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Local Search and Tabu Search Algorithms for Machine Scheduling of a Hybrid Flow Shop Under Uncertainty

机译：本地搜索和禁忌搜索算法，用于在不确定度下机器调度

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In production systems, scheduling problems need to be solved under complex environmental conditions. In this paper, we present a comprehensive scheduling approach that is applicable in real industrial environments. To cope with the parameter uncertainty of real world problems, forecasting, classification, and simulation techniques are combined with heuristic optimization algorithms. Thus, the approach allows for identifying and including demand fluctuations and scrap rates, and offers a selection of suitable schedules depending on particular demand constellations in scheduling. Furthermore, we adapt seven optimization algorithms for two-stage hybrid flow shops with unrelated machines, machine qualifications, and skipping stages with the objective to minimize the makespan. The combination of methods is validated on a real production case of the automobile industry. The paper shows for the application case that metaheuristics provide significantly better results than SPT and safety factors, above a certain size, can reduce their effect preventing incomplete demand positions.
机译：在生产系统中，需要在复杂的环境条件下解决调度问题。在本文中，我们提出了一种适用于实际工业环境的全面调度方法。为了应对现实世界问题的参数不确定性，预测，分类和仿真技术与启发式优化算法相结合。因此，该方法允许识别和包括需求波动和废料速率，并且根据调度时的特定需求星座提供适当的时间表。此外，我们采用七种优化算法，采用无关的机器，机器资格和跳闸阶段的两级混合流量店，目的是最大限度地减少MEPESPAN。在汽车行业的实际生产案例上验证了方法的组合。本文展示了施用案例，即成形机会提供比SPT和安全因素更好的结果，高于一定尺寸，可以降低其效果，防止不完全的需求位​​置。

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