Lot streaming in the presence of learning in sublot-attached setup times and processing times
研究在两机流水车间中，当每个子批次有附带准备时间且存在学习效应时，如何通过批次流技术确定最优子批次数量和大小以最小化完工时间。
Lot streaming is the process of splitting a production lot into sublots, and then processing the sublots on different machines in an overlapping manner. In this paper, we study the use of lot streaming for processing a lot in a two-machine flow shop when a sublot-attached setup time is incurred before the processing of each sublot. The objective is to determine number of sublots and sublot sizes and minimize makespan. We also consider the case when the effect of learning is observed in processing times, sublot-attached setup times, or, both. We present closed-form expressions for optimal sublot sizes and efficient search schemes to determine optimal number of sublots.