Multicut logic‐based Benders decomposition for discrete‐time scheduling and dynamic optimization of network batch plants

Abstract

This study presents the first application of a logic‐based Benders decomposition (LBBD) technique in the field of simultaneous scheduling and dynamic optimization (SSDO), applied to network batch processes with a discrete‐time scheduling formulation. The proposed algorithm employs neighborhood information of ordered discrete decisions (e.g., batching variables) to generate cuts, rather than relying on traditional cut generation techniques based on dual information that are implemented in generalized Benders decomposition (GBD) algorithms. The proposed algorithm relies on solving multiple subproblems per iteration, which is a feature that allows the generation of multiple cuts per iteration thus producing accurate approximations of the objective function in shorter computational times. This results in the herein proposed multicut logic‐based discrete Benders decomposition (MLD‐BD) algorithm, which enables features such as a pruning strategy, and a cut‐off technique. Two case studies are used to demonstrate the computational advantages of the MLD‐BD framework against GBD and heuristic methodologies.