Paper Info
Title
Spare Parts Allocation By Improved Genetic Algorithm And Monte Carlo Simulation
Abstract
A combined Monte Carlo (MC) simulation and Genetic Algorithm (GA) method was proposed by other researchers for the optimisation of spare parts allocation. From case studies, it was found that the number of simulation trials of the existing method tended to be either excessive or inadequate. Thus, a simulation replication number control method making full use of the advance simulation effort is proposed and implemented into the existing method. A numerical example shows significant improvement on overall simulation efficiency and that at the same time the required accuracy is guaranteed. Furthermore, it is argued that application-specific knowledge should be embedded into the general GA procedure so that the evolution process can be more efficient. Heuristic methods for initial population preparation for GA with and without considering component cost difference are proposed and illustrated for spare parts allocation. A computing experiment was designed and performed to examine the influence of parameters for replication number control and initial population preparation. The generation of availability-cost curve further indicates the necessity to adopt heuristic methods to improve searching efficiency in GA.
Year
DOI
Venue
2012
10.1080/00207720802556252
INTERNATIONAL JOURNAL OF SYSTEMS SCIENCE
Keywords
Field
DocType
Monte Carlo simulation, genetic algorithm, spare parts allocation
Population,Mathematical optimization,Heuristic,Monte Carlo method,Spare part,Computer science,Genetic algorithm
Journal
Volume
Issue
ISSN
43
6
0020-7721
Citations 
PageRank 
References 
1
0.35
11
Authors
2
Name
Order
Citations
PageRank
Sheng-Yi Li1177.33
Zhizhong Li29219.78