January  2020, 16(1): 189-205. doi: 10.3934/jimo.2018146

Application of the preventive maintenance scheduling to increase the equipment reliability: Case study- bag filters in cement factory

Department of Industrial Engineering, Amirkabir University of Technology, 424 Hafez Avenue, 15916-34311, Tehran, Iran

* S. M. T. Fatemi Ghomi: Fatemi@aut.ac.ir

Received  June 2017 Revised  May 2018 Published  September 2018

This paper solves a new model of preventive maintenance scheduling with novel methodology. The aim of solving this problem is to determine the period for which bag filter should be taken off line for planned preventive maintenance over a specific time horizon and maintain a certain level of reliability with minimal maintenance cost. A mathematical programming method (Benders' decomposition) and a metaheuristic algorithm are presented to provide solutions. The obtained objective value from Benders' decomposition method is considered as the stopping criterion in the metaheuristic algorithm. To demonstrate the significance and originality of the proposed model and the efficiency of the algorithms, computational analysis is provided to realistic bag filters system in the cement factory. The obtained result is a schedule that allows the cement factory to consider the preventive maintenance for bag filters over the time horizon.

Citation: Masoud Ebrahimi, Seyyed Mohammad Taghi Fatemi Ghomi, Behrooz Karimi. Application of the preventive maintenance scheduling to increase the equipment reliability: Case study- bag filters in cement factory. Journal of Industrial & Management Optimization, 2020, 16 (1) : 189-205. doi: 10.3934/jimo.2018146
References:
[1]

H. Allaoui, Simultaneously scheduling n jobs and the preventive maintenance on the two-machine flow shop to minimize the makespan, International Journal of Production Economics, 112 (2008), 161-167.  doi: 10.1016/j.ijpe.2006.08.017.  Google Scholar

[2]

J. F. Benders, Partitioning procedures for solving mixed-variables programming problems, Numerische Mathematik, 4 (1962), 238-252.  doi: 10.1007/BF01386316.  Google Scholar

[3]

S. P. Canto, Application of Benders' decomposition to power plant preventive maintenance scheduling, European Journal of Operational Research, 184 (2008), 759-777.  doi: 10.1016/j.ejor.2006.11.018.  Google Scholar

[4]

J. X. Cao, The integrated yard truck and yard crane scheduling problem: Benders' decomposition-based methods, Transportation Research Part E: Logistics and Transportation Review, 46 (2010), 344-353.   Google Scholar

[5]

T. Chen, Reusable rocket engine preventive maintenance scheduling using genetic algorithm, Reliability Engineering and System Safety, 114 (2013), 52-60.   Google Scholar

[6]

M. DoostparastF. Kolahan and M. Doostparast, A reliability-based approach to optimize preventive maintenance scheduling for coherent systems, Reliability Engineering and System Safety, 126 (2014), 98-106.   Google Scholar

[7]

M. EbrahimiS. M. T. Fatemi Ghomi and B. Karimi, Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates, Applied Mathematical Modelling, 38 (2014), 2490-2504.  doi: 10.1016/j.apm.2013.10.061.  Google Scholar

[8]

M.-C. Fitouhi and M. Nourelfath, Integrating noncyclical preventive maintenance scheduling and production planning for multi-state systems, Reliability Engineering and System Safety, 121 (2014), 175-186.   Google Scholar

[9]

H. GoJ.-S. Kim and D.-H. Lee, Operation and preventive maintenance scheduling for containerships: Mathematical model and solution algorithm, European Journal of Operational Research, 229 (2013), 626-636.  doi: 10.1016/j.ejor.2013.04.005.  Google Scholar

[10]

M. Graisa and A. Al-Habaibeh, An investigation into current production challenges facing the Libyan cement industry and the need for innovative total productive maintenance (TPM) strategy, Journal of Manufacturing Technology Management, 22 (2011), 541-558.  doi: 10.1108/17410381111126445.  Google Scholar

[11]

E. GustavssonM. PatrikssonA. B. StrömbergA. Wojciechowski and M. Önnheim, Preventive maintenance scheduling of multi-component systems with interval costs, Computers and Industrial Engineering, 76 (2014), 390-400.   Google Scholar

