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Publications

2019

Challenges in Decision-Making Modelling for New Product Development in the Pharmaceutical Industry

Authors
Marques, CM; Moniz, S; de Sousa, JP;

Publication
Computer Aided Chemical Engineering

Abstract
This study presents an assessment of the main research problems addressed in the literature on New Product Development (NPD) and its methodologies, for the pharmaceutical industry. The work is particularly focused on the establishment of an evolutionary perspective of the relevant modelling approaches, and on identifying the main current research challenges, considering the fast-changing business context of the industry. Main findings suggest a generalized misalignment of recent studies with today's technological and market trends, highlighting the need for new modelling strategies. © 2019 Elsevier B.V.

2018

Strategic decision-making in the pharmaceutical industry: A unified decision-making framework

Authors
Marques, CM; Moniz, S; de Sousa, JP;

Publication
Computers & Chemical Engineering

Abstract

2017

A simulation-optimization approach to integrate process design and planning decisions under technical and market uncertainties: A case from the chemical-pharmaceutical industry

Authors
Marques, CM; Moniz, S; de Sousa, JP; Barbosa Povoa, AP;

Publication
Computers & Chemical Engineering

Abstract

2016

Optimization and Monte Carlo Simulation for Product Launch Planning under Uncertainty

Authors
Marques, CM; Moniz, S; de Sousa, JP; Barbosa Povoa, AP;

Publication
Computer Aided Chemical Engineering

Abstract
This paper presents an innovative approach to solve the product-launch planning problem in the pharmaceutical industry, with uncertainty on the product demand and on clinical trials. A mixed integer linear programming (MILP) model, incorporating Monte Carlo simulation (MCS), was developed for optimizing the process design (process-unit allocation and scale-up decisions) and for capacity planning (acquisition of new units), considering the products that still require development, and the products that are already in commercialization. MCS is performed in a two-step procedure, based on Normal and Bernoulli distributions, in order to capture the effects of demand variability and trials pass-fail uncertainty, respectively. Product-launch decisions are made taking into account the probability distributions of alternative process designs, of new capacity requirements, and of the coefficients of the objective function. The applicability of the proposed solution approach is demonstrated in an illustrative case study. © 2016 Elsevier B.V.