Reformulation of the Multiperiod MILP Model for Capacity Expansion of Chemical Processes
针对化工过程产能扩张的多周期混合整数线性规划问题，提出两种模型重构方法：一种快速给出次优解的非线性规划重构，另一种通过更紧的线性规划松弛实现大规模问题求解速度提升一个数量级的精确解重构。
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.