The chemical-pharmaceutical sector is facing an unprecedented fast-changing environment,
with new market and technological trends impacting the companies' operational strategies …

This paper proposes a novel data-driven robust optimization framework that leverages the
power of machine learning and big data analytics for decision-making under uncertainty. By …

Regret is defined as the deviation of objective value from the perfect information solution,
and serves as an important evaluation metric for decision-making under uncertainty. This …

This paper addresses the integration of two relevant problems within industry: raw material
purchasing and production planning. The raw material purchasing problem considers the …

In this work, we propose an integer linear programming model for production scheduling of a
group of finished products with independent demand. The model for the master production …

This study addresses the product-launch planning problem in the chemical-pharmaceutical
industry under technical and market uncertainties, and considering resource limitations …

The enterprise management of current industrial processes has becoming increasingly
complex, imposing novel challenges to the implementation of efficient decision support …

The implementation of efficient strategic decisions such as process design and capacity
investment under uncertainty, during the product development process, is critical for the …

This work addresses the optimal planning of biopharmaceutical continuous manufacturing
processes under performance decay, modelling its operational constraints in a mixed …

This work addresses the optimal planning and campaign scheduling of biopharmaceutical
manufacturing processes, considering multiple operational characteristics, such as the …