Joint optimization of maintenance, buffers and machines in manufacturing lines
Joint optimization of maintenance, buffers and machines in manufacturing lines
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DOI：
10.1080/0305215x.2017.1299716
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发表时间：
2018-01
影响因子：
2.7
通讯作者：
N. Nahas;M. Nourelfath
中科院分区：
文献类型：
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作者：
N. Nahas;M. Nourelfath
摘要本文考虑由若干台机器组成的串联生产线，中间由有限容量的中间缓冲区隔开。我们的目标是找到最佳数量的预防性维护行动，每台机器上进行，机器的最佳选择和最佳的缓冲区分配计划，最大限度地减少总系统成本，同时提供所需的系统吞吐量水平。当应用定期预防性维护时，假设所有机器的平均故障间隔时间增加。为了估计生产线的吞吐量，使用分解方法。在制定的最优设计问题的决策变量是缓冲水平，类型的机器和预防性维护行动之间的时间。三个启发式方法来解决制定的组合优化问题。第一个启发式由遗传算法，第二个是基于非线性阈值接受元启发式和第三个是一个蚁群系统。通过几个数值例子比较了所提出的算法，并证明了它们的有效性。结果表明，非线性阈值接受算法的性能优于遗传算法和蚁群算法，而对于较长的生产线，遗传算法的性能优于蚁群算法。
ABSTRACT This article considers a series manufacturing line composed of several machines separated by intermediate buffers of finite capacity. The goal is to find the optimal number of preventive maintenance actions performed on each machine, the optimal selection of machines and the optimal buffer allocation plan that minimize the total system cost, while providing the desired system throughput level. The mean times between failures of all machines are assumed to increase when applying periodic preventive maintenance. To estimate the production line throughput, a decomposition method is used. The decision variables in the formulated optimal design problem are buffer levels, types of machines and times between preventive maintenance actions. Three heuristic approaches are developed to solve the formulated combinatorial optimization problem. The first heuristic consists of a genetic algorithm, the second is based on the nonlinear threshold accepting metaheuristic and the third is an ant colony system. The proposed heuristics are compared and their efficiency is shown through several numerical examples. It is found that the nonlinear threshold accepting algorithm outperforms the genetic algorithm and ant colony system, while the genetic algorithm provides better results than the ant colony system for longer manufacturing lines.