An urban-transportation optimization problem

Abstract This paper applies a bi-level formalism for the optimal control of an urban transportation network. The well known store-and-forward model in traffic control is utilized in order to increase the control space of the optimization problem. Mainly, the store-and-forward models apply the split as a control argument, assuming the traffic light cycle as a constant parameter. The paper shows that by using a bi-level formalism the control problem can be defined within increased control space comprising both the split and the cycle. Both are found as optimal solutions of a bi-level optimization problem.

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Linear-Quadratic-Gaussian Optimization of Urban Transportation Network with Application to Sofia Traffic Optimization

Cybernetics and Information Technologies ◽

10.1515/cait-2016-0041 ◽

2016 ◽

Vol 16 (3) ◽

pp. 165-184 ◽

Cited By ~ 2

Author(s):

Aleksey Balabanov ◽

Todor Stoilov ◽

Yordanka Boneva

Keyword(s):

Optimization Problem ◽

Transportation Network ◽

Urban Transportation ◽

Control Policy ◽

Special Problem ◽

Problem Definition ◽

Real Time Control ◽

Linear Quadratic ◽

Linear Quadratic Gaussian ◽

Time Control

Abstract The paper defines and solves a Linear-Quadratic-Gaussian (LQG) optimization problem addressing real time control policy of Urban Transportation Network (UTN). The paper presents UTN model definition, analysis and LQG optimization problem definition, resulting in special problem structure. A real application for UTN situated in Sofia, Bulgaria along Yosif Gurko street was provided for testing this control policy.

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Comparative Analysis of the Use Egalitarian and Utilitarian Approaches in the Freight Transportation Optimization Problem

2021 International Conference on Industrial Engineering, Applications and Manufacturing (ICIEAM) ◽

10.1109/icieam51226.2021.9446374 ◽

2021 ◽

Author(s):

Viktor Bogachev ◽

Alexandra Kravets ◽

Taras Bogachev

Keyword(s):

Comparative Analysis ◽

Optimization Problem ◽

Freight Transportation ◽

Transportation Optimization

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Exergy and sensibility analysis of each individual effect in a kraft multiple effect evaporator

TAPPI Journal ◽

10.32964/tj18.10.607 ◽

2019 ◽

Vol 18 (10) ◽

pp. 607-618

Author(s):

JÉSSICA MOREIRA ◽

BRUNO LACERDA DE OLIVEIRA CAMPOS ◽

ESLY FERREIRA DA COSTA JUNIOR ◽

ANDRÉA OLIVEIRA SOUZA DA COSTA

Keyword(s):

Optimization Problem ◽

Real Data ◽

Kraft Pulping ◽

Steam Temperature ◽

Input Flow ◽

Transfer Coefficients ◽

Heat Transfer Coefficients ◽

Multiple Effect ◽

Sensibility Analysis ◽

Exergetic Analysis

The multiple effect evaporator (MEE) is an energy intensive step in the kraft pulping process. The exergetic analysis can be useful for locating irreversibilities in the process and pointing out which equipment is less efficient, and it could also be the object of optimization studies. In the present work, each evaporator of a real kraft system has been individually described using mass balance and thermodynamics principles (the first and the second laws). Real data from a kraft MEE were collected from a Brazilian plant and were used for the estimation of heat transfer coefficients in a nonlinear optimization problem, as well as for the validation of the model. An exergetic analysis was made for each effect individually, which resulted in effects 1A and 1B being the least efficient, and therefore having the greatest potential for improvement. A sensibility analysis was also performed, showing that steam temperature and liquor input flow rate are sensible parameters.

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Genetic Algorithm for Solving the Problem of an Optimum Regression Model Construction as a Discrete Optimization Problem

Journal of Automation and Information Sciences ◽

10.1615/jautomatinfscien.v40.i6.60 ◽

2008 ◽

Vol 40 (6) ◽

pp. 60-71 ◽

Cited By ~ 4

Author(s):

Ivan M. Melnik

Keyword(s):

Genetic Algorithm ◽

Regression Model ◽

Discrete Optimization ◽

Optimization Problem ◽

Model Construction ◽

Discrete Optimization Problem

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COMPARISON OF FLIGHT PATH OPTIMIZATION PROBLEM SOLUTIONS BY DIRECT AND INDIRECT METHODS FOR A GUIDED AIRCRAFT MISSILE WITH A ROCKET ENGINE

TsAGI Science Journal ◽

10.1615/tsagiscij.2018025535 ◽

2017 ◽

Vol 48 (6) ◽

pp. 551-563

Author(s):

S. A. Lyovin

Keyword(s):

Optimization Problem ◽

Rocket Engine ◽

Flight Path ◽

Path Optimization ◽

Indirect Methods ◽

Flight Path Optimization ◽

Direct And Indirect Methods

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Imaging through Scattering Media with a Learning Based Prior

Electronic Imaging ◽

10.2352/issn.2470-1173.2020.14.coimg-306 ◽

2020 ◽

Vol 2020 (14) ◽

pp. 306-1-306-6

Author(s):

Florian Schiffers ◽

Lionel Fiske ◽

Pablo Ruiz ◽

Aggelos K. Katsaggelos ◽

Oliver Cossairt

Keyword(s):

Optimization Problem ◽

Autonomous Driving ◽

Scattering Media ◽

Photon Scattering ◽

Point Spread ◽

Spread Function ◽

Radiative Transport Equation ◽

Spatio Temporal ◽

Transient Imaging

Imaging through scattering media finds applications in diverse fields from biomedicine to autonomous driving. However, interpreting the resulting images is difficult due to blur caused by the scattering of photons within the medium. Transient information, captured with fast temporal sensors, can be used to significantly improve the quality of images acquired in scattering conditions. Photon scattering, within a highly scattering media, is well modeled by the diffusion approximation of the Radiative Transport Equation (RTE). Its solution is easily derived which can be interpreted as a Spatio-Temporal Point Spread Function (STPSF). In this paper, we first discuss the properties of the ST-PSF and subsequently use this knowledge to simulate transient imaging through highly scattering media. We then propose a framework to invert the forward model, which assumes Poisson noise, to recover a noise-free, unblurred image by solving an optimization problem.

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