Lagrangean-based techniques for the supply chain management of flexible process networks

The supply chain optimization of continuous process networks is essential for most chemical companies. The dynamic nature of this problem leads to systems that involve types of chemicals as well as multiple time periods, and ultimately many complex and large combinatorial optimization models. These models become very difficult to solve, and sometimes not even solvable. Hence, such models require the use of decomposition methods, so that they could be solved efficiently and effectively. This work develops decomposition techniques to a continuous flexible process network MILP model. The techniques include Lagrangean Decomposition, Lagrangean Relaxation, and Lagrangean/surrogate Relaxation, coupled with subgradient and modified subgradient optimization. Several schemes derived from the techniques are proposed and applied to the process network model. The results from the full-scale solution and the proposed decomposition schemes are presented and compared.