A simulation-optimization approach to integrate process design and planning decisions under technical and market uncertainties: A case from the chemical-pharmaceutical industry. (2nd November 2017)

• Record Type: Journal Article
• Authors: Marques, Catarina M.; Moniz, Samuel; de Sousa, Jorge Pinho; Barbosa-Póvoa, Ana Paula
• Is Part Of: Computers & chemical engineering. Volume 106(2017)
• Page Start: 796
• Page End: 813
• Publication Date: 2017-11-02
• Subjects: Process design -- Capacity planning -- Scale-ups -- Mixed integer linear programing -- Monte carlo simulation -- Uncertainty; Chemical engineering -- Data processing -- Periodicals; 660.0285
• DOI: 10.1016/j.compchemeng.2017.04.008
• Languages: English
• ISSNs: 0098-1354

Abstract

Highlights: A novel approach, combining a MILP model with a two-step Monte Carlo simulation is proposed. The model considers simultaneously, technical and market uncertainties, resources limitations and lots traceability. Integration of strategic and tactical decisions (i.e. process design, capacity extensions, how much to produce and store). The uncertainty impacts in process design, scale-ups, and production planning decisions are effectively captured. Results show the benefits of a large-range analysis of the uncertainty parameters, for a sound long-term decision-making. Abstract: This study addresses the product-launch planning problem in the chemical-pharmaceutical industry under technical and market uncertainties, and considering resource limitations associated to the need of processing in the same plant products under development and products in commercialization. A novel approach is developed by combining a mixed integer linear programming (MILP) model and a Monte Carlo simulation (MCS) procedure, to deal with the integrated process design and production planning decisions during the New Product Development (NPD) phase. The Monte Carlo simulation framework was designed as a two-step sampling procedure based on Bernoulli and Normal distributions. Results show the unquestionable influence of the uncertainty parameters on the decision variables and objective function, thus highlighting the inherent risks associated to the deterministic models. Process designs and scale-ups that maximize expected profit were determined, providing a valuable knowledge frame to support the long-term decision-making process, and enabling earlier and better decisions during NPD.