Collaborative Optimization Problem of Dynamic Pre-maintenance and Green Scheduling

doi:10.16182/j.issn1004731x.joss.23-1198

Abstract

Abstract:

For the traditional flexible job shop scheduling problem, a joint optimization of machine dynamic pre-maintenance and green scheduling is considered to establish an integrated optimization model with the optimization objectives of minimizing maximum completion time, total carbon emissions, and total cost. An improved NSGA-II algorithm is proposed to solve the model. A three-layer encoding method based on process, machine, and pre maintenance is adopted to design a one-step decoding scheme that considers process allocation, machine selection, and machine pre-maintenance strategies. The algorithm improves the elitist retention strategy, designs an adaptive crossover mutation function with algebraic changes, and a mutation operator based on neighborhood search. The experiment validates the effectiveness of the improved algorithm in solving scheduling problems of different scales, the proposed dynamic pre-maintenance strategy can more effectively solve the collaborative optimization problem of pre-maintenance and flexible job shop green scheduling compared to other maintenance strategies.

Key words: pre-maintenance, green scheduling, INSGA-II, collaborative optimization, flexible job shop

CLC Number:

TP391.9

Jiang Yuyan, Ma Ning, Li Yan, Gan Rumeijiang, Wang Fuyu. Collaborative Optimization Problem of Dynamic Pre-maintenance and Green Scheduling[J]. Journal of System Simulation, 2025, 37(2): 362-378.

Figures/Tables 28

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Table 1

Comparison of Brandimarte example results

算例	N×M	HGWO		Heuristic		IDE		ALPS-GA		INSGA-Ⅱ
算例	N×M	Best	$R P D$	Best	$R P D$	Best	$R P D$	Best	$R P D$	Best	$R P D$
平均值			3.26		4.41		2.18		5.29		0.93
MK01	10×6	40	0	42	5	40	0	40	0	40	0
MK02	10×6	29	3.57	28	0	28	0	29	3.57	28	0
MK03	15×8	204	0	204	0	204	0	207	1.47	204	0
MK04	15×8	65	0	75	15.38	71	9.23	65	0	66	1.54
MK05	15×4	175	0	179	2.29	179	2.29	181	3.43	175	0
MK06	10×15	79	14.49	69	0	73	5.8	81	17.39	72	4.35
MK07	20×5	149	2.05	149	2.05	146	0	149	2.05	146	0
MK08	20×10	523	0	555	6.12	528	0.96	555	6.12	523	0
MK09	20×10	325	4.84	342	10.32	321	3.55	342	10.32	310	0
MK10	20×15	253	7.66	242	2.98	235	0	255	8.51	243	3.4

Table 1

Fig. 13

Fig. 14

Table 2

Fig. 15

Table 3

Machine processing parameters

机器	$p k e$ /kW	$i k e$ /kW	$C k r$ /(元/h)
$M 1$	15	2.35	18
$M 2$	20	3.75	22
$M 3$	5	0.65	7
$M 4$	13	1.63	9
$M 5$	10	1.31	8
$M 6$	8.5	0.93	11
$M 7$	6.5	0.82	5
$M 8$	11	1.36	13
$M 9$	20	3.45	16
$M 10$	15	2.82	12

Table 3

Table 4

Comparison results of extended examples

算例	NSGA-Ⅱ				MOEA/D				本文算法
算例	$f 1$	$f 2$	$f 3$	加权值	$f 1$	$f 2$	$f 3$	加权值	$f 1$	$f 2$	$f 3$	加权值
Date-1	26	23.26	16.23	21.98	16	17.36	13.67	15.84	15	16.03	13.38	14.93
Date-2	10	10.59	7.85	9.59	9	9.41	7.33	8.66	8	9.18	7.75	8.40
Date-3	15	18.85	17.33	17.24	18	20.35	17.22	18.70	14	18.29	17.32	16.71
Date-4	35	35.18	29.80	33.51	32	34.47	30.40	32.51	30	32.98	29.35	31.00
Date-5	208	202.59	158.72	191.05	206	203.92	154.97	189.86	204	197.59	155.57	186.91
Date-6	337	506.41	458.48	441.21	344	498.38	456.92	439.63	332	492.84	457.9	434.11

Table 4

Fig. 16

Table 5

Fig. 17

Fig. 18

Table 6

Machine pre-maintenance related parameters

机器	$η$	$m$	$C k f$ /(元/h)	$周期 / m i n$	$T k v p m / m i n$
1	100	1.6	110	32	7
2	105	1.6	150	33	6
3	102	1.6	105	32	6
4	140	1.8	65	35	8
5	110	1.4	160	30	5
6	95	1.6	70	30	5
7	90	2.0	60	32	7
8	90	1.8	80	32	8

Table 6

Table 7

Comparison of results of different maintenance strategies

维护策略	$f 1 / m i n$	$f 2 / k g$	$f 3 / 元$	PM次数	PM成本/元
动态维护	82	98.7	177.57	9	91.33
右移维护	94	102.57	188.83	10	104.67
周期维护	93	103.59	253.9	16	167.67

Table 7

Fig. 19

Fig. 20

Fig. 21

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算例	SC(A,B)	SC(B,A)	SC(A,C)	SC(C,A)	IGD(A)	IGD(B)	IGD(C)
Date-1	0.25	0.067	0.667	0.067	0.138	1.532	1.010
Date-2	0.714	0	1	0	0.029	0.605	2.009
Date-3	0.8	0	0.429	0.231	0.298	1.284	1.069
Date-4	0.25	0	1	0	0.117	1.310	1.837
Date-5	1	0	0.714	0	0.607	2.669	1.081
Date-6	0.786	0	0.75	0	1.096	5.273	3.195

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