Batch Plant Design and Operations under Uncertainty

It is shown that the proposed formulation captures the various decision-making policies toward demand satisfaction in a unified way, and the employed feasibility criterion for the incorporation of the uncertainty enables the exact reformulation of the two-stage model as a single large-scale optimization model.

Abstract

The paper addresses the problem of including aspects of uncertainty in process parameters and product demands at the design stage of multiproduct/multipurpose batch plants. A conceptual two-stage stochastic programming formulation is proposed with an objective function comprising investment costs, expected revenues from product sales, and a penalty term accounting for expected losses due to unfilled orders. It is shown that (i) the proposed formulation captures the various decision-making policies toward demand satisfaction in a unified way, (ii) the employed feasibility criterion for the incorporation of the uncertainty enables the exact reformulation of the two-stage model as a single large-scale optimization model, (iii) for the case of discrete equipment sizes and despite the use of general continuous probability distribution functions to describe the uncertainty, linearity of the model is preserved, allowing detailed scheduling models to be included directly in the optimization model, and (iv) for the case of continuous equipment sizes, a careful exploitation of the model structure allows for the development of an efficient global optimization procedure. The versatility of the proposed unified framework is demonstrated with example problems covering different batch plant design and scheduling formulations previously suggested in the open literature.