Xiong, R (2024) Human-machine adaptation in lean waste elimination for customized building manufacturing systems. PhD thesis, Carnegie Mellon University, USA.
Abstract
The growing demand for customization in the building manufacturing industry has led to more frequent changeover operations, necessitating precise and effective adjustments to numerous control parameters to ensure production quality and efficiency. These adjustments often result in significant production waste, such as product scraps and stoppages. Currently, highly trained engineers manually develop these processes, seeking optimal parameter combinations to achieve satisfactory outcomes on production lines. On the other hand, the limited availability of experimental data and the high-dimensional search space present challenges for existing computer algorithms, impeding the development of accurate and robust learning models. To address these challenges, this study explores human-machine collaboration strategies to reduce the costs associated with complex manufacturing tuning processes. We designed a controlled virtual process game to systematically benchmark the performance of human engineers and computer algorithms, including Reinforcement Learning and Bayesian Optimization, in process control. Our platform captured and analyzed human-system dynamics across various manufacturing scenarios for operators with different backgrounds, providing insights into operator effectiveness and variability. We implemented and compared the performance of human operators and state-of-the-art algorithms, highlighting their strengths and improvement areas. Additionally, this study establishes an end-to-end framework for decoding cognitive behaviors and proposes a context-aware decision-making support method to improve decision quality and operational efficiency. We proposed and evaluated two distinct human-machine collaboration strategies—explicit and implicit—demonstrating the transformative potential of these approaches. Our findings suggest that a hybrid strategy, integrating human expertise with algorithmic capabilities, can improve the performance of manufacturing process control compared to using algorithms alone.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Tang, P |
| Uncontrolled Keywords: | optimization; collaboration; decision making; learning; manufacturing |
| Index terms: | reinforcement, effectiveness, variability, customization, efficiency, platform, engineer, manufacturing process, adaptation, collaboration, system dynamics, decision-making, state of the art, hybrid strategy, process control, strategy, manufacturing industry |
| Subjects: | construction manufacturing, user focus, research dissemination and communication, digital design, manufacturing engineering, building materials, computing systems, control systems, business, profession, statistical analysis, industry analysis, decision analysis, performance management, management |
| Topics: | Roles and Professions, Business Strategy, Research Practice, Construction Materials, Organizational Design, Design Practice, Digital Applications, Risk Management, Engineering Principles, Quality Management |
| Descriptive scope: | 3 PCT |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here