A Three-Stage Optimization Model for Scheduling the Facilities Maintenance Considering Random Failure Rate

Document Type : Article

Authors

1 Master of Civil Engineering, Islamic Azad University Center Tehran Branch, Tehran, Iran

2 PhD, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

3 Assistant Professor, Faculty of Industrial and Systems Engineering, Tarbiat Modares University, Tehran, Iran

Abstract

The increasing value of facilities, on the one hand, and the complexity of the equipment used in them, on the other, have increased the importance of planning for the maintenance of facilities, especially for companies which their facilities are located in different locations. In this paper, a new hybrid model has been presented to optimize facility maintenance scheduling by a combination of Genetic Algorithms (GA), Particle Swarm Optimization (PSO) and the Monte Carlo Simulation for organizing facilities which are in different locations as well as determining the optimum number of crews with three different skills of mechanical, electrical and simple workers. The main contributions of this paper include: (a) optimizing the number of crew by different skills in the first stage. (b) evaluation of fitness value for each solution through the Monte Carlo Simulation Model. (c) scheduling by consideration different failure rates for different facilities in different locations. In order to evaluate the performance of the proposed model, the model has been compared with Golpira’s model, the results of which have shown that it is possible to reduce the cost by just over 39% and reduce MTBF by over half.

Keywords

References

1.     Reference

1.             Coolen-Schrijner, P. and F.P. Coolen, 'Adaptive age replacement strategies based on nonparametric predictive inference'. Journal of the Operational Research Society, 2004. 55(12): p. 1281-1297.
2.             Zitrou, A., T. Bedford, and A. Daneshkhah, 'Robustness of maintenance decisions: Uncertainty modelling and value of information'. Reliability Engineering & System Safety, 2013. 120: p. 60-71.
3.             Liu, X., W. Wang, and R. Peng, 'An integrated production, inventory and preventive maintenance model for a multi-product production system'. Reliability Engineering & System Safety, 2015. 137: p. 76-86.
4.             Olanrewaju, A.L., A. Idrus, and M.F. Khamidi, 'Investigating building maintenance practices in Malaysia: a case study. Structural Survey', Journal of Industrial Mathematic, 2011.
5.             Lavy, S. and I.M. Shohet, 'Integrated maintenance management of hospital buildings: a case study'. Construction Management and Economics, 2004. 22(1): p. 25-34.
6.             de Jonge, B. and P.A. Scarf, 'A review on maintenance optimization'. European Journal of Operational Research, 2019.
7.             Chung, S.H., F.T. Chan, and H.K. Chan, 'A modified genetic algorithm approach for scheduling of perfect maintenance in distributed production scheduling'. Engineering Applications of Artificial Intelligence, 2009. 22(7): p. 1005-1014.
8.             Abdollahzadeh, H., K. Atashgar, and M. Abbasi, 'Multi-objective opportunistic maintenance optimization of a wind farm considering limited number of maintenance groups'. Renewable Energy, 2016. 88: p. 247-261.
9.             Wang, Y., H. Zuo, and D. Lv. 'Improved Multiobjective Maintenance Optimization of Aircraft Equipment Using Strength Pareto Genetic Algorithms with Immunity'. Fourth International Conference on Natural Computation. 2008. IEEE.
10.          Busacca, P.G., M. Marseguerra, and E. Zio. 'Application of Genetic Algorithms to the Multi-Objective Optimization of the Inspection Times of a Safety System of a Pressurized Water Reactor'. Proceedings of the European Safety & Reliability International Conference (ESREL’2001). 2001.
11.          Tan, Z. Li, J. Wu, Z. Zheng, J. et al., 'An evaluation of maintenance strategy using risk based inspection'. Safety science, 2011. 49(6): p. 852-860.