[12]

M. KhatamiM. Mahootchi and R. Z. Farahani, Benders' decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties, Transportation Research Part E: Logistics and Transportation Review, 79 (2015), 1-21.   Google Scholar

[13]

Z. LuW. Cui and X. Han, Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty, Computers and Industrial Engineering, 80 (2015), 236-244.   Google Scholar

[14]

E. A. M. Miema and A. M. Mweta, An analysis of economics of investing in IT in the maintenance department: An empirical study in a cement factory in Tanzania, Journal of Quality in Maintenance Engineering, 9 (2003), 411-435.   Google Scholar

[15]

Moghaddam and S. Kamran, Multi-objective preventive maintenance and replacement scheduling in a manufacturing system using goal programming, International Journal of Production Economics, 146 (2013), 704-716.   Google Scholar

[16]

M. Mollahassani-PourA. Abdollahi and M. Rashidinejad, Application of a novel cost reduction index to preventive maintenance scheduling, International Journal of Electrical Power and Energy Systems, 56 (2014), 235-240.   Google Scholar

[17]

B. NaderiM. Zandieh and M. Aminnayeri, Incorporating periodic preventive maintenance into flexible flowshop scheduling problems, Applied Soft Computing, 11 (2011), 2094-2101.  doi: 10.1016/j.asoc.2010.07.008.  Google Scholar

[18]

M. PandeyM. J. Zuo and R. Moghaddass, Selective maintenance scheduling over a finite planning horizon, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 230 (2016), 162-177.   Google Scholar

[19]

M. Parastegari, AC constrained hydro-thermal generation scheduling problem: Application of Benders decomposition method improved by BFPSO, International Journal of Electrical Power and Energy Systems, 49 (2013), 199-212.   Google Scholar

[20]

Pereira and C. MNA, A particle swarm optimization (PSO) approach for non-periodic preventive maintenance scheduling programming, Progress in Nuclear Energy, 52 (2010), 710-714.   Google Scholar

[21]

S. Perez-Canto and J. C. Rubio-Romero, A model for the preventive maintenance scheduling of power plants including wind farms, Reliability Engineering and System Safety, 119 (2013), 67-75.   Google Scholar

[22]

H. Shafeek, Continuous improvement of maintenance process for the cement industry — a case study, Journal of Quality in Maintenance Engineering, 20 (2014), 333-376.  doi: 10.1108/JQME-07-2013-0047.  Google Scholar

[23]

W. ZhuM. Fouladirad and C. Berenguer, Bi-criteria maintenance policies for a system subject to competing wear and shock failures, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 229 (2015), 485-500.   Google Scholar

show all references

References:
[1]

H. Allaoui, Simultaneously scheduling n jobs and the preventive maintenance on the two-machine flow shop to minimize the makespan, International Journal of Production Economics, 112 (2008), 161-167.  doi: 10.1016/j.ijpe.2006.08.017.  Google Scholar

[2]

J. F. Benders, Partitioning procedures for solving mixed-variables programming problems, Numerische Mathematik, 4 (1962), 238-252.  doi: 10.1007/BF01386316.  Google Scholar

[3]

S. P. Canto, Application of Benders' decomposition to power plant preventive maintenance scheduling, European Journal of Operational Research, 184 (2008), 759-777.  doi: 10.1016/j.ejor.2006.11.018.  Google Scholar

[4]

J. X. Cao, The integrated yard truck and yard crane scheduling problem: Benders' decomposition-based methods, Transportation Research Part E: Logistics and Transportation Review, 46 (2010), 344-353.   Google Scholar

[5]

T. Chen, Reusable rocket engine preventive maintenance scheduling using genetic algorithm, Reliability Engineering and System Safety, 114 (2013), 52-60.   Google Scholar

[6]

M. DoostparastF. Kolahan and M. Doostparast, A reliability-based approach to optimize preventive maintenance scheduling for coherent systems, Reliability Engineering and System Safety, 126 (2014), 98-106.   Google Scholar

[7]

