Calibrating Mechanistic–Empirical Pavement Design Guide for North Carolina: Genetic Algorithm and Generalized Reduced Gradient Optimization Methods

2012 ◽  
Vol 2305 (1) ◽  
pp. 131-140 ◽  
Author(s):  
Fadi M. Jadoun ◽  
Y. Richard Kim
Keyword(s):  
Genetic Algorithm ◽  
North Carolina ◽  
Optimization Methods ◽  
Pavement Design ◽  
Gradient Optimization ◽  
Design Guide ◽  
Reduced Gradient ◽  
Generalized Reduced Gradient

scholarly journals Computer Aided Optimal Design of Compressed Air Energy Storage Systems

1980 ◽  
Vol 102 (3) ◽  
pp. 437-445 ◽  
Author(s):  
F. W. Ahrens ◽  
A. Sharma ◽  
K. M. Ragsdell
Keyword(s):  
Energy Storage ◽  
Numerical Models ◽  
Operating Cost ◽  
Optimization Methods ◽  
Compressed Air ◽  
Compressed Air Energy Storage ◽  
Reduced Gradient ◽  
The Media ◽  
Generalized Reduced Gradient ◽  
Programming Algorithms

An automated procedure for the design of Compressed Air Energy Storage (CAES) systems is presented. The procedure relies upon modern nonlinear programming algorithms, decomposition theory, and numerical models of the various system components. Two modern optimization methods are employed; BIAS, a Method of Multipliers code and OPT, a Generalized Reduced Gradient code. The procedure is demonstrated by the design of a CAES facility employing the Media, Illinois Galesville aquifer as the reservoir. The methods employed produced significant reduction in capital and operating cost, and in number of aquifer wells required.

scholarly journals A comparative study on optimization methods for the constrained nonlinear programming problems

2005 ◽  
Vol 2005 (2) ◽  
pp. 165-173 ◽  
Author(s):  
Ozgur Yeniay
Keyword(s):  
Nonlinear Programming ◽  
Comparative Study ◽  
Sequential Quadratic Programming ◽  
Gradient Methods ◽  
Optimization Methods ◽  
Test Problems ◽  
Reduced Gradient ◽  
Constrained Nonlinear Programming ◽  
Generalized Reduced Gradient ◽  
Reduced Gradient Methods

Constrained nonlinear programming problems often arise in many engineering applications. The most well-known optimization methods for solving these problems are sequential quadratic programming methods and generalized reduced gradient methods. This study compares the performance of these methods with the genetic algorithms which gained popularity in recent years due to advantages in speed and robustness. We present a comparative study that is performed on fifteen test problems selected from the literature.

Clustering Analysis to Characterize Mechanistic–Empirical Pavement Design Guide Traffic Data in North Carolina

2010 ◽  
Vol 2160 (1) ◽  
pp. 118-127 ◽  
Author(s):  
Fatemeh Sayyady ◽  
John R. Stone ◽  
Kent L. Taylor ◽  
Fadi M. Jadoun ◽  
Y. Richard Kim
Keyword(s):  
North Carolina ◽  
Clustering Analysis ◽  
Pavement Design ◽  
Traffic Data ◽  
Design Guide

scholarly journals A single period inventory model of a deteriorating item sold from two shops with shortage via genetic algorithm

2007 ◽  
Vol 17 (1) ◽  
pp. 75-94 ◽  
Author(s):  
S.K. Mondal ◽  
J.K. Dey ◽  
M. Maiti
Keyword(s):  
Genetic Algorithm ◽  
Profit Maximization ◽  
Maximization Problem ◽  
Roulette Wheel Selection ◽  
Reduced Gradient Method ◽  
Roulette Wheel ◽  
Time Period ◽  
Reduced Gradient ◽  
Optimum Order ◽  
Generalized Reduced Gradient

Inventory of differential units of a deteriorating item purchased in a lot and sold separately from two shops under a single management is considered. Here deterioration increases with time and demands are time- and price-dependent for fresh and deteriorated units respectively. For the fresh units, shortages are allowed and later partially-backlogged. For the deteriorated units, there are two scenarios depending upon whether initial rate of replenishment of deteriorated units is less or more than the demand of these items. Under each scenario, five sub-scenarios are depicted depending upon the time periods of the two-shops. For each sub scenarios, profit maximization problem has been formulated and solved for optimum order quantity and corresponding time period using genetic Algorithm (GA) with Roulette wheel selection, arithmetic crossover and uniform mutation and Generalized Reduced Gradient method (GRG). All sub-scenarios are illustrated numerically and results from two methods are compared. .

