The Determination of Preventive Maintenance using Simulated Annealing Algorithm Based on Weighted Fitness Function

This study aims to determine the machine maintenance schedule. We use the Simulated Annealing Algorithm. Fitness and reliability functions are functions that are used in the optimization process. Several weighting scenarios are done to see the unity of the function. The results of the scenario produce several alternative schedules. This algorithm is implemented on machines that have more than one sub-machine. This sub-machine is a smaller engine system part. This sub-machine also has one particular function. The results of the study show that the optimal engine maintenance period to use is six periods. There are five scheduling scenarios used in this problem. The resulting schedule can increase the value of reliability and can minimize costs.

Download Full-text

The Research on Mutil-Objective Location Routing Problem Based on Genetic Simulated Annealing Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.543-547.2842 ◽

2014 ◽

Vol 543-547 ◽

pp. 2842-2845 ◽

Cited By ~ 1

Author(s):

Gai Li Du ◽

Nian Xue

Keyword(s):

Genetic Algorithm ◽

Simulated Annealing ◽

Simulated Annealing Algorithm ◽

Fitness Function ◽

Solution Space ◽

Location Routing Problem ◽

Routing Problem ◽

Genetic Simulated Annealing Algorithm ◽

Location Routing ◽

Annealing Algorithm

This paper analysis the basic principles of the genetic algorithm (GA) and simulated annealing algorithm (SA) thoroughly. According to the characteristics of mutil-objective location routing problem, the paper designs the hybrid genetic algorithm in various components, and simulate achieved the GSAA (Genetic Simulated Annealing Algorithm).Which architecture makes it possible to search the solution space easily and effectively without overpass computation. It avoids effectively the defects of premature convergence in traditional genetic algorithm, and enhances the algorithms global convergence. Also it improves the algorithms convergence rate to some extent by using the accelerating fitness function. Still, after comparing with GA and SA, the results show that the proposed Genetic Simulated Annealing Algorithm has better search ability. And the emulation experiments show that this method is valid and practicable.

Download Full-text

Research of the Determination of Thin Film Optical Constants by Using the Simulated Annealing Algorithm

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.66-68.9 ◽

2011 ◽

Vol 66-68 ◽

pp. 9-13

Author(s):

Wen Liang Wang ◽

Xiao Hong Rong

Keyword(s):

Thin Film ◽

Simulated Annealing ◽

Physical Process ◽

Optical Constant ◽

Optical Constants ◽

Simulated Annealing Algorithm ◽

Mathematic Model ◽

Transmission Data ◽

Annealing Algorithm

Simulated annealing algorithm is a mathematic model, which imitates the physical process of annealing. And optical thin film is widely used in many industry, To perform the functions for which they were designed, the films must have proper thickness, roughness and other characteristics. In the paper, the simulated annealing algorithm is used to determine the film optical constant It uses the transmission data through thin film over a range of wavelengths to calculate the thickness. According to the experimental results, the solution found by simulated annealing algorithm is unique and correct.

Download Full-text