Multi-stage production cost optimization of semi-rigid steel frames using genetic algorithms

2009 ◽  
Vol 31 (11) ◽  
pp. 2766-2778 ◽  
Author(s):  
Nizar Bel Hadj Ali ◽  
Mohamed Sellami ◽  
Anne-Françoise Cutting-Decelle ◽  
Jean-Claude Mangin
Keyword(s):  
Genetic Algorithms ◽  
Production Cost ◽  
Cost Optimization ◽  
Steel Frames ◽  
Multi Stage

scholarly journals Multi-Stage Feature Selection by Using Genetic Algorithms for Fault Diagnosis in Gearboxes Based on Vibration Signal

Sensors ◽  
2015 ◽  
Vol 15 (9) ◽  
pp. 23903-23926 ◽  
Author(s):  
Mariela Cerrada ◽  
René Sánchez ◽  
Diego Cabrera ◽  
Grover Zurita ◽  
Chuan Li
Keyword(s):  
Genetic Algorithms ◽  
Feature Selection ◽  
Fault Diagnosis ◽  
Vibration Signal ◽  
Multi Stage

scholarly journals Cost optimization of sodium hypochlorite bleaching washing for denim by combining ensemble of surrogates with particle swarm optimization

2021 ◽  
Vol 16 ◽  
pp. 155892502110223
Author(s):  
Jie Xu ◽  
Feng Liu ◽  
Zhenglei He ◽  
Zongao Zhang ◽  
Sheng Li
Keyword(s):  
Particle Swarm Optimization ◽  
Sodium Hypochlorite ◽  
Production Cost ◽  
Orthogonal Experiment ◽  
Cost Optimization ◽  
Particle Swarm ◽  
Process Variables ◽  
Swarm Optimization ◽  
Washing Process ◽  
Ensemble Of Surrogates

Sodium hypochlorite bleaching washing process has been broadly carried out in denim garment industrial production. However, the quantitative relationships between process variables and bleaching performances have not been illustrated explicitly. Hence, it is impractical to determine values of the variables that can achieve the optimal production cost while satisfying the requirements of customers. This paper proposes an optimization methodology by combining ensemble of surrogates (ESs) with particle swarm optimization (PSO) to optimize production cost of chlorine bleaching for denim. The methodology starts from the data collections by conducting a Taguchi L25 (56) orthogonal experiment with the process variables and metrics for evaluating bleaching performances. Based on the data, the quantitative relationships are separately constructed by using RBFNN, SVR, RF and ensemble of them. Then, accuracies of the surrogates are evaluated and it proves that the ESs outperforms the others. Later, the production cost optimization model is proposed and PSO is utilized to solve it, while a case study is given to depict the optimization process and verify the effectiveness of the proposed hybrid ESs-PSO approach. Overall, the ESs-PSO approach shows great capability of optimizing production cost of sodium hypochlorite bleaching washing for denim.

Implementation of Eurocode Load Cases in Optimization Problems of Steel Frames, Based on Genetic Algorithms

2013 ◽  
Vol 310 ◽  
pp. 609-613
Author(s):  
Ioana D. Balea ◽  
Radu Hulea ◽  
Georgios E. Stavroulakis
Keyword(s):  
Finite Element ◽  
Genetic Algorithms ◽  
Global Optimization ◽  
Optimal Design ◽  
Optimization Problems ◽  
Steel Frames ◽  
Global Optimization Algorithm ◽  
Planar Structures ◽  
Discrete Global Optimization ◽  
Steel Sections

This paper presents an implementation of Eurocode load cases for discrete global optimization algorithm for planar structures based on the principles of finite element methods and genetic algorithms. The final optimal design is obtained using IPE sections chosen as feasible by the algorithm, from the available steel sections from industry. The algorithm is tested on an asymmetric planar steel frame with promising results.

Production cost optimization of enzyme washing for indigo dyed cotton denim by combining Kriging surrogate with differential evolution algorithm

2020 ◽  
Vol 90 (15-16) ◽  
pp. 1860-1871
Author(s):  
Jie Xu ◽  
Zhenglei He ◽  
Sheng Li ◽  
Wenbo Ke
Keyword(s):  
Differential Evolution ◽  
Process Parameters ◽  
Production Cost ◽  
Cost Optimization ◽  
Differential Evolution Algorithm ◽  
Kriging Model ◽  
Production Costs ◽  
Color Strength ◽  
Washing Process ◽  
Input Parameters

The enzyme washing process is extensively applied in the industrial production of denim garments. The process parameters of enzyme washing have significant effects on washing performances and costs. Since the relationships between the process parameters and washing performances cannot be expressed explicitly, it is impractical to determine the process parameters to obtain the optimal production cost while satisfying requirements of customers intuitively. This paper proposes an optimization methodology by combining Kriging surrogate and differential evolution (DE) algorithm to address the production cost optimization of enzyme washing for indigo dyed cotton denim. First, an experiment using Taguchi L16 orthogonal array is conducted where temperature and concentration of cellulase enzyme are taken into consideration with processing time as the input parameters, while the washing performances (including color strength value, stiffness, and tensile strength in warp and weft directions of the washed denim fabrics) are the output responses. Second, the relationships between the inputs and outputs are established using the Kriging model. Third, the effects of the input parameters on the washing performances are analyzed, and the production cost optimization model is illustrated. Finally, a case study is given to depict the optimization process and a verification experiment is conducted to verify the effectiveness of the optimal values. On the whole, the proposed hybrid method, Kriging-DE, shows great capability of optimizing the production costs of the enzyme washing process for indigo dyed cotton denim.

Cost Optimization of a Localized Irrigation System Using Genetic Algorithms

2010 ◽  
pp. 29-36 ◽  
Author(s):  
Mônica Sakuray Pais ◽  
Júlio César Ferreira ◽  
Marconi Batista Teixeira ◽  
Keiji Yamanaka ◽  
Gilberto Arantes Carrijo
Keyword(s):  
Genetic Algorithms ◽  
Irrigation System ◽  
Cost Optimization
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