scholarly journals A Two-Stage Model for Project Optimization in Transportation Infrastructure Management System

2014 ◽  
Vol 2014 ◽  
pp. 1-8 ◽  
Author(s):  
Zhang Chen ◽  
Liyuan Liu ◽  
Li Li ◽  
Hui Li
Keyword(s):  
Management System ◽  
Mathematical Optimization ◽  
Poor Performance ◽  
Optimization Method ◽  
Transportation Infrastructure ◽  
Optimization Models ◽  
Infrastructure Management ◽  
Two Stage ◽  
Maintenance Schedule ◽  
Project Optimization

Mathematical optimization is very important for project decision in the Transportation Infrastructure Management System (TIMS). However, it has not been widely employed in TIMS due to poor performance of conventional optimization models in calculation speed and practical application. Therefore, it is necessary to improve the performance of optimization models. According to the process of decision-making in transportation management, a novel two-stage project optimization model, including budget allocation and project distribution, was proposed in this paper. Moreover, the methods of dynamic programming (DP) and genetic algorithm (GA) were applied to obtain an effective solution. The findings indicate that the new optimization method can provide a satisfactory and reasonable maintenance schedule for transportation infrastructure maintenance agencies whose routine management will benefit from the newly proposed model.

Dimensioning a recovery boiler furnace using mathematical optimization

TAPPI Journal ◽  
2015 ◽  
Vol 14 (2) ◽  
pp. 119-129 ◽  
Author(s):  
VILJAMI MAAKALA ◽  
PASI MIIKKULAINEN
Keyword(s):  
Radial Basis Function Network ◽  
High Capacity ◽  
Mathematical Optimization ◽  
Optimization Procedure ◽  
Optimization Method ◽  
Performance Criteria ◽  
Boiler Furnace ◽  
Starting Point ◽  
Recovery Boiler ◽  
Base Design

Capacities of the largest new recovery boilers are steadily rising, and there is every reason to expect this trend to continue. However, the furnace designs for these large boilers have not been optimized and, in general, are based on semiheuristic rules and experience with smaller boilers. We present a multiobjective optimization code suitable for diverse optimization tasks and use it to dimension a high-capacity recovery boiler furnace. The objective was to find the furnace dimensions (width, depth, and height) that optimize eight performance criteria while satisfying additional inequality constraints. The optimization procedure was carried out in a fully automatic manner by means of the code, which is based on a genetic algorithm optimization method and a radial basis function network surrogate model. The code was coupled with a recovery boiler furnace computational fluid dynamics model that was used to obtain performance information on the individual furnace designs considered. The optimization code found numerous furnace geometries that deliver better performance than the base design, which was taken as a starting point. We propose one of these as a better design for the high-capacity recovery boiler. In particular, the proposed design reduces the number of liquor particles landing on the walls by 37%, the average carbon monoxide (CO) content at nose level by 81%, and the regions of high CO content at nose level by 78% from the values obtained with the base design. We show that optimizing the furnace design can significantly improve recovery boiler performance.

scholarly journals A Two-Stage Mono- and Multi-Objective Method for the Optimization of General UPS Parallel Manipulators

Mathematics ◽  
2021 ◽  
Vol 9 (5) ◽  
pp. 543
Author(s):  
Alejandra Ríos ◽  
Eusebio E. Hernández ◽  
S. Ivvan Valdez
Keyword(s):  
Genetic Algorithm ◽  
Performance Metrics ◽  
Tracking Error ◽  
Parallel Manipulators ◽  
Optimization Method ◽  
Statistical Hypothesis ◽  
Two Stage ◽  
Multi Objective ◽  
Second Stage ◽  
Conflicting Objectives

This paper introduces a two-stage method based on bio-inspired algorithms for the design optimization of a class of general Stewart platforms. The first stage performs a mono-objective optimization in order to reach, with sufficient dexterity, a regular target workspace while minimizing the elements’ lengths. For this optimization problem, we compare three bio-inspired algorithms: the Genetic Algorithm (GA), the Particle Swarm Optimization (PSO), and the Boltzman Univariate Marginal Distribution Algorithm (BUMDA). The second stage looks for the most suitable gains of a Proportional Integral Derivative (PID) control via the minimization of two conflicting objectives: one based on energy consumption and the tracking error of a target trajectory. To this effect, we compare two multi-objective algorithms: the Multiobjective Evolutionary Algorithm based on Decomposition (MOEA/D) and Non-dominated Sorting Genetic Algorithm-III (NSGA-III). The main contributions lie in the optimization model, the proposal of a two-stage optimization method, and the findings of the performance of different bio-inspired algorithms for each stage. Furthermore, we show optimized designs delivered by the proposed method and provide directions for the best-performing algorithms through performance metrics and statistical hypothesis tests.

