Competitive pressure from global market forces has placed a heightened emphasis on information exchange, coordination, and integration of all decision-making layers of the chemical supply chain. Significant developments in this area, supported by advances in computer hardware, data exchange, storage, and optimization algorithms, have potential for incurring substantial economic benefits for chemical operations. This coordination often extends to inclusion of power grid and power supply networks such as distributed energy systems into the operation and control of chemical systems. Representation of complex chemical processes for use in demand response scheduling requires advanced modeling techniques. Solution of such optimization problems results in large-scale mixed-integer linear programs (MILPs) or mixed-integer nonlinear programs (MINLPs), requiring the use of parallel computing and data structures to solve in a reasonable span of time.
