TY  - JOUR
A1  - Panzer, Marcel
A1  - Bender, Benedict
A1  - Gronau, Norbert
T1  - A deep reinforcement learning based hyper-heuristic for modular production control
JF  - International journal of production research
N2  - In nowadays production, fluctuations in demand, shortening product life-cycles, and highly configurable products require an adaptive and robust control approach to maintain competitiveness. This approach must not only optimise desired production objectives but also cope with unforeseen machine failures, rush orders, and changes in short-term demand. Previous control approaches were often implemented using a single operations layer and a standalone deep learning approach, which may not adequately address the complex organisational demands of modern manufacturing systems. To address this challenge, we propose a hyper-heuristics control model within a semi-heterarchical production system, in which multiple manufacturing and distribution agents are spread across pre-defined modules. The agents employ a deep reinforcement learning algorithm to learn a policy for selecting low-level heuristics in a situation-specific manner, thereby leveraging system performance and adaptability. We tested our approach in simulation and transferred it to a hybrid production environment. By that, we were able to demonstrate its multi-objective optimisation capabilities compared to conventional approaches in terms of mean throughput time, tardiness, and processing of prioritised orders in a multi-layered production system. The modular design is promising in reducing the overall system complexity and facilitates a quick and seamless integration into other scenarios.
KW  - production control
KW  - modular production
KW  - multi-agent system
KW  - deep reinforcement learning
KW  - deep learning
KW  - multi-objective optimisation
Y1  - 2023
U6  - https://doi.org/10.1080/00207543.2023.2233641
SN  - 0020-7543
SN  - 1366-588X
SN  - 0278-6125
SP  - 1
EP  - 22
PB  - Taylor & Francis
CY  - London
ER  - 
TY  - JOUR
A1  - Wilksch, Moritz
A1  - Abramova, Olga
T1  - PyFin-sentiment
BT  - towards a machine-learning-based model for deriving sentiment from financial tweets
JF  - International journal of information management data insights
N2  - Responding to the poor performance of generic automated sentiment analysis solutions on domain-specific texts, we collect a dataset of 10,000 tweets discussing the topics of finance and investing. We manually assign each tweet its market sentiment, i.e., the investor’s anticipation of a stock’s future return. Using this data, we show that all existing sentiment models trained on adjacent domains struggle with accurate market sentiment analysis due to the task’s specialized vocabulary. Consequently, we design, train, and deploy our own sentiment model. It outperforms all previous models (VADER, NTUSD-Fin, FinBERT, TwitterRoBERTa) when evaluated on Twitter posts. On posts from a different platform, our model performs on par with BERT-based large language models. We achieve this result at a fraction of the training and inference costs due to the model’s simple design. We publish the artifact as a python library to facilitate its use by future researchers and practitioners.
KW  - sentiment analysis
KW  - financial market sentiment
KW  - opinion mining
KW  - machine learning
KW  - deep learning
Y1  - 2023
U6  - https://doi.org/10.1016/j.jjimei.2023.100171
SN  - 2667-0968
VL  - 3
IS  - 1
PB  - Elsevier
CY  - Amsterdam
ER  -