Reformulations of Mixed-Integer Programming Continuous-Time Models for Chemical Production Scheduling

Abstract

Although several optimization models have been proposed for chemical production scheduling, there is still a need for effective solution methods. Accordingly, the goal of this work is to present different reformulations of representative continuous-time models by introducing an explicit variable for the number of batches of a given task. This idea, which has been successfully applied to discrete-time models, results in significant computational enhancement. We discuss how different objective functions benefit from particular reformulations and show significant improvements by means of an extensive computational study that includes several instances containing different process networks and scheduling horizons