Medium-term Planning of a Multi-product Batch Plant under Multi-period Multi-uncertainty by Two-stage Stochastic Mixed Integer Linear Programming

Since planning and scheduling under uncertainties is a class of optimization problems with significant potential economic benefits, optimization algorithms and methods have been developed and applied to model and to optimize industry processes with various uncertainties involved in the past decades. Tow-stage stochastic mixes integer programming with recourse (2S-MILP) is one of the most promising methods. In this thesis, 2S-MILP is employed and three issues of medium-term planning under uncertainties with discrete probability distributions are considered. One is the modification of an existing multi-product batch plant planning model, the second is a scenario group based approach (SGA) and the last is moving horizon strategy (MHS).