Multi-Objective Chance Constrained Programming of Spare Parts Based on Uncertainty Theory

The optimization of spare parts inventory is very important in the modern aerospace engineering system, especially in the environment with low management effectiveness and a wide variety of spare parts. At present, there are many optimization models for spare parts inventory, and the single-objectiv...

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Bibliographic Details
Published in:IEEE access Vol. 6; pp. 50049 - 50054
Main Authors: Yang, Yi, Wei, Kunlun, Kang, Rui, Wang, Sixin
Format: Journal Article
Language:English
Published: Piscataway IEEE 01.01.2018
The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subjects:
Aerospace engineering
Biological system modeling
Distribution functions
Genetic algorithm
Genetic algorithms
Hybrid systems
Mathematical model
Mathematical programming
multi-objective chance planning
Multiple objective analysis
Object oriented modeling
Optimization
Programming
Reliability
Spare parts
Uncertainty
uncertainty theory
ISSN:2169-3536, 2169-3536
Online Access:Get full text
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Summary:The optimization of spare parts inventory is very important in the modern aerospace engineering system, especially in the environment with low management effectiveness and a wide variety of spare parts. At present, there are many optimization models for spare parts inventory, and the single-objective optimization inventory is mostly used. But the single-objective optimization model has some limitations. First, in the applications of practical engineering, a single-goal decision problem is generally rare, and most of the decisions we have experienced involve many complicated goals. Second, it is difficult to truly present the actual situation when the mathematical programming model is used to discuss the optimization problem in practical engineering application. The solution to solve the model is a hybrid intelligent algorithm by combining the genetic algorithm with the inverse uncertainty distribution function. Finally, an example is given to illustrate the feasibility of the optimization model.
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ISSN:2169-3536
2169-3536
DOI:10.1109/ACCESS.2018.2860252