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.

1.
Levitt
,
J.
, 1997,
The Handbook of Maintenance Management
,
Industrial Press
,
NY
.
2.
Mann
,
L. J.
, 1983,
Maintenance Management
,
Lexington Books
,
Lexington, MA
.
3.
Saaty
,
T. L.
, 1990,
The Analytic Hierarchy Process—Planning, Priority Setting, Resource Allocation
,
RWS Publications
,
University of Pittsburgh
.
4.
Dekker
,
R.
, 1995, “
Integrating Optimization, Priority Setting, Planning and Combining of Maintenance Activities
,”
Eur. J. Oper. Res.
0377-2217,
82
, pp.
225
240
.
5.
Shen
,
Q.-P.
,
Lo
,
K.-K.
, and
Wang
,
Q.
, 1998, “
Priority Setting in Maintenance Management: A Modified Multi-Attribute Approach Using Analytic Hierarchy Process
,”
Constr. Manage. Econom.
0144-6193,
16
, pp.
693
702
.
6.
Wang
,
H.
, 2002, “
A Survey of Maintenance Policies of Deteriorating Systems
,”
Eur. J. Oper. Res.
0377-2217,
139
, pp.
469
489
.
7.
Koc
,
M.
,
Ni
,
J.
,
Lee
,
J.
, and
Bandyopadhyay
,
P.
, 2003, “
e-Manufacturing—Trends and Opportunities
,”
International Journal of Advanced Manufacturing Systems
,
6
(
1
), pp.
29
46
.
8.
Casoetto
,
N
,
Djurdjanovic
,
D.
,
Mayor
,
R.
,
Lee
,
J.
, and
Ni
,
J.
, 2003, “
Multisensor Process Performance Assessment Through the Use of Autoregressive Modeling and Feature Maps
,”
Trans. NAMRI/SME
1047-3025,
31
, pp.
483
490
.
9.
Djurdjanovic
,
D.
,
Lee
,
J.
, and
Ni
,
J.
, 2003 “
Watchdog Agent—An Infotronics Based Prognostics Approach for Product Performance Degradation Assessment and Prediction
,”
Adv. Eng. Inf.
1474-0346,
17
(3–4), pp.
109
125
.
10.
Biggs
,
N. L.
,
Lloyd
,
E. K.
, and
Wilson
,
R. J.
, 1976,
Graph Theory 1736–1936
,
Clarendon
,
Oxford
.
11.
NIST/SEMATECH,
e-Handbook of Statistical Methods
, Section 3.3.3.3, available via http://www.itl.nist.gov/div898/handbookhttp://www.itl.nist.gov/div898/handbook
12.
Burdick
,
R. K.
, 1992,
Confidence Intervals on Variance Components
,
Dekker
,
New York
.
13.
PROMODEL Corp
,
ProModel: Manufacturing Simulation Software: User’s Guide
, 2002.
14.
Kelton
,
W.
,
Sadowski
,
R.
, and
Sturrock
,
D.
, 2004,
Simulation with Arena
,
3rd ed.
,
McGraw-Hill
,
Boston, MA
.
15.
Holland
,
J. H.
, 1962,
Adaptation in Natural and Artificial Systems
,
University of Michigan
,
Ann Arbor, MI
.
16.
Back
,
T.
, 1996,
Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms
,
Oxford University Press
,
NY
.
17.
De Jong
,
K. A.
, 1975, “
Analysis of the Behavior of a Class of Genetic Adaptive Systems
,” Doctoral dissertation, University of Michigan, Dissertation Abstracts International, Vol. 36(10), p.
5140B
.
18.
Bäck
,
T.
, 1996,
Evolutionary Algorithms in Theory and Practice
,
Oxford University Press
,
NY
.
You do not currently have access to this content.