scholarly journals A Road Pricing Model for Congested Highways Based on Link Densities

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Louis de Grange ◽  
Rodrigo Troncoso ◽  
Felipe González
Keyword(s):  
Objective Function ◽  
Optimization Problem ◽  
Road Pricing ◽  
Model Parameters ◽  
Pricing Model ◽  
Constrained Optimization Problem ◽  
Highway Design ◽  
Nonlinear Constrained Optimization ◽  
Capital Costs ◽  
Pricing Scheme

A road pricing model is presented that determines tolls for congested highways. The main contribution of this paper is to include density explicitly in the pricing scheme and not just flow and time. The methodology solves a nonlinear constrained optimization problem whose objective function maximizes toll revenue or highway use (2 scenarios). The results show that the optimal tolls depend on highway design and the level of congestion. The model parameters are estimated from a Chile’s highway data. Significant differences were found between the highway’s observed tolls and the optimal toll levels for the two scenarios. The proposed approach could be applied to either planned highway concessions with recovery of capital costs or the extension or retendering of existing concessions.

scholarly journals Automatic Rectification of the Hybrid Stereo Vision System

Sensors ◽  
2018 ◽  
Vol 18 (10) ◽  
pp. 3355
Author(s):  
Chengtao Cai ◽  
Bing Fan ◽  
Xin Liang ◽  
Qidan Zhu
Keyword(s):  
Stereo Vision ◽  
Optimization Problem ◽  
Vision System ◽  
Field Of View ◽  
Computation Complexity ◽  
Constrained Optimization Problem ◽  
Projection Model ◽  
Nonlinear Constrained Optimization ◽  
Stereo Vision System ◽  
Dynamic Hybrid

By combining the advantages of 360-degree field of view cameras and the high resolution of conventional cameras, the hybrid stereo vision system could be widely used in surveillance. As the relative position of the two cameras is not constant over time, its automatic rectification is highly desirable when adopting a hybrid stereo vision system for practical use. In this work, we provide a method for rectifying the dynamic hybrid stereo vision system automatically. A perspective projection model is proposed to reduce the computation complexity of the hybrid stereoscopic 3D reconstruction. The rectification transformation is calculated by solving a nonlinear constrained optimization problem for a given set of corresponding point pairs. The experimental results demonstrate the accuracy and effectiveness of the proposed method.

Cost Minimum Proportioning of Non-Slump Concrete Mix Using Genetic Algorithms

2012 ◽  
Vol 468-471 ◽  
pp. 50-54 ◽  
Author(s):  
Md. Moshiur Rahman ◽  
Mohd Zamin Jumaat
Keyword(s):  
Compressive Strength ◽  
Optimization Problem ◽  
Constrained Optimization Problem ◽  
Nonlinear Constrained Optimization ◽  
Constituent Material ◽  
Optimization Formulation ◽  
Materials Used ◽  
The Individual ◽  
The Cost ◽  
Optimal Quantity

This paper presents a generalized formulation for determining the optimal quantity of the materials used to produce Non-Slump Concrete with minimum possible cost. The proposed problem is formulated as a nonlinear constrained optimization problem. The proposed problem considers cost of the individual constituent material costs as well as the compressive strength and other requirement. The optimization formulation is employed to minimize the cost function of the system while constraining it to meet the compressive strength and workability requirement. The results demonstrate the efficiency of the proposed approach to reduce the cost as well as to satisfy the above requirement.

scholarly journals Learning Compact Model for Large-Scale Multi-Label Data

2019 ◽  
Vol 33 ◽  
pp. 5385-5392 ◽  
Author(s):  
Tong Wei ◽  
Yu-Feng Li
Keyword(s):  
Large Scale ◽  
Optimization Problem ◽  
Performance Metrics ◽  
State Of The Art ◽  
Optimization Method ◽  
Model Parameters ◽  
Generalization Capability ◽  
Constrained Optimization Problem ◽  
Unseen Data ◽  
Model Size

Large-scale multi-label learning (LMLL) aims to annotate relevant labels from a large number of candidates for unseen data. Due to the high dimensionality in both feature and label spaces in LMLL, the storage overheads of LMLL models are often costly. This paper proposes a POP (joint label and feature Parameter OPtimization) method. It tries to filter out redundant model parameters to facilitate compact models. Our key insights are as follows. First, we investigate labels that have little impact on the commonly used LMLL performance metrics and only preserve a small number of dominant parameters for these labels. Second, for the remaining influential labels, we reduce spurious feature parameters that have little contribution to the generalization capability of models, and preserve parameters for only discriminative features. The overall problem is formulated as a constrained optimization problem pursuing minimal model size. In order to solve the resultant difficult optimization, we show that a relaxation of the optimization can be efficiently solved using binary search and greedy strategies. Experiments verify that the proposed method clearly reduces the model size compared to state-of-the-art LMLL approaches, in addition, achieves highly competitive performance.

scholarly journals Multi-Objective Optimization of Gas Pipeline Networks

Energies ◽  
2020 ◽  
Vol 13 (19) ◽  
pp. 5141
Author(s):  
Andrzej J. Osiadacz ◽  
Niccolo Isoli
Keyword(s):  
Optimization Problem ◽  
Optimization Method ◽  
Optimization Approach ◽  
Multi Objective Optimization ◽  
Constrained Optimization Problem ◽  
Nonlinear Constrained Optimization ◽  
Scalar Optimization ◽  
Gas Networks ◽  
Pipeline Networks ◽  
Gas Supply

