Manufacturing Dispatching using Reinforcement and Transfer Learning

by   Shuai Zheng, et al.

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.


page 1

page 2

page 3

page 4


Reward Learning for Efficient Reinforcement Learning in Extractive Document Summarisation

Document summarisation can be formulated as a sequential decision-making...

Combined Peak Reduction and Self-Consumption Using Proximal Policy Optimization

Residential demand response programs aim to activate demand flexibility ...

Representation Learning for Continuous Action Spaces is Beneficial for Efficient Policy Learning

Deep reinforcement learning (DRL) breaks through the bottlenecks of trad...

Defining Admissible Rewards for High Confidence Policy Evaluation

A key impediment to reinforcement learning (RL) in real applications wit...

Semiconductor Fab Scheduling with Self-Supervised and Reinforcement Learning

Semiconductor manufacturing is a notoriously complex and costly multi-st...

Similarity-based transfer learning of decision policies

A problem of learning decision policy from past experience is considered...

A structured approach for the implementation of distributed manufacturing simulation

Manufacturing has been changing from a mainly inhouse effort to a distri...

Please sign up or login with your details

Forgot password? Click here to reset