Heinbach, Benjamin
2 results
Now showing 1 - 2 of 2
- Some of the metrics are blocked by yourconsent settings
Publication Access status: Metadata only , gym-flp: A Python Package for Training Reinforcement Learning Algorithms on Facility Layout ProblemsReinforcement learning (RL) algorithms have proven to be useful tools for combinatorial optimisation. However, they are still underutilised in facility layout problems (FLPs). At the same time, RL research relies on standardised benchmarks such as the Arcade Learning Environment. To address these issues, we present an open-source Python package (gym-flp) that utilises the OpenAI Gym toolkit, specifically designed for developing and comparing RL algorithms. The package offers one discrete and three continuous problem representation environments with customisable state and action spaces. In addition, the package provides 138 discrete and 61 continuous problems commonly used in FLP literature and supports submitting custom problem sets. The user can choose between numerical and visual output of observations, depending on the RL approach being used. The package aims to facilitate experimentation with different algorithms in a reproducible manner and advance RL use in factory planning.Source Type:Article2 - Some of the metrics are blocked by yourconsent settings
Publication Access status: Open Access , Machine learning-based prediction of missing components for assembly - a case study at an engineer-to-order manufacturerFor manufacturing companies, especially for machine and plant manufacturers, the assembly of products in time has an essential impact on meeting delivery dates. Often missing individual components lead to a delayed assembly start, hereinafter referred to as assembly start delayers. Identifying the assembly start delayers early in the production process can help to set countermeasures to meet the required delivery dates. In order to achieve this, we set up 24 prediction models on four different levels of detail utilizing different machine learning-algorithms - six prediction models on every level of detail - and applying a case-based research approach in order to identify the model with the highest model quality. The modeling approach on the four levels of detail is different. The models on the coarsest level of detail predict assembly start delayers utilizing a classification approach. The models on the three finer levels of detail predict assembly start delayers via a regression of different lead times and subsequent postprocessing operations to identify the assembly start delayers. After training of the 24 prediction models based on a real data set of a machine and plant manufacturer and evaluating their model quality, the classification model utilizing a Gradient Boosting classifier showed best results. Thus, performing a binary classification to identify assembly start delayers was the best modelling approach. With the achieved results, our study is a first approach to predict assembly start delayers and gives insights in the performance of different modeling approaches in the area of a production planning and control.Source Type:Article2 9
