In this project, the objective is to explore the use of data mining in manufacturing by implementing. The selected problem involves the assignment of due dates in a dynamic job shop using data mining. First, a discrete event simulation model of the dynamic job shop environment will be constructed using Python. The data generated by the simulation replications will be used to generate the training and testing data sets. Considering the job characteristic and shop condition-based factors reported in the literature, feature selection will be implemented. Finally, a decision tree will be constructed to develop rule-based due date assignments. Computational experiments based on different dispatching rules and shop utilizations will be conducted to demonstrate the efficiency and effectiveness of the due dates determined using data mining.
Faculty Department of Project Supervisor:
School of Management
Number of Students:
Related Areas of Project:
Computer Science and Engineering