Opportunistic Maintenance Modeling of Large-Scale Hot Rolling Production Line Based on Maintenance Priority and Dual Variable Time Window

doi:10.16183/j.cnki.jsjtu.2023.173

Abstract

Abstract:

To solve the problem of differential maintenance demand caused by daily dynamic maintenance and periodic replacement of support rolls in large-scale hot rolling lines, a maintenance priority rule is introduced to identify maintenance judgment order of components under long distance condition and downtime constraint. A dual variable time window rule is constructed to identify whether components should be maintained and distinguish maintenance demand, and then a dynamic opportunity maintenance model is established for the system. The case study shows that the model can effectively solve the problem of differential maintenance scheduling for large-scale hot rolling lines under the constraints of maintenance resources and downtime, and has more cost advantages than the traditional time-window-based model.

Key words: large-scale hot rolling line, opportunistic maintenance, preventive maintenance, maintenance priority, variable time window

CLC Number:

TP207

MAO Wenxin, ZHOU Xiaojun. Opportunistic Maintenance Modeling of Large-Scale Hot Rolling Production Line Based on Maintenance Priority and Dual Variable Time Window[J]. Journal of Shanghai Jiao Tong University, 2024, 58(8): 1221-1230.

Figures/Tables 10

Fig.1

Fig.2

Fig.3

Tab.1

Tab.2

Component maintenance parameters and fault parameters

i	j	α_i,j	β_i,j	θ_i,j	$c i, j M R$ /元	$c i, j P M$ /元	c^D/元	$τ i, j P M$ /h
1	1	1.91	190.30	0.99	19 955.75	10 022.30	63 926.94	0.26
1	2	2.11	572.81	0.99	14 660.86	7 338.08	63 926.94	0.29
1	3	3.19	262.87	0.99	19 628.20	9 834.12	63 926.94	0.34
2	1	2.15	190.81	0.97	21 364.43	10 778.73	63 926.94	0.23
2	2	2.89	336.95	0.97	24 905.46	12 551.00	63 926.94	0.30
2	3	2.38	298.07	0.97	11 100.67	5 552.24	63 926.94	0.30
3	1	2.05	323.81	0.98	19 687.53	9 845.41	63 926.94	0.34
3	2	1.40	218.42	0.98	21 586.06	10 854.72	63 926.94	0.33
3	3	2.42	226.00	0.97	16 004.07	8 068.35	63 926.94	0.23
3	4	2.34	189.25	0.98	18 473.28	8 274.24	63 926.94	0.29
4	1	2.44	289.29	0.98	11 235.69	8 163.95	63 926.94	0.26
4	2	2.91	450.77	0.96	7 524.52	3 765.91	63 926.94	0.24
4	3	1.96	360.03	0.97	23 924.34	11 962.74	63 926.94	0.21
4	4	1.65	240.31	0.98	24 208.23	12 198.36	63 926.94	0.29
4	5	2.04	352.06	0.98	11 982.24	6 026.53	63 926.94	0.23
5	2	2.42	253.50	0.96	10 127.18	5 139.05	63 926.94	0.34
5	3	2.18	277.35	0.99	15 932.36	8 253.69	63 926.94	0.32
5	4	2.08	244.23	0.96	20 118.95	10 129.69	63 926.94	0.25
6	1	2.78	190.87	0.99	15 752.26	7 950.42	63 926.94	0.30
6	2	1.90	356.04	0.99	10 034.88	5 073.03	63 926.94	0.36
6	3	3.91	239.46	0.97	8 474.86	4 303.24	63 926.94	0.27

Tab.2

Tab.3

Tab.4

Comparison of results of different strategies

策略	$T 1 W$ /h	$T 2 W$ /h	C/元	$C s y s P M$ /元	$C s y s M R$ /元	$C s y s D$ /元
1	60	50	11 906 720.0	1 804 806.52	5 237 031.37	4 864 882.08
2	30	70	12 245 530.1	1 752 787.59	5 383 183.74	5 109 558.77
3	120	120	12 331 983.3	1 805 587.98	5 502 394.46	5 024 000.91

