Date of Award


Degree Type


Degree Name

Doctor of Philosophy


Mechanical and Materials Engineering


Dr. Lihui Wang

Second Advisor

Dr. Hsi-Yung (Steve) Feng


The dynamic environment of a shop floor is usually characterized with operation uncertainties. An ideal shop floor should be the one that effectively uses available manufacturing resources to achieve the best overall performance according to real-time system conditions and requirements. It thus requires an adaptive means for task planning and execution that is responsive to dynamic changes of distributed production capacity and functionality. The objective of this research is to develop an adaptive setup planning system that is responsive to dynamic changes during machining job shop operations. Setup is the most commonly used task dispatching and scheduling unit in a machine shop, and setup planning is a critical bridge between general process planning and detailed operation planning. Targeting shop floor dynamism and its adaptability to unpredictable changes, the potential flexibility of setup planning and dispatching is investigated in this research. Different from traditional approaches, in this research, the complex dynamic setup planning problem is decomposed into two subproblems: (1) off-line generic setup planning, and (2) run-time adaptive setup merging and dispatching. The former takes care of generating static and machine-independent setups, whereas the latter is responsible for machine specific decision-making. This separation makes the adaptive setup planning efficient and responsive to dynamic changes. The run-time setup merging and dispatching is the main challenge of this research, and is explored through three tasks: (1) single-machine adaptive setup planning for machine tools with different configurations, (2) multi-machine or cross-machine adaptive setup planning, and (3) dynamic setup dispatching and execution monitoring. iii The single-machine adaptive setup planning focuses on machine capacity analysis, where tool accessibility examination plays a key role. Cross-machine setup planning considers tool accessibility and scheduling requirements, which is accomplished by an extended genetic algorithm. Finally, event-driven function blocks are employed for information encapsulation to facilitate dynamic setup dispatching and execution monitoring. The integration of all three tasks provides a flexible and adaptive solution to the dynamic machining job shop operations. The proposed methodology and algorithms are implemented in MATLAB®, and validated through test parts. It is expected that the proposed approach and algorithms can not only achieve the best overall performance according to available manufacturing resources, but also bridge the gap and facilitate information exchange between process planning and scheduling, effectively.



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