Refine
Has Fulltext
- yes (2)
Document Type
- Doctoral Thesis (2)
Language
- English (2)
Is part of the Bibliography
- yes (2)
Keywords
- simulation (2) (remove)
Institute
- Wirtschaftswissenschaften (2) (remove)
Volatile supply and sales markets, coupled with increasing product individualization and complex production processes, present significant challenges for manufacturing companies. These must navigate and adapt to ever-shifting external and internal factors while ensuring robustness against process variabilities and unforeseen events. This has a pronounced impact on production control, which serves as the operational intersection between production planning and the shop- floor resources, and necessitates the capability to manage intricate process interdependencies effectively. Considering the increasing dynamics and product diversification, alongside the need to maintain constant production performances, the implementation of innovative control strategies becomes crucial.
In recent years, the integration of Industry 4.0 technologies and machine learning methods has gained prominence in addressing emerging challenges in production applications. Within this context, this cumulative thesis analyzes deep learning based production systems based on five publications. Particular attention is paid to the applications of deep reinforcement learning, aiming to explore its potential in dynamic control contexts. Analysis reveal that deep reinforcement learning excels in various applications, especially in dynamic production control tasks. Its efficacy can be attributed to its interactive learning and real-time operational model. However, despite its evident utility, there are notable structural, organizational, and algorithmic gaps in the prevailing research. A predominant portion of deep reinforcement learning based approaches is limited to specific job shop scenarios and often overlooks the potential synergies in combined resources. Furthermore, it highlights the rare implementation of multi-agent systems and semi-heterarchical systems in practical settings. A notable gap remains in the integration of deep reinforcement learning into a hyper-heuristic.
To bridge these research gaps, this thesis introduces a deep reinforcement learning based hyper- heuristic for the control of modular production systems, developed in accordance with the design science research methodology. Implemented within a semi-heterarchical multi-agent framework, this approach achieves a threefold reduction in control and optimisation complexity while ensuring high scalability, adaptability, and robustness of the system. In comparative benchmarks, this control methodology outperforms rule-based heuristics, reducing throughput times and tardiness, and effectively incorporates customer and order-centric metrics. The control artifact facilitates a rapid scenario generation, motivating for further research efforts and bridging the gap to real-world applications. The overarching goal is to foster a synergy between theoretical insights and practical solutions, thereby enriching scientific discourse and addressing current industrial challenges.
This dissertation consists of five self-contained essays, addressing different aspects of career choices, especially the choice of entrepreneurship, under risk and ambiguity. In Chapter 2, the first essay develops an occupational choice model with boundedly rational agents, who lack information, receive noisy feedback, and are restricted in their decisions by their personality, to analyze and explain puzzling empirical evidence on entrepreneurial decision processes. In the second essay, in Chapter 3, I contribute to the literature on entrepreneurial choice by constructing a general career choice model on the basis of the assumption that outcomes are partially ambiguous. The third essay, in Chapter 4, theoretically and empirically analyzes the impact of media on career choices, where information on entrepreneurship provided by the media is treated as an informational shock affecting prior beliefs. The fourth essay, presented in Chapter 5, contains an empirical analysis of the effects of cyclical macro variables (GDP and unemployment) on innovative start-ups in Germany. In the fifth, and last, essay in Chapter 6, we examine whether information on personality is useful for advice, using the example of career advice.