Integrated Scheduling Algorithm of Complex Product with No-wait Constraint Based on Virtual Component

被引：7

作者：

Guo W. ^{[1
]}

Lei Q. ^{[1
]}

Song Y. ^{[1
]}

Lü X. ^{[1
]}

Li L. ^{[1
]}

机构：

[1] State Key Laboratory of Mechanical Transmission, Chongqing University, Chongqing

来源：

Jixie Gongcheng Xuebao/Journal of Mechanical Engineering | 2020年 / 56卷 / 04期

关键词：

Genetic algorithm; Integrated scheduling; No-wait constraint; Virtual component;

D O I：

10.3901/JME.2020.04.246

中图分类号：

学科分类号：

摘要：

For the complex product scheduling problem with no-wait constraint between operations in the actual production, an integrated scheduling algorithm based on design structure matrix and genetic algorithm is proposed. Based on the concepts of no-wait virtual component, wait virtual component, furcated virtual component and child virtual component, a more effective encoding based on lower triangular design structure matrix of digital virtual component is designed, which not only satisfies the sequence constraints of complex product processing and assembly, but also reflects the no-wait constraint between operations. Feasible crossover and mutation methods are designed and avoid the transformation work of infeasible offspring individuals. A decoding method which can meet the no-wait constraint between operations is also presented, and ensures that chromosomes are decoded into active schedules. Experimental results show that the proposed integrated scheduling algorithm has good solution speed and quality for complex product scheduling problem with no-wait constraint between operations. © 2020 Journal of Mechanical Engineering.

引用

页码：246 / 257

页数：11

共 32 条

[1]

Zhang C., Sun J., Yang Q., Et al., A hybrid algorithm for flowshop scheduling problem, Acta Automatica Sinica, 3, 3, pp. 332-336, (2009)

[2]

Lei D., Novel teaching-learning-based optimization algorithm for low carbon scheduling of flexible job shop, Control and Decision, 32, 9, pp. 1621-1627, (2017)

[3]

Shao W., Pi D., Shao Z., A hybrid discrete teaching-learning based meta-heuristic for solving no-idle flow shop scheduling problem with total tardiness criterion, Computers and Operations Research, 94, pp. 89-105, (2018)

[4]

Jiang T., Flexible job shop scheduling problem with hybrid grey wolf optimization algorithm, Control and Decision, 33, 3, pp. 503-508, (2018)

[5]

Qin W., Zhang J., Song D., An improved ant colony algorithm for dynamic hybrid flow shop scheduling with uncertain processing time, Journal of Intelligent Manufacturing, 29, 4, (2018)

[6]

Gong G., Deng Q., Gong X., Et al., A new double flexible job-shop scheduling problem integrating processing time, green production, and human factor indicators, Journal of Cleaner Production, 174, pp. 560-576, (2018)

[7]

Mou J., Guo Q., Gao L., Et al., Multi-objective genetic algorithm for solving multi- objective flow-shop inverse scheduling problems, Journal of Mechanical Engineering, 52, 22, pp. 186-197, (2016)

[8]

Nouiri M., Bekrar A., Jemai A., Et al., An effective and distributed particle swarm optimization algorithm for flexible job-shop scheduling problem, Journal of Intelligent Manufacturing, 29, 3, pp. 603-615, (2018)

[9]

Xie Z., Study on operation scheduling of complex product with constraint among jobs, (2009)

[10]

Xie Z., Liu S., Qiao P., Dynamic job-shop scheduling algorithm based on ACPM and BFSM, Journal of Computer Research and Development, 40, 7, pp. 977-983, (2003)

← 1 2 3 4 →