Simulation Optimization on Joint Production and Preventive Maintenance Scheduling for Distributed Job-shop

doi:10.16182/j.issn1004731x.joss.21-1288

Abstract

Abstract:

Distributed job-shop production scheduling is the key to high efficient production. Preventive maintenance, an essential means to ensure the safety and reliability of equipment, should be the necessary content of the production decision-making. Aiming at the production delay caused by equipment maintenance, a simulation-corrected optimization method is proposed. The mathematical model and simulation model for the joint production and preventive maintenance scheduling are established. The sequence exchange-based genetic algorithm is combined with the simulation-corrected optimization method to form a fast simulation optimization scheme. Experimental results on typical cases show that the proposed simulation optimization method can improve the solution quality and robustness for the production process with uncertainty.

Key words: distributed job-shop, task scheduling, preventive maintenance, simulation optimization, evolutionary computation

CLC Number:

TP391.9

Fei Ye, Ziqing Li, Yuanjun Laili. Simulation Optimization on Joint Production and Preventive Maintenance Scheduling for Distributed Job-shop[J]. Journal of System Simulation, 2022, 34(4): 688-699.

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	O₁	O₂	O₃	O₄	O₅	O₆
J₁	3	1	2	4	6	5
J₂	2	3	5	6	1	4
J₃	3	4	6	1	2	5
J₄	2	1	3	4	5	6
J₅	3	2	5	6	1	4
J₆	2	4	6	1	5	3

	P₁	P₂	P₃	P₄	P₅	P₆
M₁	1	4	3	6	2	5
M₂	2	4	6	5	1	3
M₃	1	3	2	5	4	6
M₄	3	6	4	1	2	5
M₅	2	5	4	3	6	1
M₆	3	6	2	5	1	4

设备可靠性	设备平均寿命/h
[0.9，1.0]	393.6
[0.8，0.9)	326.6
[0.7，0.8)	266.8
[0.6，0.7)	215.3
[0.5，0.6)	170.4
[0.4，0.5)	131.0
[0.3，0.4)	96.0
[0.2，0.3)	64.9
[0.1，0.2)	36.9
[0，0.1)	11.7

验证次数	种群代数	算法完工时间	模型完工时间
1	4	563	589
2	98	472	496
3	213	461	496
4	281	440	496
5	323	427	453
6	416	427	453
7	486	427	453
8	533	427	453
9	704	427	453
10	783	427	453
11	1 000	427	453

验证次数	种群代数	算法完工时间	模型完工时间
1	4	560	570
2	98	443	458
3	213	406	412
4	281	406	416
5	323	406	416
6	416	406	427
7	486	406	416
8	533	406	421
9	704	406	421
10	783	406	421
11	1 000	406	416