A dimension-reduced method of sensitivity analysis for stochastic user equilibrium assignment model

This paper gives a new dimension-reduced method of sensitivity analysis for perturbed stochastic user equilibrium assignment (SUEA) model based on the relation between its Lagrange function and logarithmic barrier function combined with a Courant quadratic penalty term. The advantage of this method...

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Bibliographic Details
Published in:Applied mathematical modelling Vol. 34; no. 2; pp. 325 - 333
Main Authors: Liu, Jianmei, Ma, Shoufeng, Huang, Chongchao, Ma, Shuaiqi
Format: Journal Article
Language:English
Published: Kidlington Elsevier Inc 01.02.2010
Elsevier
Subjects:
Applied sciences
Dimension-reduced sensitivity analysis
Exact sciences and technology
Flows in networks. Combinatorial problems
Ground, air and sea transportation, marine construction
Mathematical programming
Non-smoothness
Nonlinear program
Operational research and scientific management
Operational research. Management science
Penalty function
Road transportation and traffic
SUEA
Penalty function
SUEA
Dimension-reduced sensitivity analysis
Nonlinear program
Non-smoothness
Variable conditions
Sensitivity analysis
Statistical analysis
Logarithmic function
Barrier function
Complex system
Non linear programming
Constrained optimization
Stochastic analysis
Statistical equilibrium
Lagrange multiplier
Nonsmooth analysis
Traffic assignment
Traffic management
Feasibility
Reduced order systems
Road traffic
Objective function
ISSN:0307-904X
Online Access:Get full text
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Summary:This paper gives a new dimension-reduced method of sensitivity analysis for perturbed stochastic user equilibrium assignment (SUEA) model based on the relation between its Lagrange function and logarithmic barrier function combined with a Courant quadratic penalty term. The advantage of this method is of smaller dimension than general sensitivity analysis and reducing complexity. Firstly, it presents the dimension-reduced sensitivity results of the general nonlinear programming perturbation problem and the improved results when the objective or constraint functions are not twice continuously differentiable. Then it proves the corresponding conclusion of SUEA with smooth or non-smooth cost functions by the method of converting constraint conditions and decision variables. Finally, two corresponding examples (smooth and non-smooth) are given to illustrate the feasibility of this method.
Bibliography:ObjectType-Article-2
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ISSN:0307-904X
DOI:10.1016/j.apm.2009.04.012