Integrated Framework for Operational Planning and Scheduling Under Uncertainty

Princeton University, Princeton NJ

Investigators

Abstract

Integrated Framework for Operational Planning and Scheduling under Uncertainty Professor C.A. Floudas Abstract This grant provides funding for the development of systematic approaches based on novel optimization methods and tools for the effective integration of operational planning, short-term scheduling and medium-term scheduling under uncertainty for large scale batch and continuous processes in the chemical process industry. The developed computational methods and tools will determine the optimal operational plan and detailed production schedule of batch and continuous processes, as well as establish the trade-offs between operational planning and scheduling in the presence of uncertainty in the product demands, costs, and processing times and rates. A new modeling approach for the operational planning with production disaggregation will be introduced that can address large scale batch and continuous plants. A framework for the integration of operational planning and medium-term scheduling, applicable to large-scale single-site and multi-site batch and continuous plants will be developed. A robust optimization approach for operational planning under uncertainty in product demands will be developed and applied to large scale batch and continuous chemical plants. If successful, the results of this research will lead into significant improvements in the operational planning and production scheduling under uncertainty of chemical processes. The primary goal of this work is to develop novel optimization methods and tools that address the complexity due to different types of uncertainty; material balances on raw materials, intermediate and final products; intermediate/final storage and cleanup requirements; variable processing times and rates; treatment of alternative stations; intermediate due dates; and resource constraints. The proposed work will also contribute to attaining faster response to market demand of chemical, pharmaceutical, and discrete manufacturing processes.