Abstract:
Higher production rates combined with consistent product quality and efficient energy
use are the key objectives to be solved by production scheduling in the iron and steel
industry. Scheduling systems used to assign activities to resources often assume the
generated schedule will remain workable for the foreseeable future. Due to the dynamic
nature of the steelmaking process however, it is often difficult to maintain the original
short-term schedule. Unplanned events or disturbances can disrupt plans requiring
modification actions or even rescheduling. Frequent rescheduling often results in
instability and lack of continuity in detailed schedule execution. The schedule
disturbance management is a manual process and requires many years of experience.
The thesis presents a knowledge model for decision support to manage schedule
disturbance in steelmaking.
Literature review shows the lack of research in developing a knowledge model approach
for decision making in steelmaking. Manufacturing process such as steelmaking is
`process centric'. The thesis presents a novel knowledge elicitation approach called
XPat, which is suitable for engineering process knowledge capture. XPat is used to
identify knowledge intensive tasks in steelmaking scheduling. Managing schedule
disturbance is recognised as the most knowledge intensive task within the scheduling
process. Problem solving knowledge of different types of disturbance in steelmaking is
captured. The thesis presents a novel task template for managing the schedule
disturbance. A knowledge model of the disturbance management is developed following
CommonKADS methodology.
The knowledge model is implemented through a design model. The design model helps
in developing a prototype decision support system (DSS) to manage schedule
disturbance. The system helps the users to make right decisions and implement
consistency in the management process.
The XPat methodology, the knowledge model and finally the prototype are validated
using a number of techniques such as case studies, workshops, paper-based simulation,
and user trials. It is observed that the prototype DSS is capable of providing effective
decision support to manage schedule disturbance.
