2 research outputs found
Manufacturing Dispatching using Reinforcement and Transfer Learning
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.Comment: ECML PKDD 2019 (The European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases, 2019
Generative Adversarial Networks for Failure Prediction
Prognostics and Health Management (PHM) is an emerging engineering discipline
which is concerned with the analysis and prediction of equipment health and
performance. One of the key challenges in PHM is to accurately predict
impending failures in the equipment. In recent years, solutions for failure
prediction have evolved from building complex physical models to the use of
machine learning algorithms that leverage the data generated by the equipment.
However, failure prediction problems pose a set of unique challenges that make
direct application of traditional classification and prediction algorithms
impractical. These challenges include the highly imbalanced training data, the
extremely high cost of collecting more failure samples, and the complexity of
the failure patterns. Traditional oversampling techniques will not be able to
capture such complexity and accordingly result in overfitting the training
data. This paper addresses these challenges by proposing a novel algorithm for
failure prediction using Generative Adversarial Networks (GAN-FP). GAN-FP first
utilizes two GAN networks to simultaneously generate training samples and build
an inference network that can be used to predict failures for new samples.
GAN-FP first adopts an infoGAN to generate realistic failure and non-failure
samples, and initialize the weights of the first few layers of the inference
network. The inference network is then tuned by optimizing a weighted loss
objective using only real failure and non-failure samples. The inference
network is further tuned using a second GAN whose purpose is to guarantee the
consistency between the generated samples and corresponding labels. GAN-FP can
be used for other imbalanced classification problems as well.Comment: ECML PKDD 2019 (The European Conference on Machine Learning and
Principles and Practice of Knowledge Discovery in Databases, 2019