The main goal of this paper is to prove that bi-objective optimization of high-pressure gas networks ensures grater system efficiency than scalar optimization. The proposed algorithm searches for a trade-off between minimization of the running costs of compressors and maximization of gas networks capacity (security of gas supply to customers). The bi-criteria algorithm was developed using a gradient projection method to solve the nonlinear constrained optimization problem, and a hierarchical vector optimization method. To prove the correctness of the algorithm, three existing networks have been solved. A comparison between the scalar optimization and bi-criteria optimization results confirmed the advantages of the bi-criteria optimization approach.

scholarly journals Locomotor Development Prediction Based on Statistical Model Parameters Identification

2012 ◽  
Vol 2012 ◽  
pp. 1-5
Author(s):  
A. V. Wildemann ◽  
A. A. Tashkinov ◽  
V. A. Bronnikov
Keyword(s):  
Optimization Problem ◽  
Real Data ◽  
Parameters Identification ◽  
Model Parameters ◽  
Large Set ◽  
Constrained Optimization Problem ◽  
Unknown Parameters ◽  
Locomotor Development ◽  
Feasible Solutions ◽  
The Individual

This paper introduces an approach for parameters identification of a statistical predicting model with the use of the available individual data. Unknown parameters are separated into two groups: the ones specifying the average trend over large set of individuals and the ones describing the details of a concrete person. In order to calculate the vector of unknown parameters, a multidimensional constrained optimization problem is solved minimizing the discrepancy between real data and the model prediction over the set of feasible solutions. Both the individual retrospective data and factors influencing the individual dynamics are taken into account. The application of the method for predicting the movement of a patient with congenital motility disorders is considered.

Teeth-Number Synthesis of an Automatic Planetary Transmission Using Evolutionary Computation

2004 ◽  
Author(s):  
P. A. Simionescu ◽  
D. G. Beale ◽  
G. V. Dozier
Keyword(s):  
Constrained Optimization ◽  
Objective Function ◽  
Evolutionary Computation ◽  
Optimization Problem ◽  
Design Parameters ◽  
Constrained Optimization Problem ◽  
Gear Teeth ◽  
Estimation Of Distribution ◽  
Number Synthesis ◽  
Planetary Transmission

The gear-teeth number synthesis of an automatic planetary transmission used in automobiles is formulated as a constrained optimization problem that is solved with the aid of an Estimation of Distribution Algorithm. The design parameters are the teeth number of each gear, the number of multiple planets and gear module, while the objective function is defined based on the departure between the imposed and the actual gear ratios, constrained by teeth-undercut avoidance, limiting the maximum overall diameter of the transmission and ensuring proper planet spacing.

A Computer-Aided Optimization Approach for the Design of Injection Mold Cooling Systems

1998 ◽  
Vol 120 (2) ◽  
pp. 165-174 ◽  
Author(s):  
L. Q. Tang ◽  
K. Pochiraju ◽  
C. Chassapis ◽  
S. Manoochehri
Keyword(s):  
Finite Element ◽  
Optimal Design ◽  
Constrained Optimization ◽  
Objective Function ◽  
Optimization Problem ◽  
Temperature Gradients ◽  
Optimization Approach ◽  
Injection Mold ◽  
Constrained Optimization Problem ◽  
Cooling Systems

A methodology is presented for the design of optimal cooling systems for injection mold tooling which models the mold cooling as a nonlinear constrained optimization problem. The design constraints and objective function are evaluated using Finite Element Analysis (FEA). The objective function for the constrained optimization problem is stated as minimization of both a function related to part average temperature and temperature gradients throughout the polymeric part. The goal of this minimization problem is to achieve reduction of undesired defects as sink marks, differential shrinkage, thermal residual stress built-up, and part warpage primarily due to non-uniform temperature distribution in the part. The cooling channel size, locations, and coolant flow rate are chosen as the design variables. The constrained optimal design problem is solved using Powell’s conjugate direction method using penalty function. The cooling cycle time and temperature gradients are evaluated using transient heat conduction simulation. A matrix-free algorithm of the Galerkin Finite Element Method (FEM) with the Jacobi Conjugate Gradient (JCG) scheme is utilized to perform the cooling simulation. The optimal design methodology is illustrated using a case study.

An Artificial Evolutionary Approach for Solving the Nonlinear Constrained Optimization Problems

2013 ◽  
Vol 479-480 ◽  
pp. 861-864
Author(s):  
Yi Chih Hsieh ◽  
Peng Sheng You
Keyword(s):  
Constrained Optimization ◽  
Optimization Problem ◽  
Optimization Problems ◽  
Evolutionary Approach ◽  
Benchmark Problems ◽  
Second Phase ◽  
Constrained Optimization Problem ◽  
Constrained Optimization Problems ◽  
Two Phase ◽  
Nonlinear Constrained Optimization

In this paper, an artificial evolutionary based two-phase approach is proposed for solving the nonlinear constrained optimization problems. In the first phase, an immune based algorithm is applied to solve the nonlinear constrained optimization problem approximately. In the second phase, we present a procedure to improve the solutions obtained by the first phase. Numerical results of two benchmark problems are reported and compared. As shown, the solutions by the new proposed approach are all superior to those best solutions by typical approaches in the literature.

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