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

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.

2013 ◽  
Vol 791-793 ◽  
pp. 1423-1426
Author(s):  
Hai Min Wei ◽  
Rong Guang Liu

Project schedule management is the management to each stage of the degree of progress and project final deadline in the project implementation process. Its purpose is to ensure that the project can meet the time constraints under the premise of achieving its overall objectives.When the progress of schedule found deviation in the process of schedule management ,the progress of the plan which have be advanced previously need to adjust.This article mainly discussed to solve the following two questions:establish the schedule optimization model by using the method of linear;discuss the particle swarm optimization (PSO) algorithm and its parameters which have effect on the algorithm:Particle swarm optimization (PSO) algorithm is presented in the time limited project and the application of a cost optimization.


Author(s):  
Jiten Makadia ◽  
C.D. Sankhavara

Swarm Intelligence algorithms like PSO (Particle Swarm Optimization), ACO (Ant Colony Optimization), ABC (Artificial Bee Colony), Glow-worm swarm Optimization, etc. have been utilized by researchers for solving optimization problems. This work presents the application of a novel modified EHO (Elephant Herding Optimization) for cost optimization of shell and tube heat exchanger. A comparison of the results obtained by EHO in two benchmark problems shows that it is superior to those obtained with genetic algorithm and particle swarm optimization. The overall cost reduction is 13.3 % and 9.68% for both the benchmark problem compared to PSO. Results indicate that EHO can be effectively utilized for solving real-life optimization problems.


2005 ◽  
Vol 127 (4) ◽  
pp. 885-892 ◽  
Author(s):  
P. Asokan ◽  
N. Baskar ◽  
K. Babu ◽  
G. Prabhaharan ◽  
R. Saravanan

The development of comprehensive grinding process models and computer-aided manufacturing provides a basis for realizing grinding parameter optimization. The variables affecting the economics of machining operations are numerous and include machine tool capacity, required workpiece geometry, cutting conditions such as speed, feed, and depth of cut, and many others. Approximate determination of the cutting conditions not only increases the production cost, but also diminishes the product quality. In this paper a new evolutionary computation technique, particle swarm optimization, is developed to optimize the grinding process parameters such as wheel speed, workpiece speed, depth of dressing, and lead of dressing, simultaneously subjected to a comprehensive set of process constraints, with an objective of minimizing the production cost and maximizing the production rate per workpiece, besides obtaining the finest possible surface finish. Optimal values of the machining conditions obtained by particle swarm optimization are compared with the results of genetic algorithm and quadratic programming techniques.


2012 ◽  
Vol 605-607 ◽  
pp. 1448-1452
Author(s):  
Wei Wei Liu ◽  
Ye Zheng

Based on CNC lathe product’s technical and economic properties demand for manufacturing industry of CNC equipment, this thesis studied the mutual relationship of machining accuracy and cost. According to adaptive particle swarm optimization, a optimization model of the two parts was established. The case of a CNC lathe shows that the optimization model has the advantages of rapid searching, high accuracy, effective and provides a new way for the machining accuracy and cost optimization.


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