Reformulation of the Multiperiod MILP Model for Capacity Expansion of Chemical Processes

Abstract

The problem of selecting processes and capacity expansion policies for a chemical complex consisting of continuous chemical processes can be formulated as a multiperiod, mixed integer linear programming (MILP) problem. Based on a variable disaggregation technique which exploits lot sizing substructures, we propose two reformulations of the conventional MILP model. The first one is an NLP reformulation which very quickly yields good suboptimal solutions. The second is an MILP reformulation for exact solutions which leads to up to an order of magnitude faster computational results for large problems due to its tighter linear programming relaxation.