IEEE Trans Cybern. 2019 Jan;49(1):184-197. doi: 10.1109/TCYB.2017.2771213. Epub 2017 Nov 17.
In various flow shop scheduling problems, it is very common that a machine suffers from breakdowns. Under this situation, a robust and stable suboptimal scheduling solution is of more practical interest than a global optimal solution that is sensitive to environmental changes. However, blocking lot-streaming flow shop (BLSFS) scheduling problems with machine breakdowns have not yet been well studied up to date. This paper presents, for the first time, a multiobjective model of the above problem including robustness and stability criteria. Based on this model, an evolutionary multiobjective robust scheduling algorithm is suggested, in which solutions obtained by a variant of single-objective heuristic are incorporated into population initialization and two novel crossover operators are proposed to take advantage of nondominated solutions. In addition, a rescheduling strategy based on the local search is presented to further reduce the negative influence resulted from machine breakdowns.The proposed algorithm is applied to 22 test sets, and compared with the state-of-the-art algorithms without machine breakdowns. Our empirical results demonstrate that the proposed algorithm can effectively tackle BLSFS scheduling problems in the presence of machine breakdowns by obtaining scheduling strategies that are robust and stable.
在各种流水车间调度问题中，机器故障是很常见的。在这种情况下，稳健且稳定的次优调度解决方案比对环境变化敏感的全局最优解决方案更具实际意义。然而，到目前为止，具有机器故障的阻塞批量流车间调度问题还没有得到很好的研究。本文首次提出了一个包括稳健性和稳定性标准的上述问题的多目标模型。基于该模型，提出了一种进化多目标稳健调度算法，其中通过单目标启发式的变体获得的解决方案被合并到种群初始化中，并提出了两个新的交叉算子来利用非支配解。此外，还提出了一种基于局部搜索的重调度策略，以进一步降低机器故障造成的负面影响。将所提出的算法应用于 22 个测试集，并与没有机器故障的最新算法进行比较。我们的实验结果表明，所提出的算法可以通过获得稳健和稳定的调度策略来有效地解决存在机器故障的 BLSFS 调度问题。
