Setting up the RL-DL-CRLMM model
This section describes how to set up the previous chapter's model for this project and add a few functions.
In Chapter 11, Combining Reinforcement Learning and Deep Learning, the RL-DL-CRLMM model analyzed webcam images of pieces of cut cloth to be sewed in real-time on a conveyor belt. The goal was to determine if they contained a gap (not too many pieces to sew) or not (a lot of pieces to sew). Then the model selected the best sewing station. A sewing station with a lot of work to do is best optimized with a small number of pieces to sew. A sewing station with little work to do will be best optimized with a large number of pieces to sew. By doing this, the RL-DL-CRLMM optimized the load on each sewing station, as shown in the following diagram:
Figure 12.1: Apparel production flow
This leads to the following circular optimizing model:
Figure 12.2: Circular RL-DL-CRLMM
This RL-DL-CRLMM model that we explored...
