Solving the aircraft engine maintenance scheduling problem using a multi-objective evolutionary algorithm

Authors:
Mark P. Kleeman;Gary B. Lamont
Affiliations:
Dept of Electrical and Computer Engineering, Graduate School of Engineering & Management, Wright-Patterson AFB, Air Force Institute of Technology, Dayton, OH;Dept of Electrical and Computer Engineering, Graduate School of Engineering & Management, Wright-Patterson AFB, Air Force Institute of Technology, Dayton, OH
Venue:
EMO'05 Proceedings of the Third international conference on Evolutionary Multi-Criterion Optimization
Year:
2005

Citing 5
Cited 3

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms

Evolutionary algorithms in theory and practice: evolution strategies, evolutionary programming, genetic algorithms
GENOCOP: a genetic algorithm for numerical optimization problems with linear constraints

Communications of the ACM - Electronic supplement to the December issue
Multiobjective Scheduling by Genetic Algorithms

Multiobjective Scheduling by Genetic Algorithms
Schaum's Outline of Introduction to Probability & Statistics: Principles & Applications for Engineering & the Computing Sciences

Schaum's Outline of Introduction to Probability & Statistics: Principles & Applications for Engineering & the Computing Sciences
Evolutionary Algorithms for Solving Multi-Objective Problems

Evolutionary Algorithms for Solving Multi-Objective Problems

A multi-tiered memetic multiobjective evolutionary algorithm for the design of quantum cascade lasers

EMO'07 Proceedings of the 4th international conference on Evolutionary multi-criterion optimization
A new multi-objective particle swarm optimization algorithm for strategic planning of equipment maintenance

ICSI'11 Proceedings of the Second international conference on Advances in swarm intelligence - Volume Part II
A simple adaptive algorithm for numerical optimization

MICAI'12 Proceedings of the 11th Mexican international conference on Advances in Computational Intelligence - Volume Part II

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper investigates the use of a multi-objective genetic algorithm, MOEA, to solve the scheduling problem for aircraft engine maintenance. The problem is a combination of a modified job shop problem and a flow shop problem. The goal is to minimize the time needed to return engines to mission capable status and to minimize the associated cost by limiting the number of times an engine has to be taken from the active inventory for maintenance. Our preliminary results show that the chosen MOEA called GENMOP effectively converges toward better scheduling solutions and our innovative chromosome design effectively handles the maintenance prioritization of engines.