Evaluation of sampling alternatives to quantify tree leaf area

2003 ◽  
Vol 33 (1) ◽  
pp. 82-95 ◽  
Author(s):  
H Temesgen
Keyword(s):  
Leaf Area ◽  
Poor Performance ◽  
Tree Crown ◽  
Two Stage ◽  
Zero Bias ◽  
Probability Sampling ◽  
First Order ◽  
Unequal Probability Sampling ◽  
Unequal Probability ◽  
Tree Leaf

High within- and among-tree crown variation have contributed to the difficulty of tree-crown sampling and single-tree leaf area (area available for photosynthesis) estimation. Using reconstructed trees, simulations were used to compare five sampling designs for bias, mean square error (MSE), and distribution of the estimates. All sampling designs showed nearly zero bias. For most sample trees, stratified random sampling resulted in the lowest MSE values, followed by ellipsoidal, two-stage systematic, simple random, and then by two-stage unequal probability sampling. The poor performance of two-stage unequal probability sampling can be ascribed to the unequal probability of inclusion of first-order branches and twigs.

scholarly journals Optimization of Powerful Two-stage Screw Centrifugal Pump

2018 ◽  
Vol 220 ◽  
pp. 03009 ◽  
Author(s):  
Oleg Baturin ◽  
Grigorii Popov ◽  
Daria Kolmakova ◽  
Vasilii Zubanov ◽  
Julia Novikova ◽  
...  
Keyword(s):  
High Pressure ◽  
Centrifugal Pump ◽  
Mathematical Optimization ◽  
Pressure Head ◽  
Low Pressure ◽  
Calculation Model ◽  
Pump Efficiency ◽  
Rotor Speed ◽  
Two Stage ◽  
Optimization Parameters

The article presents a refining method for a two-stage screw centrifugal pump by the joint usage of mathematical optimization software IOSO, meshing complex NUMECA and CFD software ANSYS CFX. The pump main parameters: high-pressure stage rotor speed was 13300 rpm; low-pressure rotor speed was 3617 rpm by gearbox; inlet total pressure was 0.4 MPa; outlet mass flow was 132.6 kg/s at the nominal mode. This article describes the process of simplifying the calculation model for the optimization. The parameters of camber lines of the low-pressure impeller, transition duct, and high-pressure impeller blades for two sections (hub and shroud) were chosen as optimization parameters. The blades of low-pressure impeller, transition duct and high-pressure impeller have changed during optimization. The optimization goal was the increase of the pump efficiency with preservation or slight increase in the pressure head. The efficiency was increased by 3%.

A Two-Stage Optimization Method for the Stencil Printing Process Based on Neural Network and Response Surface Method

2010 ◽  
Vol 156-157 ◽  
pp. 10-17 ◽  
Author(s):  
Er Shun Pan ◽  
Yao Jin ◽  
Zhao Mei ◽  
Ying Wang
Keyword(s):  
Neural Network ◽  
Response Surface ◽  
Response Surface Method ◽  
Model Fitting ◽  
Optimization Method ◽  
Small Sample ◽  
Two Stage ◽  
Stencil Printing ◽  
Printing Process ◽  
Surface Method

A stencil printing process (SPP) optimization problem is studied in this paper. Due to the limitation that neural network requires a large number of samples for the accurate model fitting, a two-stage SPP optimization method is proposed. The design interval can be reduced with small sample by using neural network. In this reduced design interval , response surface method is adopted to obtain the accurate mathematical SPP model. The concept of confidence level is introduced to make the proposed model robust. An interactive method is used to solve the model. The proposed method is compared with the one-stage optimization method and the results show that the proposed method achieves a better performance on each objective.

scholarly journals A novel two-stage optimization method for beam–plate structure design

2016 ◽  
Vol 8 (11) ◽  
pp. 168781401667956 ◽  
Author(s):  
Kai Li ◽  
Yunlong Wang ◽  
Yan Lin ◽  
Wei Xu ◽  
Manting Liu
Keyword(s):  
Optimization Method ◽  
Structure Design ◽  
Two Stage ◽  
Plate Structure
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