An efficient simulation-based optimization algorithm for large-scale transportation problems

This paper applies a computationally efficient simulation-based optimization (SO) algorithm suitable for large-scale transportation problems. The algorithm is based on a metamodel approach. The metamodel combines information from a high-resolution yet inefficient microscopic urban traffic simulator...

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Published in:Proceedings of the 2012 Winter Simulation Conference (WSC) pp. 1 - 11
Main Authors: Osorio, C., Linsen Chong
Format: Conference Proceeding
Language:English
Published: IEEE 01.12.2012
Subjects:
Analytical models
Computational modeling
Equations
Mathematical model
Optimization
Queueing analysis
Transportation
ISBN:1467347795, 9781467347792
ISSN:0891-7736
Online Access:Get full text
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Abstract This paper applies a computationally efficient simulation-based optimization (SO) algorithm suitable for large-scale transportation problems. The algorithm is based on a metamodel approach. The metamodel combines information from a high-resolution yet inefficient microscopic urban traffic simulator with information from a scalable and tractable analytical macroscopic traffic model. We then embed the model within a derivative-free trust region algorithm. We evaluate its performance considering tight computational budgets. We illustrate the efficiency of this algorithm by addressing an urban traffic signal control problem for the full city of Lausanne, Switzerland. The problem consists of a nonlinear objective function with nonlinear constraints. The problem addressed is considered large-scale and complex both in the fields of derivative-free optimization and simulation-based optimization. We compare the performance of the method to a traditional metamodel method.
AbstractList This paper applies a computationally efficient simulation-based optimization (SO) algorithm suitable for large-scale transportation problems. The algorithm is based on a metamodel approach. The metamodel combines information from a high-resolution yet inefficient microscopic urban traffic simulator with information from a scalable and tractable analytical macroscopic traffic model. We then embed the model within a derivative-free trust region algorithm. We evaluate its performance considering tight computational budgets. We illustrate the efficiency of this algorithm by addressing an urban traffic signal control problem for the full city of Lausanne, Switzerland. The problem consists of a nonlinear objective function with nonlinear constraints. The problem addressed is considered large-scale and complex both in the fields of derivative-free optimization and simulation-based optimization. We compare the performance of the method to a traditional metamodel method.
Author Linsen Chong
Osorio, C.
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  surname: Linsen Chong
  fullname: Linsen Chong
  email: linsenc@mit.edu
  organization: Massachusetts Inst. of Technol., Cambridge, MA, USA
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Snippet This paper applies a computationally efficient simulation-based optimization (SO) algorithm suitable for large-scale transportation problems. The algorithm is...
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SubjectTerms Analytical models
Computational modeling
Equations
Mathematical model
Optimization
Queueing analysis
Transportation
Title An efficient simulation-based optimization algorithm for large-scale transportation problems
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