Scheduling policy and performance analysis of multi-product two-stage serial lines with batch machines

被引:0
作者
Yan, Fei-Yi [1 ]
Wang, Jun-Qiang [2 ,3 ]
机构
[1] School of Economics and Management, Nanjing Tech University, Nanjing
[2] Performance Analysis Center of Production and Operations Systems, Northwestern Polytechnical University, Xi’an
[3] School of Mechanical Engineering, Northwestern Polytechnical University, Xi’an
来源
Kongzhi yu Juece/Control and Decision | 2024年 / 39卷 / 11期
关键词
batch machines; multi-product; performance analysis; scheduling policy; serial lines;
D O I
10.13195/j.kzyjc.2023.0931
中图分类号
学科分类号
摘要
Aiming at multi-product two-stage serial lines with batch machines, the impact of different scheduling policies on system performance is studied, performance indicators such as productivity and work-in-process level are analyzed analytically. First, the system state space is described for four different scheduling policies, namely highest upstream machine efficiency policy, highest downstream machine efficiency policy, longest queue policy, and cyclic policy. The analytical model is established based on Markov process to quantify the performance indicators such as productivity. Then, considering the relationship between buffer capacity and batch capacity, the impact of four scheduling policies on system performance is compared through numerical experiments. The results show that when buffer capacity and batch capacity are independent, the cyclic policy has better performance than the other three policies. However, when there is a positive correlation between buffer capacity and batch capacity, the longest queue priority policy has the best performance. © 2024 Northeast University. All rights reserved.
引用
收藏
页码:3791 / 3800
页数:9
相关论文
共 24 条
[1]  
Wang J Q, Yan F Y, Cui P H, Et al., Bernoulli serial lines with batching machines: Performance analysis and system-theoretic properties, ⅠⅠSE Transactions, 51, 6, pp. 1-33, (2018)
[2]  
Tu J C, Zhang L., Performance analysis and optimisation of Bernoulli serial production lines with dynamic real-time bottleneck identification and mitigation, Ⅰnternational Journal of Production Research, 60, 13, pp. 3989-4005, (2022)
[3]  
Papadopoulos C T, Li J S, O'Kelly M E J., A classification and review of timed Markov models of manufacturing systems, Computers& Ⅰndustrial Engineering, 128, pp. 219-244, (2019)
[4]  
Pei Z, Wang Y J, Yang P Q, Et al., Transient analysis of the soft robotic clip production line with energy consumption constraint, Computer Ⅰntegrated Manufacturing Systems, 29, 5, pp. 1491-1505, (2023)
[5]  
Huang L Z, Jia Z Y, Wang Z J, Et al., Distributed flexible systems with degenerate machines: Performance analysis, production scheduling, and predictive maintenance, Control and Decision, 38, 9, pp. 2641-2652, (2023)
[6]  
Cui P H, Wang J Q, Zhang W P, Et al., Predictive maintenance decision-making for serial production lines based on deep reinforcement learning, Computer Ⅰntegrated Manufacturing Systems, 27, 12, pp. 3416-3428, (2021)
[7]  
Dasci A, Karakul M., Performance evaluation of a single-stage two-product manufacturing system operating under pull-type control, Computers & Operations Research, 35, 9, pp. 2861-2876, (2008)
[8]  
Feng W, Zheng L, Li J S., Scheduling policies in multi-product manufacturing systems with sequence-dependent setup times, Proceedings of the Winter Simulation Conference, pp. 2055-2066, (2011)
[9]  
Feng W, Zheng L, Li J S., The robustness of scheduling policies in multi-product manufacturing systems with sequence-dependent setup times and finite buffers, Computers & Ⅰndustrial Engineering, 63, 4, pp. 1145-1153, (2012)
[10]  
Kang N X, Zheng L, Li J S., Analysis of multi-product manufacturing systems with arbitrary processing times, Ⅰnternational Journal of Production Research, 53, 3, pp. 983-1001, (2015)