Pablo Garcia-Herreros

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The design of resilient supply chains under the risk of disruptions at candidate locations for distribution centers (DCs) is formulated as a two-stage stochastic program. The problem involves selecting DC locations, determining storage capacities for multiple commodities, and establishing the distribution strategy in scenarios that describe disruptions at(More)
We formulate the capacity expansion planning as a bilevel optimization to model the hierarchical decision structure involving industrial producers and consumers. The formulation is a mixed-integer bilevel linear program in which the upper level maximizes the profit of a producer and the lower level minimizes the cost paid by markets. The upper-level problem(More)
Dedicated to Manfred Morari for his pioneering and inspiring research work that has produced major advances in process systems engineering. Abstract This paper provides a historical perspective and an overview of the pioneering work that Manfred Morari developed in the area of resiliency for chemical processes. Motivated by unique counter-intuitive(More)
We describe a decomposition algorithm that combines Benders and scenario-based Lagrangean decomposition for two-stage stochastic programming investment planning problems with complete recourse, where the first-stage variables are mixed-integer and the second-stage variables are continuous. The algorithm is based on the cross-decomposition scheme and fully(More)
Capacity planning addresses the decision problem of an industrial producer investing on infrastructure to satisfy future demand at the highest profit. Traditional formulations neglect the rational behavior of external decision-makers by assuming static competition and captive markets. We propose a mathematical programming formulation with three levels of(More)
Production-inventory systems model the interaction of manufacturing processes with internal and external customers. The role of inventory in these systems is to buffer mismatches between production and demand caused by process uncertainty. Often, production and demand variability is described using simplified probabilistic models that ignore underlying(More)
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