On discrete time chemical production scheduling MILP models containing record keeping variables

In this paper, we present strategies for reformulating discrete time-based mixed-integer programming models for chemical production scheduling. We introduce several new integer variables, which we refer to as record keeping variables, that mixed-integer linear programming solvers are able to exploit in order to reduce total solution time. We consider these record keeping variables in the context of both batch process networks and continuous process networks. Results of several computational tests are provided that demonstrate the utility of incorporating record keeping variables in chemical production scheduling models.