Neural Network Based On the Improved Genetic Algorithm to Optimize Component Separation Conditions of Small Peptide

2011 ◽  
Vol 284-286 ◽  
pp. 261-264
Author(s):  
Jing Wen Tian ◽  
Feng Jun Wu ◽  
Hui Chen ◽  
Jing Di Ren
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Liquid Chromatography ◽  
Forecast Model ◽  
Optimization Methods ◽  
Reversed Phase ◽  
Improved Genetic Algorithm ◽  
Small Peptide ◽  
Neural Network Theory ◽  
Separation Conditions

Reference to traditional optimization methods, neural network based on improved genetic algorithm is used in optimization of reversed phase chromatography pluralistic isocratic mobile phase separation conditions. With detailing the combination of the improved genetic algorithm and neural network theory, the optimization process for the liquid chromatography conditions is introduced in details. Used this method to small peptide RP chromatography optimization, after searching operation, the establishment of an effective separation of forecast model receives satisfactory predictive value, which can prove that this method can be used in optimization of drug liquid chromatography conditions.

scholarly journals Multiobjective Optimization Methods for Congestion Management in Deregulated Power Systems

2012 ◽  
Vol 2012 ◽  
pp. 1-8 ◽  
Author(s):  
K. Vijayakumar
Keyword(s):  
Genetic Algorithm ◽  
Power Systems ◽  
Electricity Market ◽  
Optimization Methods ◽  
Evolutionary Programming ◽  
Congestion Management ◽  
Transmission Congestion ◽  
Nsga Ii ◽  
Line Overload ◽  
Nondominated Sorting

Congestion management is one of the important functions performed by system operator in deregulated electricity market to ensure secure operation of transmission system. This paper proposes two effective methods for transmission congestion alleviation in deregulated power system. Congestion or overload in transmission networks is alleviated by rescheduling of generators and/or load shedding. The two objectives conflicting in nature (1) transmission line over load and (2) congestion cost are optimized in this paper. The multiobjective fuzzy evolutionary programming (FEP) and nondominated sorting genetic algorithm II methods are used to solve this problem. FEP uses the combined advantages of fuzzy and evolutionary programming (EP) techniques and gives better unique solution satisfying both objectives, whereas nondominated sorting genetic algorithm (NSGA) II gives a set of Pareto-optimal solutions. The methods propose an efficient and reliable algorithm for line overload alleviation due to critical line outages in a deregulated power markets. The quality and usefulness of the algorithm is tested on IEEE 30 bus system.

Implementing the Mechanistic–Empirical Design Guide Procedure for a Hot-Mix Asphalt–Rehabilitated Pavement in Indiana

2005 ◽  
Vol 1919 (1) ◽  
pp. 121-133 ◽  
Author(s):  
Khaled A. Galal ◽  
Ghassan R. Chehab
Keyword(s):  
Hot Mix Asphalt ◽  
Pavement Design ◽  
Design Parameters ◽  
Design Procedures ◽  
Design Concepts ◽  
Asphalt Overlay ◽  
Design Guide ◽  
Strategic Goals ◽  
And Performance

One of the Indiana Department of Transportation's (INDOT's) strategic goals is to improve its pavement design procedures. This goal can be accomplished by fully implementing the 2002 mechanistic–empirical (M-E) pavement design guide (M-E PDG) once it is approved by AASHTO. The release of the M-E PDG software has provided a unique opportunity for INDOT engineers to evaluate, calibrate, and validate the new M-E design process. A continuously reinforced concrete pavement on I-65 was rubblized and overlaid with a 13–in.-thick hot-mix asphalt overlay in 1994. The availability of the structural design, material properties, and climatic and traffic conditions, in addition to the availability of performance data, provided a unique opportunity for comparing the predicted performance of this section using the M-E procedure with the in situ performance; calibration efforts were conducted subsequently. The 1993 design of this pavement section was compared with the 2002 M-E design, and performance was predicted with the same design inputs. In addition, design levels and inputs were varied to achieve the following: ( a) assess the functionality of the M-E PDG software and the feasibility of applying M-E design concepts for structural pavement design of Indiana roadways, ( b) determine the sensitivity of the design parameters and the input levels most critical to the M-E PDG predicted distresses and their impact on the implementation strategy that would be recommended to INDOT, and ( c) evaluate the rubblization technique that was implemented on the I-65 pavement section.

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