12.          Siddiqui, M,A. Shahid, I. Aamer, A. Jiping, L. et al., 'A Novel Idea for Optimizing Condition-Based Maintenance Using Genetic Algorithms and Continuous Event Simulation Techniques'. Mathematical Problems in Engineering, 2017. 2017.
13.          Ding, S.-H. and S. Kamaruddin, 'Maintenance policy optimization—literature review and directions'. The International Journal of Advanced Manufacturing Technology, 2015. 76(5-8): p. 1263-1283.
14.          Chikezie, C.U., A.T. Olowosulu, and O.S. Abejide, 'Multiobjective optimization for pavement maintenance and rehabilitation programming using genetic algorithms'. Arch Appl Sci Res, 2013. 5(4): p. 76-83.
15.          Garg, H., 'Fuzzy multiobjective reliability optimization problem of industrial systems using particle swarm optimization'. Journal of Industrial Mathematics, 2013. 2013.
16.          Deb, K. and S. Karthik. 'Dynamic multi-objective optimization and decision-making using modified NSGA-II: a case study on hydro-thermal power scheduling'.  International conference on evolutionary multi-criterion optimization. 2007. Springer.
17.          Riccardo, M. Riccardo, A. Teresa, Cennerazzo, Emilio. et al., 'The scheduling of maintenance. A resource-constraints mixed integer linear programming model'. Computers & Industrial Engineering, 2015. 87: p. 561-568.
18.          Cassady, C.R. and E. Kutanoglu, 'Minimizing job tardiness using integrated preventive maintenance planning and production scheduling'. IIE transactions, 2003. 35(6): p. 503-513.
19.          L, Dieullea. C, Bérenguera. A, Gralla. M, Roussignolb., 'Sequential condition-based maintenance scheduling for a deteriorating system'. European Journal of operational research, 2003. 150(2): p. 451-461.
20.          Lugtigheid, D., X. Jiang, and A.K. Jardine, 'A finite horizon model for repairable systems with repair restrictions'. Journal of the Operational Research Society, 2008. 59(10): p. 1321-1331.
21.          Roger, F. David, W. James, T. Luxhøj, T. 'Safety constraints applied to an adaptive Bayesian condition-based maintenance optimization model'. Reliability Engineering & System Safety, 2012. 102: p. 16-26.
22.          Wang, G.J. and Y.L. Zhang, 'Optimal repair–replacement policies for a system with two types of failures'. European Journal of Operational Research, 2013. 226(3): p. 500-506.
23.          Finkelstein, M., 'On the optimal degree of imperfect repair'. Reliability Engineering & System Safety, 2015. 138: p. 54-58.
24.          Lin, Z.-L., Y.-S. Huang, and C.-C. Fang, 'Non-periodic preventive maintenance with reliability thresholds for complex repairable systems'. Reliability Engineering & System Safety, 2015. 136: p. 145-156.
25.          Xiaojun, Z. Yanting, L. Lifeng, X. Jay, L., 'Multi-phase preventive maintenance policy for leased equipment'. International Journal of Production Research, 2015. 53(15): p. 4528-4537.
26.          De Jonge, B. and E. Jakobsons, 'Optimizing block-based maintenance under random machine usage'. European Journal of Operational Research, 2018. 265(2): p. 703-709.
27.          Fouladirad, M., C. Paroissin, and A. Grall, 'Sensitivity of optimal replacement policies to lifetime parameter estimates'. European Journal of Operational Research, 2018. 266(3): p. 963-975.
28.          Baklacioglu, T., 'Modeling the fuel flow-rate of transport aircraft during flight phases using genetic algorithm-optimized neural networks'. Aerospace Science and Technology, 2016. 49: p. 52-62.
29.          Bai, Q., 'Analysis of particle swarm optimization algorithm'. Computer and information science, 2010. 3(1): p. 180.
30.          Gountis, V.P. and A.G. Bakirtzis, 'Bidding strategies for electricity producers in a competitive electricity marketplace'. IEEE Transactions on Power Systems, 2004. 19(1): p. 356-365.
Scientia Iranica

Articles in Press, Accepted Manuscript
Available Online from 07 July 2021

Files

Share

How to cite

Statistics