Lot Splitting in Stochastic Flow Shop and Job Shop Environments
通过仿真模型研究随机流水车间和作业车间中不同批量拆分策略对长期平均流程时间的影响，发现批量拆分比具体方法更重要。
This paper studies various lot splitting policies in stochastic job shop and flow shop settings with the objective of minimizing long-run mean flow time (MFT).Using a simulation model, we estimate MFT for each policy in stochastic, dynamic situations.When lot splitting is combined with repetitive lots priority, MFT decreases, but there are few differences between the exact lot splitting policy used.Thus, in stochastic, dynamic situations the use of lot splitting is more important than the exact method used.Methods which perform well in static, deterministic environments do not necessarily perform well in other scenarios.We conclude our analysis with a discussion of our findings in relation to flow dominance and JIT/kanban issues.