Stochastic programming-based mathematical model and solution strategy for chemical production scheduling with processing time uncertainty

Abstract

Scheduling of multipurpose batch chemical plant is always affected by uncertain factors, including processing time of tasks. When the processing time deviates from its nominal value, the task sequence and executing time based upon the original schedule may become suboptimal or even infeasible. To address this issue, an optimization model based on stochastic programming is proposed for the short-term scheduling of multipurpose batch chemical plant, by introducing task sequence variables and new logical constraints relating multiple binary variables. Additionally, a network-based decomposition solution strategy, accounting for different situations of profits and shared units, is proposed to solve the large-scale problems, which has been shown to provide high quality solutions while consuming substantially less solution time than solving the entire process directly.