou  (楼颖燕).

Additional information

Foundation item: Project(ADLT 930-809R) supported by the Alabama Department of Transportation, USA

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Li, Ph., Lou, Yy. An integer multi-objective optimization model and an enhanced non-dominated sorting genetic algorithm for contraflow scheduling problem. J. Cent. South Univ. 22, 2399–2405 (2015). https://doi.org/10.1007/s11771-015-2766-5

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  • DOI: https://doi.org/10.1007/s11771-015-2766-5

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