scholarly journals GAPWM: a genetic algorithm method for optimizing a position weight matrix

2007 ◽  
Vol 23 (10) ◽  
pp. 1188-1194 ◽  
Author(s):  
L. Li ◽  
Y. Liang ◽  
R. L. Bass
Keyword(s):  
Genetic Algorithm ◽  
Position Weight Matrix ◽  
Weight Matrix ◽  
Genetic Algorithm Method

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.

Genetic Algorithm and its Application in Optimization of Frequency Selective Surface

2010 ◽  
Vol 143-144 ◽  
pp. 1046-1050
Author(s):  
Jing Yu Han ◽  
Wang Qun ◽  
Chuan You Li ◽  
Zhang Hong Tang ◽  
Mei Wu Shi
Keyword(s):  
Genetic Algorithm ◽  
Cost Function ◽  
Electroless Plating ◽  
Genetic Basis ◽  
Optimization Method ◽  
Frequency Selective Surface ◽  
Test System ◽  
Genetic Algorithm Method ◽  
Frequency Selective ◽  
Test Result

In this paper, a new genetic algorithm method to optimize the frequency selective surface(FSS) is presented. The optimization speed and definition are promoted by limiting the parameter range and changing the genetic basis. A new cost function is introduced to optimize the multi-frequency of FSS by multi-object optimization (MO). The cirque element was optimized by the optimization method, fabricated by the selective electroless plating on fabric and measured by the arch test system. Test result proves the simulated result coincide with measured result. Result shows that it’s possible to realize different optimizations based on the various applying by this method.

Air Suspension Optimal Control Based on the Genetic Algorithm

2011 ◽  
Vol 467-469 ◽  
pp. 1066-1071
Author(s):  
Zhong Xin Li ◽  
Ji Wei Guo ◽  
Ming Hong Gao ◽  
Hong Jiang
Keyword(s):  
Genetic Algorithm ◽  
Optimal Control ◽  
Dynamic Model ◽  
Control Method ◽  
Control Model ◽  
Weight Matrix ◽  
Object A ◽  
Full Vehicle ◽  
Air Suspension ◽  
Optimal Control Method

Taking the full-vehicle eight-freedom dynamic model of a type of bus as the simulation object , a new optimal control method is introduced. This method is based on the genetic algorithm, and the full-vehicle optimal control model is built in the MatLab. The weight matrix of the optimal control is optimized through the genetic algorithm; then the outcome is compared with the artificially-set optimal control simulation, which shows that the genetic-algorithm based optimal control presents better performance, thereby creating a smoother ride and improving the steering stability of the vehicle.

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