Tab.4

Tab.5

Tab.6

Fig.4

References 20

[1]	LIM J H, PARK D H. Optimal periodic preventive maintenance schedules with improvement factors depending on number of preventive maintenances[J]. Asia-Pacific Journal of Operational Research, 2007, 24(1): 111-124.
[2]	PONGPECH J, MURTHY D N P. Optimal periodic preventive maintenance policy for leased equipment[J]. Reliability Engineering & System Safety, 2006, 91(7): 772-777.
[3]	SHEU S H. A modified block replacement policy with two variables and general random minimal repair cost[J]. Journal of Applied Probability, 1996, 33(2): 557-572.
[4]	LU Y, SUN L, KANG J, et al. Opportunistic maintenance optimization for offshore wind turbine electrical and electronic system based on rolling horizon approach[J]. Journal of Renewable and Sustainable Energy, 2017, 9(3): 033307.
[5]	XIA T, XI L, ZHOU X, et al. Dynamic maintenance decision-making for series-parallel manufacturing system based on MAM-MTW methodology[J]. European Journal of Operational Research, 2012, 221(1): 231-240.
[6]	ZHOU X, LU B. Preventive maintenance scheduling for serial multi-station manufacturing systems with interaction between station reliability and product quality[J]. Computers & Industrial Engineering, 2018, 122: 283-291.
[7]	SONG S, LI Q, FELDER F A, et al. Integrated optimization of offshore wind farm layout design and turbine opportunistic condition-based maintenance[J]. Computers & Industrial Engineering, 2018, 120: 288-297.
[8]	DO P, ASSAF R, SCARF P, et al. Modelling and application of condition-based maintenance for a two-component system with stochastic and economic dependencies[J]. Reliability Engineering & System Safety, 2019, 182: 86-97.
[9]	COLLEDANI M, MAGNANINI M C, TOLIO T. Impact of opportunistic maintenance on manufacturing system performance[J]. CIRP Annals, 2018, 67(1): 499-502.
[10]	LU B, ZHOU X. Opportunistic preventive maintenance scheduling for serial-parallel multistage manufacturing systems with multiple streams of deterioration[J]. Reliability Engineering & System Safety, 2017, 168: 116-127.
[11]	俞梦琦, 史凯龙, 周晓军. 基于双时间窗的多设备串行系统机会维护策略[J]. 上海交通大学学报, 2020, 54(1): 69-75. doi: 10.16183/j.cnki.jsjtu.2020.01.009
	YU Mengqi, SHI Kailong, ZHOU Xiaojun. Opportunistic maintenance strategy for multi-unit serial systems based on dual time window[J]. Journal of Shanghai Jiao Tong University, 2020, 54(1): 69-75.
[12]	ZHOU X, NING X. Maintenance gravity window based opportunistic maintenance scheduling for multi-unit serial systems with stochastic production waits[J]. Reliability Engineering & System Safety, 2021, 215: 107828.
[13]	MOSHEIOV G, SARIG A, STRUSEVICH V A, et al. Two-machine flow shop and open shop scheduling problems with a single maintenance window[J]. European Journal of Operational Research, 2018, 271(2): 388-400.
[14]	YANG L, ZHAO Y, PENG R, MA X. Opportunistic maintenance of production systems subject to random wait time and multiple control limits[J]. Journal of Manufacturing Systems, 2018, 47(4): 12-34.
[15]	ZHANG C, GAO W, GUO S, et al. Opportunistic maintenance for wind turbines considering imperfect, reliability-based maintenance[J]. Renewable energy, 2017, 103: 606-612.
[16]	CAVALCA K L. Availability optimization with genetic algorithm[J]. International Journal of Quality & Reliability Management, 2003, 20(7): 847-863.
[17]	韩李杰. 考虑备件和维护人力资源的串联系统机会维护[D]. 上海: 上海交通大学, 2016.
	HAN Lijie. Opportunity maintenance of serial system considering spare parts and human resource[D]. Shanghai: Shanghai Jiao Tong University, 2016.
[18]	QIN W, ZHUANG Z, LIU Y, et al. Sustainable service oriented equipment maintenance management of steel enterprises using a two-stage optimization approach[J]. Robotics and Computer-Integrated Manufacturing, 2022, 75: 102311.
[19]	LUO M, WU S, SCARF P. Models of imperfect repair[M]//DE ALMEIDA A T, EKENBERG L, SCARF P, et al. Multicriteria and optimization models for risk, reliability, and maintenance decision analysis. Switzerland: Springer, 2022: 391-402.
[20]	BALAKRISHNAN N, KATERI M. On the maximum likelihood estimation of parameters of Weibull distribution based on complete and censored data[J]. Statistics & Probability Letters, 2008, 78(17): 2971-2975.

i	X_i/m
1	36
2	48
3	147.3
4	251
5	338.7
6	517

i	j	参与系统维护的时间/h
i	j	558	602	604	615	720	750	800	837	845	904	915
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3	2
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4	1					⚪
4	2
4	3					⚪
4	4					⚪
4	5										⚪
5	2			⚪
5	3			⚪
5	4							⚪
6	1					⚪					⚪
6	2		⚪
6	3		⚪					⚪

r^D	C/元
r^D	策略1	策略2	策略3
0.5	9 620 459.5	9 705 047.2	9 809 341.9
0.8	11 137 170.5	11 253 346.3	13 177 470.9
0.9	11 645 926.6	11 758 861.0	11 857 452.1
1.1	12 450 669.2	12 649 852.0	12 649 851.9
1.2	12 836 969.8	13 147 182.0	13 177 470.9
1.3	13 182 547.6	13 467 639.1	13 467 639.1
1.4	13 692 836.8	13 982 316.3	14 003 082.2
1.5	13 974 578.3	14 177 837.0	14 221 703.6
1.6	14 529 246.8	14 712 756.4	14 740 627.2
1.7	14 872 695.2	15 225 633.6	15 260 733.4
1.8	15 225 633.5	15 472 867.0	15 482 514.8
1.9	15 668 527.0	15 928 027.0	16 020 137.9
2.0	16 004 840.5	16 334 781.7	16 337 258.5
3.0	18 962 605.2	19 118 940.2	19 430 157.4
4.0	21 386 509.9	21 932 994.6	22 347 742.4

r^PM	C/元			r^MR	C/元
r^PM	策略1	策略2	策略3	r^MR	策略1	策略2	策略3
0.4	10 842 663	11 058 172	11 076 813	0.4	7 617 885	7 675 420	7 773 159
0.6	11 092 007	11 218 433	11 289 565	0.6	9 362 511	9 483 791	9 532 783
0.8	11 431 720	11 585 273	11 585 273	0.8	10 756 493	10 879 263	10 896 904
1.0	11 737 294	11 900 250	11 951 963	1.0	11 906 720	12 245 530	12 331 983
1.2	11 906 720	12 245 530	12 331 983	1.2	13 240 993	13 287 997	13 501 420
1.4	12 377 192	12 434 377	12 434 377	1.4	14 353 124	14 578 590	14 631 391
1.6	12 711 406	12 832 599	12 892 816	1.6	15 347 480	15 612 128	15 612 128
1.8	12 881 166	13 030 062	13 030 062	1.8	16 355 153	16 522 962	16 634 735
2.0	13 139 298	13 348 537	13 348 537	2.0	17 233 474	17 371 955	17 602 482