Robust process systems
Scheduling is a decision-making process that arises in almost any type of industrial production facilities (Pulp and Paper, Metals, Oil and Gas, Chemicals, Food and Beverages, Pharmaceuticals, Transportation, Service, Military, etc.). It is a key element of enterprise-wide optimization and has been an area of intense research activity among Operations Research and Process Systems Engineering Community. Scheduling is an inherently dynamic process with new information becoming available continuously and, in addition, multiple sources of uncertainty. Uncertainty cannot be avoided as it can cause infeasibilities and production disturbances. Our research focuses at developing mixed-integer optimization models and solution algorithms to address a variety of process-oriented planning and scheduling problems under uncertainty using conventional and data-driven robust optimization methods. Note that in contrast to general Operations Research (OR) scheduling models, the process-oriented models need the use of material flows, and very often network topologies. These are quite different from the more traditional serial and multistage systems and often require the use of detailed nonlinear process models.
- Process industry scheduling optimization using genetic algorithm and mathematical programming
- The development of a natural gas transportation logistics management system
- Energy and reserve scheduling under an N-k security criterion via robust optimization
- Contingency-constrained unit commitment with n-K security criterion: A robust optimization approach
- Tactical capacity planning in a real-world ETO industry case: An action research