M. EbrahimiS. M. T. Fatemi Ghomi and B. Karimi, Hybrid flow shop scheduling with sequence dependent family setup time and uncertain due dates, Applied Mathematical Modelling, 38 (2014), 2490-2504.  doi: 10.1016/j.apm.2013.10.061.  Google Scholar

[8]

M.-C. Fitouhi and M. Nourelfath, Integrating noncyclical preventive maintenance scheduling and production planning for multi-state systems, Reliability Engineering and System Safety, 121 (2014), 175-186.   Google Scholar

[9]

H. GoJ.-S. Kim and D.-H. Lee, Operation and preventive maintenance scheduling for containerships: Mathematical model and solution algorithm, European Journal of Operational Research, 229 (2013), 626-636.  doi: 10.1016/j.ejor.2013.04.005.  Google Scholar

[10]

M. Graisa and A. Al-Habaibeh, An investigation into current production challenges facing the Libyan cement industry and the need for innovative total productive maintenance (TPM) strategy, Journal of Manufacturing Technology Management, 22 (2011), 541-558.  doi: 10.1108/17410381111126445.  Google Scholar

[11]

E. GustavssonM. PatrikssonA. B. StrömbergA. Wojciechowski and M. Önnheim, Preventive maintenance scheduling of multi-component systems with interval costs, Computers and Industrial Engineering, 76 (2014), 390-400.   Google Scholar

[12]

M. KhatamiM. Mahootchi and R. Z. Farahani, Benders' decomposition for concurrent redesign of forward and closed-loop supply chain network with demand and return uncertainties, Transportation Research Part E: Logistics and Transportation Review, 79 (2015), 1-21.   Google Scholar

[13]

Z. LuW. Cui and X. Han, Integrated production and preventive maintenance scheduling for a single machine with failure uncertainty, Computers and Industrial Engineering, 80 (2015), 236-244.   Google Scholar

[14]

E. A. M. Miema and A. M. Mweta, An analysis of economics of investing in IT in the maintenance department: An empirical study in a cement factory in Tanzania, Journal of Quality in Maintenance Engineering, 9 (2003), 411-435.   Google Scholar

[15]

Moghaddam and S. Kamran, Multi-objective preventive maintenance and replacement scheduling in a manufacturing system using goal programming, International Journal of Production Economics, 146 (2013), 704-716.   Google Scholar

[16]

M. Mollahassani-PourA. Abdollahi and M. Rashidinejad, Application of a novel cost reduction index to preventive maintenance scheduling, International Journal of Electrical Power and Energy Systems, 56 (2014), 235-240.   Google Scholar

[17]

B. NaderiM. Zandieh and M. Aminnayeri, Incorporating periodic preventive maintenance into flexible flowshop scheduling problems, Applied Soft Computing, 11 (2011), 2094-2101.  doi: 10.1016/j.asoc.2010.07.008.  Google Scholar

[18]

M. PandeyM. J. Zuo and R. Moghaddass, Selective maintenance scheduling over a finite planning horizon, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 230 (2016), 162-177.   Google Scholar

[19]

M. Parastegari, AC constrained hydro-thermal generation scheduling problem: Application of Benders decomposition method improved by BFPSO, International Journal of Electrical Power and Energy Systems, 49 (2013), 199-212.   Google Scholar

[20]

Pereira and C. MNA, A particle swarm optimization (PSO) approach for non-periodic preventive maintenance scheduling programming, Progress in Nuclear Energy, 52 (2010), 710-714.   Google Scholar

[21]

S. Perez-Canto and J. C. Rubio-Romero, A model for the preventive maintenance scheduling of power plants including wind farms, Reliability Engineering and System Safety, 119 (2013), 67-75.   Google Scholar

[22]

H. Shafeek, Continuous improvement of maintenance process for the cement industry — a case study, Journal of Quality in Maintenance Engineering, 20 (2014), 333-376.  doi: 10.1108/JQME-07-2013-0047.  Google Scholar

[23]

W. ZhuM. Fouladirad and C. Berenguer, Bi-criteria maintenance policies for a system subject to competing wear and shock failures, Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability, 229 (2015), 485-500.   Google Scholar

