Optimal Operations Planning under Uncertainty by Using Probabilistic Programming

Due to the lack of systematic reliability analysis, intuitive decisions can usually be made in planning process operations under uncertainty. We propose a new analysis and optimization framework to address this problem. By using the technique of probabilistic programming, the solution provides comprehensive information on profit as a function of the confidence level as well as its sensitivities to different uncertain variables. For operations under multiple uncertainties, the sources of risk that have the most significant impacts on the profitability can be identified. An optimal decision can be made, from which a suitable compensation between profit achievement and risk of constraint violation can be achieved. The approach is applied to problems of production planning, unit operations and inventory management. In particular, a novel closed framework for planning unit operations under open-loop uncertain disturbances is proposed and applied to a distillation column operation.