The economic and ecological added value for manufacturing companies crucially depends on how efficiently they handle dynamic events in production and conduct real-time scheduling. Individual customer orders present a significant challenge for overall planning. Therefore, both reactive and proactive planning strategies are necessary to increase the agility and robustness of the production system. Rule-based heuristics and metaheuristic algorithms are often used in complex planning tasks. While heuristics provide quick solutions, their rigidity often affects quality. Metaheuristic algorithms iterate various approaches to improve optimization but reach their limits with increasing complexity and often get trapped in local optima, reducing efficiency. Reinforcement Learning (RL) offers a promising alternative here. RL continuously learns from the environment and adapts its strategies, enabling production systems to respond to unexpected events and improve their performance. This leads to more robust and agile planning that is better aligned with the challenges of modern production paradigms.
In this context, the following research topics are offered as part of the work:
- Literature review on relevant fundamentals and suitable methodological approaches, including:
- Fundamentals of (Deep) Reinforcement Learning (DRL)
- Fundamentals of agent-based process chain simulation
- Battery production, surface technology, semiconductor production, and the process chain in general
- Training environmental modeling for various shop-floor production types (e.g., flow shop, job shop, etc.) using an open-source tool, such as OpenAI Gym, SimPy, AnyLogic, etc.
- Definition of the state space and action space for DRL training
- Definition of the reward function for dynamic scheduling (e.g., Make span, machine utilization rate, energy consumption, total tardiness, etc.)
- Selection of a suitable learning algorithm/agent for training and validation, such as DQN, policy-based algorithms and others
- Evaluation based on the defined reward function regarding productivity as well as economic and ecological benefits
- Critical evaluation and outlook
Depending on the scope of the work, only some of the above points may be covered. Experience with programming languages such as Python and Java is advantageous but not mandatory. If you are interested in these topics, I would be pleased to receive your email with the corresponding documents at chao.zhang@tu-braunschweig.de.
Art:
- Studienarbeit(Master)
- Masterarbeit
Fachrichtungen: Computer science, mechanical engineering, industrial engineering, electromobility, etc.
Begin der Arbeit: Sofort
Zuletzt geändert: 18.06.2024