Simultaneous machine selection and buffer allocation in large unbalanced series-parallel production lines
Simultaneous machine selection and buffer allocation in large unbalanced series-parallel production lines
复制标题
大型不平衡串并联生产线同时机器选择和缓冲区分配
DOI：
10.1080/00207543.2021.1884306
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发表时间：
2021-03
影响因子：
9.2
通讯作者：
Yu Ai-lin
中科院分区：
文献类型：
--
作者：
Xi Shao-hui;James MacGregor Smith;Chen Qing-xin;Mao Ning;Zhang Hui-yu;Yu Ai-lin
大型串并联生产线中机器和缓冲器的同时优化是一个NP难问题。本研究中所建立的优化模型是通过优化机器类型、并行机数量和缓冲容量，在满足期望的吞吐速率和周期时间的条件下，最小化总投资成本。为了解决这类设计问题，提出了一种分解-协调方法来高效、准确地生成大型生产线的分配方案。该方法包括两个迭代过程：分解过程将原始线路解耦为多条小线路并分别对其进行优化，协调过程确保分解后的线路的优化问题与原始线路的相应部分相似。通过数值实验，与模拟退火法和非支配排序遗传算法II进行了比较，验证了该方法的有效性。最后，通过一组数值结果和多因素实验分析，说明了目标系统参数对资源配置的影响。
ABSTRACT Simultaneous optimisation of machines and buffers in a large series-parallel production line is an NP-hard problem. The formulated optimisation model in this study is used to minimise the total investment cost subject to the desired throughput rate and cycle time by optimising the machine types, number of parallel machines, and buffer capacities. To solve this kind of design problem, a decomposition-coordination method is proposed to efficiently and accurately generate allocation solutions for large production lines. The proposed method includes two iterative processes: the decomposition process decouples the original line into several small lines and optimises them separately, while the coordination process ensures that the optimisation problems of the decomposed lines are similar to the corresponding part of the original. The performance of this approach is demonstrated through numerical experiments by comparisons with the simulated annealing algorithm and non-dominated sorting genetic algorithm-II. Finally, the sets of numerical results and a multi-factorial experimental analysis illustrate the influences of target system parameters on the resource configurations.
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影响因子：
9.2
作者：
Colledani, M.;Ekvall, M.;Tolio, T.
通讯作者：
Tolio, T.
影响因子：
2.6
作者：
Smith, James MacGregor
通讯作者：
Smith, James MacGregor
影响因子：
--
作者：
Smith, JM;Cruz, FRB
通讯作者：
Cruz, FRB
影响因子：
2.7
作者：
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通讯作者：
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影响因子：
2.7
作者：
J. Smith;Ryan Barnes
通讯作者：
J. Smith;Ryan Barnes