Using Genetic Algorithm to Robust Multi Objective Optimization of Maintenance Scheduling Considering Engineering Insurance
محورهای موضوعی : Business StrategySomayeh Molaei 1 , Mir Mahdi Seyed Esfahani 2 , Akbar Esfahanipour 3
1 - Department of Industrial Engineering,
Amirkabir University of Technology,
Iran, Tehran
2 - Department of Industrial Engineering,
Amirkabir University of Technology,
Iran, Tehran
3 - Department of Industrial Engineering,
Amirkabir University of Technology,
Iran, Tehran
کلید واژه: Genetic Algorithm, Robust Optimization, Preventive maintenance, engineering insurance, global criterion method, Pareto set solutions,
چکیده مقاله :
Efficient and on-time maintenance plays a crucial role inreducing cost and increasing the market share of an industrial unit. Preventivemaintenance is a broad term that encompasses a set of activitiesaimed at improving the overall reliability and availability of a systembefore machinery breakdown. The previous studies have addressed thescheduling of preventive maintenance. These studies have computed thetime and the type of preventive maintenance by modeling the total costrelated to it. Todays the engineering insurance is an appropriate anddurable protection for reducing the risks related to the industrial machinery.This kind of insurance covers a part of maintenance costs. Previousresearches did not consider the effect of engineering insuranceon maintenance scheduling while it affects the total cost function of maintenance scheduling seriously. Given the above-mentioned remarks,this paper introduces for the first time a new scheduling of preventivemaintenance with considering total cost and total reliability of the systemin which the effect of engineering insurance has been taken intoaccount. Due to the uncertainty in the input parameters, which arevery common in application, the paper proposed the application of robustdesign approaches. To solve this multi objective model, first it hasbeen transformed into a single objective model by using global criterionand the resultant model is solved through genetic algorithm. Theresults show the magnitude effect of engineering insurance on maintenancescheduling. Therefore, neglecting the importance of engineeringinsurance leads to an inefficient scheduling maintenance.
[1] Bashiri, M., Badri, H., and Hejazi, T. H. (2011), Selecting optimum maintenance
strategy by fuzzy interactive linear assignment method. Applied
Mathematical Modeling, 35 (1), 152-164.
[2] Bevilacqua, M. and Braglia, M. (2000), The analytic hierarchy process applied
to maintenance strategy selection. Reliability Engineering & System
Safety, 70 (1), 71-83.
[3] Mobley, R. K. (2002), An introduction to predictive maintenance.
Butterworth-Heinemann.
[4] Li, J. R., Khoo, L. P., and Tor, S. B. (2006), Generation of possible multiple
components disassembly sequence for maintenance using a disassembly
constraint graph. International Journal of Production Economics, 102 (1)
51-65.
[5] Canfield, R. V. (1986), Cost optimization of periodic preventive maintenance.
Reliability, IEEE Transactions, 35 (1), 78-81.
[6] Panagiotidou, S. and Tagaras, G. (2007), Optimal preventive maintenance
for equipment with two quality states and general failure time distributions.
European journal of operational research, 180 (1), 329-353.
[7] Yao, X., Fu, M., Marcus, S. I., and Fernandez-Gaucherand, E. (2001), Optimization
of preventive maintenance scheduling for semiconductor manufacturing
systems: models and implementation. In Control Applications,
Proceedings of the 2001 IEEE International Conference, 407-411.
[8] Charles, A. S., Floru, I. R., Azzaro-Pantel, C., Pibouleau, L., and
Domenech, S. (2003), Optimization of preventive maintenance strategies
in a multipurpose batch plant: application to semiconductor manufacturing.
Computers & chemical engineering, 27 (4), 449-467.
[9] Usher, J. S., Kamal, A. H., and Syed, W. H. (1998), Cost optimal preventive
maintenance. IIE transactions, 309 (12), 1121-1128.
[10] Levitin, G. and Lisnianski, A. (2000), Short communication optimal replacement
scheduling in multi?state series-parallel systems. Quality and
Reliability Engineering International, 16 (2), 157-162.
[11] Jayakumar, A. and Asgarpoor, S. (2004), Maintenance optimization of
equipment by linear programming. In Probabilistic Methods Applied to
Power Systems, 145-149.
[12] Canto, S. P. (2008), Application of Benders’ decomposition to power plant
preventive maintenance scheduling. European journal of operational research,
184 (2), 759-777.
[13] Budai, G., Huisman, D., and Dekker, R. (2005), Scheduling preventive
railway maintenance activities. Journal of the Operational Research Society,
57 (9), 1035-1044.
[14] Shirmohammadi, A. H., Zhang, Z. G., and Love, E. (2007), A computational
model for determining the optimal preventive maintenance policy
with random breakdowns and imperfect repairs. Reliability, IEEE Transactions,
56 (2), 332-339.
[15] Tam, A. S. B., Chan, W. M., and Price, J. W. H. (2006), Optimal maintenance
intervals for a multi-component system. Production Planning and
Control, 17 (8), 769-779.
[16] Moghaddam, K. S. and Usher, J. S. (2011), Preventive maintenance and
replacement scheduling for repairable and maintainable systems using dynamic
programming. Computers & Industrial Engineering, 60 (4), 654-
665.
[17] Kim, H., Nara, K., and Gen, M. (1994), A method for maintenance
scheduling using GA combined with SA. Computers & Industrial Engineering,
27 (1), 477-480.
[18] Samrout, M., Yalaoui, F., Chtelet, E., and Chebbo, N. (2005), New methods
to minimize the preventive maintenance cost of series-parallel systems
using ant colony optimization. Reliability engineering & system safety, 89
(3), 346-354.
[19] Limbourg, P. and Kochs, H. D. (2006), Preventive maintenance scheduling
by variable dimension evolutionary algorithms. International journal of
pressure vessels and piping, 83 (4), 262-269.
[20] Quan, G., Greenwood, G. W., Liu, D., and Hu, S. (2007), Searching for
multiobjective preventive maintenance schedules: Combining preferences
with evolutionary algorithms. European Journal of Operational Research,
177 (3), 1969-1984.
[21] Moradi, E., Fatemi Ghomi, S. M. T., and Zandieh, M. (2011), Bi-objective
optimization research on integrated fixed time interval preventive maintenance
and production for scheduling flexible job-shop problem. Expert
systems with applications, 38 (6), 7169-7178.
[22] Naderi, B., Zandieh, M., and Aminnayeri, M. (2011), Incorporating periodic
preventive maintenance into flexible flow shop scheduling problems.
Applied Soft Computing, 11 (2), 2094-2101.
[23] Berrichi, A., Yalaoui, F., Amodeo, L., and Mezghiche, M. (2010), Bi-
Objective Ant Colony Optimization approach to optimize production and
maintenance scheduling. Computers & Operations Research, 37 (9), 1584-
1596.
[24] Smith. C. O. (1976), Introduction to Reliability in Design, first edition,
McGraw-Hill, New York.
[25] Yun, W. Y. and Kim, J. W. (2004), Multi-level redundancy optimization
in series systems. Computers & Industrial Engineering, 46 (2), 337-346.
[26] Kouvelis, P. and Yu, G. (1997), Robust discrete optimization and its applications,
14, Springer.
[27] Serafini, P. (1994), Simulated annealing for multi objective optimization
problems. In Multiple criteria decision making, 283-292).
[28] Suppapitnarm, A., Seffen, K. A., Parks, G. T., and Clarkson, P. J. (2000),
A simulated annealing algorithm for multiobjective optimization. Engineering
Optimization, 33 (1), 59-85.
