  Efficient dispatching rule in manufacturing industry is key to ensure product
on-time delivery and minimum past-due and inventory cost. Manufacturing,
especially in the developed world, is moving towards on-demand manufacturing
meaning a high mix, low volume product mix. This requires efficient dispatching
that can work in dynamic and stochastic environments, meaning it allows for
quick response to new orders received and can work over a disparate set of shop
floor settings. In this paper we address this problem of dispatching in
manufacturing. Using reinforcement learning (RL), we propose a new design to
formulate the shop floor state as a 2-D matrix, incorporate job slack time into
state representation, and design lateness and tardiness rewards function for
dispatching purpose. However, maintaining a separate RL model for each
production line on a manufacturing shop floor is costly and often infeasible.
To address this, we enhance our deep RL model with an approach for dispatching
policy transfer. This increases policy generalization and saves time and cost
for model training and data collection. Experiments show that: (1) our approach
performs the best in terms of total discounted reward and average lateness,
tardiness, (2) the proposed policy transfer approach reduces training time and
increases policy generalization.
