Smart manufacturing is an effective way to improve the efficiency of resource utilization and reduce the response time of making joint decisions for the enterprises. Though, with the globalization of manufacturing enterprises, manufacturing optimization problems often occur in complex manufacturing systems under the deteriorating and fuzzy environment, which brings many challenges to smart manufacturing, such as the lack of coordinating scheduling strategies to guarantee the low latency requirement. This paper investigates a robust parallel-batching scheduling problem with fuzzy processing time and past-sequence-dependent delivery time. Some structural properties are first identified, and an optimal algorithm is further developed for the single-machine scheduling problem. Then, the problem is proved to be NP-hard. We thus design a hybrid Multi-Verse Optimizer-Variable Neighborhood Search algorithm to solve the investigated problem in a reasonable time. Abundant experiments of different scales are conducted to verify the performance of the proposed hybrid method with a comparison of the state-of-the-art methods. The proposed hybrid meta-heuristic shows excellent results, robustness, and computational time performance under various experiments.

中文翻译：

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智能制造中具有模糊恶化的处理时间和可变的交付时间的鲁棒并行批处理调度

智能制造是提高资源利用效率，减少企业联合决策响应时间的有效途径。尽管随着制造企业的全球化，在不断恶化和模糊的环境下，复杂的制造系统中经常会出现制造优化问题，这给智能制造带来了许多挑战，例如缺乏协调调度策略来保证低时延要求。本文研究了具有模糊处理时间和过去序列依赖的交付时间的鲁棒并行批处理调度问题。首先确定一些结构特性，然后针对单机调度问题进一步开发最佳算法。然后，证明该问题是NP难的。因此，我们设计了一种混合的多词优化器-变量邻域搜索算法，以在合理的时间内解决所研究的问题。进行了不同规模的大量实验，以验证所提出的混合方法的性能，并与最新方法进行比较。所提出的混合元启发式算法在各种实验下均显示出优异的结果，鲁棒性和计算时间性能。