Operational Planning of Large-Scale Continuous Processes: Deterministic Planning Model and Robust Optimization for Demand Amount and Due Date Uncertainty

Abstract

The operational planning problem typically determines both the plant's aggregate and daily production targets to meet product demands over a long time horizon varying from one to three months. The daily production profile generated from the operational planning level is used to schedule the day-to-day operations of the plant. In this paper, we first extend the production disaggregation model framework proposed by Verderame and Floudas, Ind. Eng. Chem. Res. 2008, 47, 4845-4860 to develop a novel mixed-integer linear programming operational planning model based on discrete-time representation for a large-scale multiproduct continuous plant. We allow unused processing time to carry over from one day to the next and thus capture the continuous time nature of the plant. The production totals are disaggregated into a feasible distribution of daily production requirements, and daily production profiles are provided to meet the requirements of the medium-term scheduling model developed by Shaik and Floudas, Comput. Chem. Eng. 2009, 33, 670-686. Then, we extend the work of Lin et al. Ind. Eng. Chem. Res. 2004, 28, 1069-1085 and Janak et al. Ind. Eng. Chem. Res. 2007, 31, 171-195 to address various forms of demand amount and due date uncertainty. The computational results show that the proposed deterministic and robust planning models successfully provide tight upper bounds on the plant's true production capacity.