Optimal Production Planning for Chemical Processes under Uncertain Market Conditions

Abstract

Abstract Due to lack of a systematic reliability analysis, intuitive decisions can often be made in production planning under uncertainty. These decisions will lead to either conservative or aggressive production. In this work, we propose a novel analysis and optimization framework to address this problem, with which the relationship between the profitability and reliability of a planned operation can be quantified. By using probabilistic programming, the solution of the problem provides comprehensive information on the economical achievement as a function of the desired confidence level of satisfying process constraints. The feasible region will shrink and the profit will decrease when increasing the confidence level. For complex plant operations under multiple uncertainties, the sources of risk that have the most significant impacts on the profitability can be identified. Through this approach, an optimal as well as reliable decision for operations can be made, from which a suitable compensation between the profit and the probability of constraints violation can be received. The approach is applied to three problems of production planning under uncertainty and various issues in planning these operations are discussed.