scholarly journals Optimized design of concrete-filled steel columns

2022 ◽  
Vol 15 (1) ◽  
Author(s):  
Jéssica Salomão Lourenção ◽  
Paulo Augusto Tonini Arpini ◽  
Gabriel Erlacher ◽  
Élcio Cassimiro Alves
Keyword(s):  
Genetic Algorithm ◽  
Cost Efficiency ◽  
Optimization Problem ◽  
Optimized Design ◽  
Composite Columns ◽  
Steel Columns ◽  
Normal Forces ◽  
Steel Bars ◽  
Genetic Algorithm Method ◽  
Filled Composite

Abstract The objective of this paper is to present the formulation of the optimization problem and its application to the design of concrete-filled composite columns with and without reinforcement steel bars, according to recommendations from NBR 8800:2008, NBR 16239:2013 and EN 1994-1-1:2004. A comparative analysis between the aforementioned standards is performed for various geometries considering cost, efficiency and materials in order to verify which parameters influence the solution of the composite column that satisfies the proposed problems. The solution of the optimization problem is obtained by using the genetic algorithm method featured in MATLAB’s guide toolbox. For the examples analyzed, results show that concretes with compressive strength greater than 50MPa directly influence the solution of the problem regarding cost and resistance to normal forces.

scholarly journals The Use of a Simulation Model for High-Runner Strategy Implementation in Warehouse Logistics

2020 ◽  
Vol 12 (23) ◽  
pp. 9818
Author(s):  
Gabriel Fedorko ◽  
Vieroslav Molnár ◽  
Nikoleta Mikušová
Keyword(s):  
Genetic Algorithm ◽  
Simulation Model ◽  
Optimization Problem ◽  
Software Tool ◽  
Time Saving ◽  
General Validity ◽  
Resistance Function ◽  
Genetic Algorithm Method ◽  
Working Day ◽  
Enterprise Logistics

This paper examines the use of computer simulation methods to streamline the process of picking materials within warehouse logistics. The article describes the use of a genetic algorithm to optimize the storage of materials in shelving positions, in accordance with the method of High-Runner Strategy. The goal is to minimize the time needed for picking. The presented procedure enables the creation of a software tool in the form of an optimization model that can be used for the needs of the optimization of warehouse logistics processes within various types of production processes. There is a defined optimization problem in the form of a resistance function, which is of general validity. The optimization is represented using the example of 400 types of material items in 34 categories, stored in six rack rows. Using a simulation model, a comparison of a normal and an optimized state is realized, while a time saving of 48 min 36 s is achieved. The mentioned saving was achieved within one working day. However, the application of an approach based on the use of optimization using a genetic algorithm is not limited by the number of material items or the number of categories and shelves. The acquired knowledge demonstrates the application possibilities of the genetic algorithm method, even for the lowest levels of enterprise logistics, where the application of this approach is not yet a matter of course but, rather, a rarity.

Optimization of Contact Problem by Genetic Algorithm Method

2011 ◽  
Vol 105-107 ◽  
pp. 386-391 ◽  
Author(s):  
Jan Szweda ◽  
Zdenek Poruba
Keyword(s):  
Genetic Algorithm ◽  
Contact Problem ◽  
Shape Optimization ◽  
Optimization Problem ◽  
Optimization Technique ◽  
Contact Problems ◽  
Global Optimum ◽  
Genetic Algorithm Method ◽  
Characteristic Features ◽  
Contact Shape

In this paper is discussed the way of suitable numerical solution of contact shape optimization problem. The first part of the paper is focused on method of global optimization field among which the genetic algorithm is chosen for computer processing and for application on contact problem optimization. The brief description of this method is done with emphasis of its characteristic features. The experiment performed on plane structural problem validates the ability of genetic algorithm in search the area of the global optimum. On the base of the research described in this work, it is possible to recommend optimization technique of genetic algorithm to use for shape optimization of engineering contact problems in which it is possible for any shape to achieve successful convergence of contact task solution.

Multi-Objective Optimization Design of Screw Conveyor Using Genetic Algorithm

2013 ◽  
Vol 732-733 ◽  
pp. 402-406
Author(s):  
Duan Yi Wang
Keyword(s):  
Genetic Algorithm ◽  
Mathematical Model ◽  
Optimization Design ◽  
Optimization Problem ◽  
Machine Design ◽  
Screw Conveyor ◽  
Optimized Design ◽  
Multi Objective Optimization ◽  
Multi Objective ◽  
The Mathematical Model

The weight minimum and drive efficiency maxima1 of screw conveyor were considered as double optimizing objects in this paper. The mathematical model of the screw conveyor has been established based on the theory of the machine design, and the genetic algorithm was adopted to solving the multi-objective optimization problem. The results show that the mass of spiral shaft reduces 13.6 percent, and the drive efficiency increases 6.4 percent because of the optimal design based on genetic algorithm. The genetic algorithm application on the screw conveyor optimized design can provided the basis for designing the screw conveyor.

