Table of Contents
ISRN Aerospace Engineering
Volume 2014 (2014), Article ID 204546, 13 pages
http://dx.doi.org/10.1155/2014/204546
Research Article

Condition Based Maintenance Optimization of an Aircraft Assembly Process Considering Multiple Objectives

1Shanghai Aircraft Manufacturing Co., Ltd., Shanghai 200436, China
2Integrated Vehicle Health Management Centre, Cranfield University, Bedford MK43 0AL, UK
3Division of Engineering Sciences, Cranfield University, Bedford MK43 0AL, UK

Received 31 October 2013; Accepted 29 December 2013; Published 11 February 2014

Academic Editors: V. G. M. Annamdas, C. Bigelow, R. V. Rao, Y. Shi, and A. Yesildirek

Copyright © 2014 J. Li et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The Commercial Aircraft Cooperation of China (COMAC) ARJ21 fuselage’s final assembly process is used as a case study. The focus of this paper is on the condition based maintenance regime for the (semi-) automatic assembly machines and how they impact the throughput of the fuselage assembly process. The fuselage assembly process is modeled and analyzed by using agent based simulation in this paper. The agent approach allows complex process interactions of assembly, equipment, and maintenance to be captured and empirically studied. In this paper, the built network is modeled as the sequence of activities in each stage, which are parameterized by activity lead time and equipment used. A scatter search is used to find multiobjective optimal solutions for the CBM regime, where the maintenance related cost and production rate are the optimization objectives. In this paper, in order to ease computation intensity caused by running multiple simulations during the optimization and to simplify a multiobjective formulation, multiple Min-Max weightings are used to trace Pareto front. The empirical analysis reviews the trade-offs between the production rate and maintenance cost and how sensitive the design solution is to the uncertainties.