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首页> 外文期刊>Simulation modelling practice and theory: International journal of the Federation of European Simulation Societies >Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing
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Simulation optimization approach for hybrid flow shop scheduling problem in semiconductor back-end manufacturing

机译：半导体后端制造中混合流水车间调度问题的仿真优化方法

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This study presents a simulation optimization approach for a hybrid flow shop scheduling problem in a real-world semiconductor back-end assembly facility. The complexity of the problem is determined based on demand and supply characteristics. Demand varies with orders characterized by different quantities, product types, and release times. Supply varies with the number of flexible manufacturing routes but is constrained in a multi-line/multi-stage production system that contains certain types and numbers of identical and unrelated parallel machines. An order is typically split into separate jobs for parallel processing and subsequently merged for completion to reduce flow time. Split jobs that apply the same qualified machine type per order are compiled for quality and traceability. The objective is to achieve the feasible minimal flow time by determining the optimal assignment of the production line and machine type at each stage for each order. A simulation optimization approach is adopted due to the complex and stochastic nature of the problem. The approach includes a simulation model for performance evaluation, an optimization strategy with application of a genetic algorithm, and an acceleration technique via an optimal computing budget allocation. Furthermore, scenario analyses of the different levels of demand, product mix, and lot sizing are performed to reveal the advantage of simulation. This study demonstrates the value of the simulation optimization approach for practical applications and provides directions for future research on the stochastic hybrid flow shop scheduling problem. (C) 2014 Published by Elsevier B.V.
机译：这项研究提出了一种仿真优化方法，用于解决现实世界中的半导体后端组装设施中的混合流水车间调度问题。问题的复杂性取决于需求和供应特征。需求随定额，产品类型和发布时间不同而定的订单而变化。供应量随柔性制造路线的数量而变化，但受多线/多阶段生产系统的约束，该系统包含某些类型和数量的相同和不相关的并行机。通常将订单拆分为多个单独的作业以进行并行处理，然后合并以完成以减少流程时间。对每个订单应用相同合格机器类型的拆分作业进行编译，以确保质量和可追溯性。目的是通过确定每个订单的每个阶段的生产线和机器类型的最佳分配来实现可行的最小流动时间。由于问题的复杂性和随机性，因此采用了仿真优化方法。该方法包括用于性能评估的仿真模型，应用遗传算法的优化策略以及通过最佳计算预算分配的加速技术。此外，对不同级别的需求，产品组合和批次大小进行了情景分析，以揭示仿真的优势。这项研究证明了仿真优化方法在实际应用中的价值，并为未来关于随机混合流水车间调度问题的研究提供了方向。 （C）2014由Elsevier B.V.发布

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