Figure 1.  Benders decomposition flow chart
Figure 2.  Solution representation
Figure 3.  A crossover example
Figure 4.  Solution procedures of NSGAII algorithm
Figure 5.  Converges of the lower and upper bounds versus iterations
Figure 6.  Trade offs between two objectives
Figure 7.  The progress of NSGAII for obtaining the optimal solution
Table 1.  The input parameters for model
Bag filter.NoBag filter sizeScale parameterShape parameterRepair time (hr)Replacement time (hr)Repair cost ($) Replacement cost($)
1 Small 2500 2.5 50 120 20 40
2 Small 2500 2.5 50 120 20 40
3 Small 2500 2.5 50 120 20 40
4 Small 2500 2.5 50 120 20 40
5 Small 2500 2.5 50 120 20 40
6 Small 2500 2.5 50 120 20 40
7 Small 2500 2.5 50 120 20 40
8 Small 2500 2.5 50 120 20 40
9 Small 2500 2.5 50 120 20 40
10 Small 2500 2.5 50 120 20 40
11 Small 2500 2.5 50 120 20 40
12 Medium 2400 2.6 50 120 50 100
13 Medium 2400 2.6 50 120 50 100
14 Medium 2400 2.6 50 120 50 100
15 Small 2500 2.5 50 120 20 40
16 Small 2500 2.5 50 120 20 40
17 Small 2500 2.5 50 120 20 40
18 Large 2400 2.4 50 120 120 240
19 Small 2500 2.5 50 120 20 40
20 Small 2500 2.5 50 120 20 40
21 Small 2500 2.5 50 120 20 40
22 Small 2500 2.5 50 120 20 40
23 Large 2400 2.4 50 120 120 240
24 Small 2500 2.5 50 120 20 40
25 Small 2500 2.5 50 120 20 40
26 Small 2500 2.5 50 120 20 40
27 Small 2500 2.5 50 120 20 40
28 Small 2500 2.5 50 120 20 40
29 Small 2500 2.5 50 120 20 40
30 Large 2400 2.4 50 120 120 240
31 Small 2500 2.5 50 120 20 40
32 Small 2500 2.5 50 120 20 40
33 Large 2400 2.4 50 120 120 240
34 Small 2500 2.5 50 120 20 40
35 Small 2500 2.5 50 120 20 40
Bag filter.NoBag filter sizeScale parameterShape parameterRepair time (hr)Replacement time (hr)Repair cost ($) Replacement cost($)
1 Small 2500 2.5 50 120 20 40
2 Small 2500 2.5 50 120 20 40
3 Small 2500 2.5 50 120 20 40
4 Small 2500 2.5 50 120 20 40
5 Small 2500 2.5 50 120 20 40
6 Small 2500 2.5 50 120 20 40
7 Small 2500 2.5 50 120 20 40
8 Small 2500 2.5 50 120 20 40
9 Small 2500 2.5 50 120 20 40
10 Small 2500 2.5 50 120 20 40
11 Small 2500 2.5 50 120 20 40
12 Medium 2400 2.6 50 120 50 100
13 Medium 2400 2.6 50 120 50 100
14 Medium 2400 2.6 50 120 50 100
15 Small 2500 2.5 50 120 20 40
16 Small 2500 2.5 50 120 20 40
17 Small 2500 2.5 50 120 20 40
18 Large 2400 2.4 50 120 120 240
19 Small 2500 2.5 50 120 20 40
20 Small 2500 2.5 50 120 20 40
21 Small 2500 2.5 50 120 20 40
22 Small 2500 2.5 50 120 20 40
23 Large 2400 2.4 50 120 120 240
24 Small 2500 2.5 50 120 20 40
25 Small 2500 2.5 50 120 20 40
26 Small 2500 2.5 50 120 20 40
27 Small 2500 2.5 50 120 20 40
