Extended Discrete-Time Resource Task Network Formulation for the Reactive Scheduling of a Mixed Batch/Continuous Process

Abstract

A mixed integer linear programming (MILP) model is developed for the optimal reactive scheduling of a mixed batch/continuous process, based on the discrete time resource task network (RTN) representation and extensions. The scheduling task is complicated with the mixed process units and network structure, as well as operation rules such as product changeovers. The extended RTN model introduces modifications to the conventional RTN models such as multiextent resource balances and, also, adds more features such as resource limit balances and resource slacks. These extensions allow for efficient modeling of the mixed plant in great detail. The extended RTN model is further reformulated to the state space form by incorporating lifted state variables that represent task histories. The state space RTN model facilitates reactive schedule design, particularly when used with the rolling horizon scheme. In the case study, we show the advantages of the state space RTN model in periodic rescheduling under process disruptions.