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 . 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 dispatch 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 (2) the proposed policy

Links: PDF - Abstract

Code :


Keywords : manufacturing - dispatching - model - policy - time -

Leave a Reply

Your email address will not be published. Required fields are marked *


Enjoy this blog? Please spread the word :)