28 Small 2500 2.5 50 120 20 40
29 Small 2500 2.5 50 120 20 40
30 Large 2400 2.4 50 120 120 240
31 Small 2500 2.5 50 120 20 40
32 Small 2500 2.5 50 120 20 40
33 Large 2400 2.4 50 120 120 240
34 Small 2500 2.5 50 120 20 40
35 Small 2500 2.5 50 120 20 40
Table 2.  Maintenance scheduling for bag filters
B/p12345678910111213
1 $\surd$
2 $\surd$
3 $\surd$
4 $\surd$
5 $\surd$
6 $\surd$
7 $\surd$
8 $\surd$
9 $\surd$
10 $\surd$
11 $\surd$
12 $\surd$
13 $\surd$
14 $\surd$
15 $\surd$
16 $\surd$
17 $\surd$
18 $\surd$
19 $\surd$
20 $\surd$
21 $\surd$
22 $\surd$
23 $\surd$
24 $\surd$
25 $\surd$
26 $\surd$
27 $\surd$
28 $\surd$
29 $\surd$
30 $\surd$
31 $\surd$
32 $\surd$
33 $\surd$
34 $\surd$
35 $\surd$
B/p12345678910111213
1 $\surd$
2 $\surd$
3 $\surd$
4 $\surd$
5 $\surd$
6 $\surd$
7 $\surd$
8 $\surd$
9 $\surd$
10 $\surd$
11 $\surd$
12 $\surd$
13 $\surd$
14 $\surd$
15 $\surd$
16 $\surd$
17 $\surd$
18 $\surd$
19 $\surd$
20 $\surd$
21 $\surd$
22 $\surd$
23 $\surd$
24 $\surd$
25 $\surd$
26 $\surd$
27 $\surd$
28 $\surd$
29 $\surd$
30 $\surd$
31 $\surd$
32 $\surd$
33 $\surd$
34 $\surd$
35 $\surd$
Table 3.  Maintenance scheduling based on 52 weeks and type of bag filters, system reliability
WeekSmall bag filterMedium bag filterLarge bag filterReliability at the end of week
1 97.2%
2 3 93.6%
3 97.6%
4 23, 30 91.3%
5 9 92.4%
6 26 92.0%
7 95.7%
8 95.4%
9 15 93.6%
10 96.0%
11 21, 28, 29 90.8%
12 1, 5, 35 91.1%
13 95.5%
14 94.8%
15 l 96.2%
16 93.9%
17 6 92.4%
18 94.6%
19 7, 20 90.4%
20 95.0%
21 96.2%
22 15 93.9%
23 24 93.4%
24 91.1%
25 92.2%
26 94.6%
27 2 90.4%
28 34 90.0%
29 91.7%
30 90.0%
31 16, 19 91.6%
32 2.5 93.2%
33 8 90.7%
34 25 91.3%
35 91.8%
36 33 90.7%
37 12, 13 90.0%
38 22 90.8%
39 14 91.2%
40 92.1%
41 92.0%
42 32 90.3%
43 93.0%
44 11 2500 2.5 91.0%
45 92.1%
46 91.9%
47 17, 31 90.2%
48 91.7%
49 3, 27 90.0%
50 93.4%
51 17 91.6%
52 95.7%
WeekSmall bag filterMedium bag filterLarge bag filterReliability at the end of week
1 97.2%
2 3 93.6%
3 97.6%
4 23, 30 91.3%
5 9 92.4%
6 26 92.0%
7 95.7%
8 95.4%
9 15 93.6%
10 96.0%
11 21, 28, 29 90.8%
12 1, 5, 35 91.1%
13 95.5%
14 94.8%
15 l 96.2%
16 93.9%
17 6 92.4%
18 94.6%
19 7, 20 90.4%
20 95.0%
21 96.2%
22 15 93.9%
23 24 93.4%
24 91.1%
25 92.2%
26 94.6%
27 2 90.4%
28 34 90.0%
29 91.7%
30 90.0%
31 16, 19 91.6%
32 2.5 93.2%
33 8 90.7%
34 25 91.3%
35 91.8%
36 33 90.7%
37 12, 13 90.0%
38 22 90.8%
39 14 91.2%
40 92.1%
41 92.0%
42 32 90.3%
43 93.0%
44 11 2500 2.5 91.0%
45 92.1%
46 91.9%
47 17, 31 90.2%
48 91.7%
49 3, 27 90.0%
50 93.4%
51 17 91.6%
52 95.7%
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