An efficient simulation-based optimization algorithm for large-scale transportation problems
Carolina Osorio,Linsen Chong +1 more
- 09 Dec 2012
- pp 423
TL;DR: This paper applies a computationally efficient simulation-based optimization (SO) algorithm suitable for large-scale transportation problems based on a metamodel approach based on information from a high-resolution yet inefficient microscopic urban traffic simulator to a scalable and tractable analytical macroscopic traffic model.
read more
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.
read more
Chat with Paper
AI Agents for this Paper
Find similar papers on Google Scholar, PubMed and Arxiv
Write a critical review of this paper
Analyze citations of this paper to find unaddressed research gaps
Citations
A simulation-based optimization framework for urban transportation problems
Carolina Osorio,Michel Bierlaire +1 more
TL;DR: A simulation-based optimization method that enables the efficient use of complex stochastic urban traffic simulators to address various transportation problems is proposed and a metamodel that integrates information from a simulator with an analytical queueing network model is presented.
A Hierarchical Framework for Intelligent Traffic Management in Smart Cities
TL;DR: The proposed framework which is based on the multi-agent system manages to mitigate potential traffic congestions and minimize drivers’ average travel time in metropolitan areas and can be achieved by the utilization of a closed-loop management system.
53
Managing Emergency Traffic Evacuation With a Partially Random Destination Allocation Strategy: A Computational-Experiment-Based Optimization Approach
TL;DR: A partially random destination allocation strategy for evacuation management is proposed using a metamodel-based simulation optimization method to design the strategy, leading to reduced network clearance times.
37
Modeling traffic flow using simulation and big data analytics
Casey N. Bowman,John A. Miller +1 more
- 11 Dec 2016
TL;DR: This work focuses mainly on generating models for vehicle arrivals, turning behavior, and traffic flow and using them to drive microscopic traffic simulations built upon real world data.
14
A Structured Approach for Rapidly Mapping Multilevel System Measures via Simulation Metamodeling
TL;DR: The method described here leverages simulation metamodeling to map the three basic SE metrics, namely, measures of performance to measures of effectiveness to a single figure of merit, which enables using meetamodels to map multilevel system measures supports rapid decision making.
11
References
An Interior Trust Region Approach for Nonlinear Minimization Subject to Bounds
Thomas F. Coleman,Yuying Li +1 more
TL;DR: In this paper, a trust region approach for minimizing nonlinear functions subject to simple bounds is proposed, where the trust region is defined by minimizing a quadratic function subject only to an ellipsoidal constraint and the iterates generated by these methods are always strictly feasible.
•Book
Introduction to derivative-free optimization
Andrew R. Conn,Katya Scheinberg,Luís Nunes Vicente +2 more
- 16 Apr 2009
TL;DR: This book explains how sampling and model techniques are used in derivative-free methods and how these methods are designed to efficiently and rigorously solve optimization problems, in the first contemporary comprehensive treatment of optimization without derivatives.
1.8K
Optimization by Direct Search: New Perspectives on Some Classical and Modern Methods ∗
TL;DR: This review begins by briefly summarizing the history of direct search methods and considering the special properties of problems for which they are well suited, then turns to a broad class of methods for which the underlying principles allow general-ization to handle bound constraints and linear constraints.
1.8K
•Book
Queueing Theory
C. D'Apice,A. V. Pechinkin,P. P. Bocharov +2 more
- 01 Jan 2003
Abstract: Single queueing nodes are usually described using Kendall’s notation in the form A/S/C where A describes the time between arrivals to the queue, S the size of jobs and C the number of servers at the node.[5][6] Many theorems in queueing theory can be proved by reducing queues to mathematical systems known as Markov chains, first described by Andrey Markov in his 1906 paper.[7] Agner Krarup Erlang, a Danish engineer who worked for the Copenhagen Telephone Exchange, published the first paper on what would now be called queueing theory in 1909.[8][9][10] He modeled the number of telephone calls arriving at an exchange by a Poisson process and solved the M/D/1 queue in 1917 and M/D/k queueing model in 1920.[11] In Kendall’s notation:
477
Chapter 18 Metamodel-Based Simulation Optimization
TL;DR: Two approaches of Iterative optimization methods for simulation optimization, based on a metamodel or model of the simulation model, and an example, are discussed in this chapter and illustrated with an example.
308
Related Papers (5)
Carolina Osorio,Michel Bierlaire +1 more
- 01 Jan 2011
Tobias Kiesling,Johannes Lüthi +1 more
- 01 Jun 2005