Scheduling is one of the significant managerial tools for the process industries. Optimal production scheduling can achieve many advantages including significant profit improvement, higher utilisation of resources, lower inventory cost and better responsiveness to fluctuating manufacturing environment such as demand uncertainty. During real-time scheduling, several uncertainties often happen such as rush orders, order cancellation, equipment malfunction, processing time variation, etc which often cause the nominal schedule suboptimal or even infeasible.
This project aims to develop new solution methods for systematic generation of optimal or near optimal solutions for online process scheduling. The solution approach will combine data analysis techniques, artificial intelligence methods, machine learning approaches and mathematical programming approaches. The solution approach will be tested and demonstrated in a practical context with some industrial collaborators.
The successful candidate will join the Centre for Process Integration, which is recognised as a world leader in process design, integration and operations management. The Centre has already produced a range of process design and integration methodologies that have been successfully applied around the world.
This project is primarily for self-funded students. Funding may be available through University of Manchester scholarships should candidates match certain criteria.