The Research on the Improved Genetic Algorithm and Optimized Based on the Random Functional Analysis

2012 ◽  
Vol 166-169 ◽  
pp. 819-823
Author(s):  
Zheng Bing Xia ◽  
Zhi Xiang Cao ◽  
Ke Feng Zhang
Keyword(s):  
Genetic Algorithm ◽  
Functional Analysis ◽  
Optimization Problem ◽  
Technical Problem ◽  
Optimized Design ◽  
Engineering Optimization ◽  
Building Structure ◽  
Improved Genetic Algorithm ◽  
Traditional Algorithm ◽  
Practical Engineering

The optimization problem of building structure has the technical difficulties of nonlinear, discrete, indifferentiable and nonconvexity character, the traditional algorithm based on MATLAB can not solve practical engineering optimization problem accurately. Based on general genetic algorithm is as the foundation in this paper, the theory basis of modified algorithm is applied based on the application of functional analysis and analyses the genetic algorithm for the iterative characteristics is analyzed, and the three stem rod calculation is verified based on application of functional analysis improved genetic algorithm, which solves the optimized design of common rod technical problem and achieves good calculation accuracy and efficiency.

scholarly journals Finite element analysis of concrete filled lean duplex stainless steel columns

2018 ◽  
Author(s):  
Dennis Lam ◽  
Jie Yang ◽  
Xianghe Dai
Keyword(s):  
Finite Element Analysis ◽  
Finite Element ◽  
Stainless Steel ◽  
Duplex Stainless Steel ◽  
Element Model ◽  
Element Analysis ◽  
Composite Columns ◽  
Steel Columns ◽  
Lean Duplex Stainless Steel ◽  
Filled Composite

In recent years, a new low nickel content stainless steel (EN 1.4162) commonly referred as ‘lean duplex stainless steel’ has been developed, which has over two times the tensile strength of the more familiar austenitic stainless steel but at approximately half the cost. This paper presents the finite element analysis of concrete filled lean duplex stainless steel columns subjected to concentric axial compression. To predict the performance of this form of concrete filled composite columns, a finite element model was developed and finite element analyses were conducted. The finite element model was validated through comparisons of the results obtained from the experimental study. A parametric study was conducted to examine the effect of various parameters such as section size, wall thickness, infill concrete strength, etc. on the overall behaviour and compressive resistance of this form of composite columns. Through both experimental and numerical studies, the merits of using lean duplex stainless steel hollow sections in concrete filled composite columns are highlighted. In addition, a new formula based on the Eurocode 4 is proposed to predict the cross-section capacity of the concrete filled lean duplex stainless steel composite columns subjected to axial compression.

scholarly journals An Improved MOEA/D Based on Reference Distance for Software Project Portfolio Optimization

Complexity ◽  
2018 ◽  
Vol 2018 ◽  
pp. 1-16 ◽  
Author(s):  
Jing Xiao ◽  
Jing-Jing Li ◽  
Xi-Xi Hong ◽  
Min-Mei Huang ◽  
Xiao-Min Hu ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Evolutionary Algorithms ◽  
Portfolio Optimization ◽  
Optimization Problem ◽  
Experimental Comparison ◽  
Software Project ◽  
Project Portfolio ◽  
Multiobjective Evolutionary Algorithms ◽  
Reference Distance ◽  
Nondominated Sorting

As it is becoming extremely competitive in software industry, large software companies have to select their project portfolio to gain maximum return with limited resources under many constraints. Project portfolio optimization using multiobjective evolutionary algorithms is promising because they can provide solutions on the Pareto-optimal front that are difficult to be obtained by manual approaches. In this paper, we propose an improved MOEA/D (multiobjective evolutionary algorithm based on decomposition) based on reference distance (MOEA/D_RD) to solve the software project portfolio optimization problems with optimizing 2, 3, and 4 objectives. MOEA/D_RD replaces solutions based on reference distance during evolution process. Experimental comparison and analysis are performed among MOEA/D_RD and several state-of-the-art multiobjective evolutionary algorithms, that is, MOEA/D, nondominated sorting genetic algorithm II (NSGA2), and nondominated sorting genetic algorithm III (NSGA3). The results show that MOEA/D_RD and NSGA2 can solve the software project portfolio optimization problem more effectively. For 4-objective optimization problem, MOEA/D_RD is the most efficient algorithm compared with MOEA/D, NSGA2, and NSGA3 in terms of coverage, distribution, and stability of solutions.

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