Maintenance Priority Assignment Utilizing On-line Production Information

[+] Author and Article Information
Zimin Yang, Dragan Djurdjanovic, Jun Ni

Center for Intelligent Maintenance Systems (IMS), Department of Mechanical Engineering, University of Michigan, 2350 Hayward Street, Ann Arbor, MI 48109-2125

Qing Chang

 General Motors Research and Development Center, 30500 Mound Road, Warren, MI 48090-9055

Jay Lee

Center for Intelligent Maintenance Systems (IMS), Department of Mechanical, Industrial and Nuclear Engineering, University of Cincinnati, 598 Rhodes Hall, P. O. Box 210072, Cincinnati, OH 45221-0072

J. Manuf. Sci. Eng 129(2), 435-446 (Mar 24, 2006) (12 pages) doi:10.1115/1.2336257 History: Received April 07, 2005; Revised March 24, 2006

Maintenance management has a direct influence on production performance. Existing works have not systematically taken the on-line production information into consideration in determining maintenance work-order priority, which is often assigned either through an ad hoc approach or using largely heuristic and static methods. In this paper, we first present a metric that can be used to quantitatively evaluate the effects of different maintenance priorities. Based on this index, one can employ a search algorithm to obtain maintenance work-order priorities that will lead to improved productivity within the optimization horizon. These concepts and methods are validated through simulation experiments and implementation in a real industrial facility. The results show that the effective utilization of on-line production data in dynamic maintenance scheduling can yield visible production benefit through maintenance priority optimization.

Copyright © 2007 by American Society of Mechanical Engineers
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Figure 3

Genetic algorithm (GA) used to implement a heuristic optimization for finding the optimal priority of maintenance work orders

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Figure 1

Buffer effects on machine importance

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Figure 2

Comparing gravity field with production flow

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Figure 4

Crossover and mutation scheme used in the priority search

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Figure 5

Test line configuration and parameters

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Figure 6

Line configuration used in Scenarios 3 and 4

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Figure 7

Relative improvement by using SVB

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Figure 8

Simplified layout of the system used in the experimental study

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Figure 9

Simplified representation of group 2



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