Integrated Scheduling and Dynamic Optimization for Network Batch Processes

We address the integration of scheduling and dynamic optimization for batch processes that have complex network structures, allowing material splitting and mixing. The integrated problem is formulated into a mixed-integer nonlinear programming (MINLP) problem. To reduce the computational complexity, we develop a tailored and efficient decomposition method based on the framework of generalized Benders decomposition by exploiting the special structure of the integrated problem. The decomposed master problem is a scheduling problem with variable processing times and processing costs, as well as the Benders cuts. The primal problem comprises a set of separable dynamic optimization problems for the processing units. In comparison with the simultaneous method which solves the integrated problem by a general-purpose MINLP solver, the proposed method can reduce computational times by orders of magnitude, although global optimality of the solutions cannot be guaranteed for non-convex problems.