Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems
Robustness measures and robust scheduling for multi-objective stochastic flexible job shop scheduling problems
复制标题
多目标随机柔性作业车间调度问题的鲁棒性测度与鲁棒调度
DOI：
10.1007/s00500-016-2245-4
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发表时间：
2017-11
期刊：
影响因子：
4.1
通讯作者：
Shen XN
中科院分区：
文献类型：
--
作者：
Shen Xiao-Ning;Han Ying;Fu Jing-Zhi;Shen XN
不确定环境下的柔性作业车间调度问题是现实制造系统中的一个重要问题。针对柔性作业车间调度问题的不确定性和多目标性，建立了一个多目标随机柔性作业车间调度问题的数学模型，该模型在多种实际约束条件下同时考虑了制造周期、最大机器负荷和对不确定性的鲁棒性3个目标.基于统计工具定义了两个新的基于神经网络的鲁棒性度量。为了更好地求解该问题，提出了一种改进的基于分解的多目标进化算法（m-MOEA/D）。我们的方法的新奇在于，它采用了一种新的子问题更新方法，该方法利用了全局信息，允许保存在档案中的精英参与儿童一代，采用了子问题选择和暂停策略，将更多的计算工作集中在有前途的子问题上，并结合了问题特定的遗传算子的变化。在18个问题实例（包括8个完全柔性和10个部分柔性实例）上的实验结果表明，这两种新的鲁棒性度量方法在提高调度对不确定性的鲁棒性和保持目标值扰动方差较小方面比现有的鲁棒性度量方法更有效.与现有的多目标优化进化算法（MOEAs）相比，本文提出的基于m-MOEA/D的鲁棒调度方法具有更好的收敛性能。不同的权衡之间的三个目标进行了分析。
Flexible job shop scheduling in uncertain environments plays an important part in real-world manufacturing systems. With the aim of capturing the uncertain and multi-objective nature of flexible job shop scheduling, a mathematical model for the multi-objective stochastic flexible job shop scheduling problem (MOSFJSSP) is constructed, where three objectives of make-span, maximal machine workload, and robustness to uncertainties are considered simultaneously under a variety of practical constraints. Two new scenario-based robustness measures are defined based on statistical tools. To solve MOSFJSSP appropriately, a modified multi-objective evolutionary algorithm based on decomposition (m-MOEA/D) is developed for robust scheduling. The novelty of our approach is that it adopts a new subproblem update method which exploits the global information, allows the elitists kept in an archive to participate in the child generation, employs a subproblem selection and suspension strategy to focus more computational efforts on promising subproblems, and incorporates problem-specific genetic operators for variation. Extensive experimental results on 18 problem instances, including 8 total flexible and 10 partial flexible instances, show that the two new robustness measures are more effective than the existing scenario-based measures, in improving the schedule robustness to uncertainties and maintaining a small variance of disrupted objective values. Compared to the state-of-the-art multi-objective optimization evolutionary algorithms (MOEAs), our proposed m-MOEA/D-based robust scheduling approach achieves a much better convergence performance. Different trade-offs among the three objectives are also analyzed.
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DOI：
--
发表时间：
1996
期刊：
--
影响因子：
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作者：
D. Mossé;Sunondo Ghosh
通讯作者：
D. Mossé;Sunondo Ghosh
DOI：
10.1109/ieem.2009.5373216
发表时间：
2009-12
期刊：
2009 IEEE International Conference on Industrial Engineering and Engineering Management
影响因子：
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作者：
Saif Ullah;Qiong Liu;Chaoyong Zhang;Y. R. Awan
通讯作者：
Saif Ullah;Qiong Liu;Chaoyong Zhang;Y. R. Awan
影响因子：
12
作者：
M. Al-Fawzan;M. Haouari
通讯作者：
M. Al-Fawzan;M. Haouari
DOI：
10.1016/j.cirpj.2009.10.001
发表时间：
2010-01-01
影响因子：
4.8
作者：
Fattahi, Parviz;Fallahi, Alireza
通讯作者：
Fallahi, Alireza
DOI：
10.5772/13195
发表时间：
2011-01
期刊：
--
影响因子：
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作者：
Guojun Zhang;Haiping Zhu;Chaoyong Zhang
通讯作者：
Guojun Zhang;Haiping Zhu;Chaoyong Zhang