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Search This Site | People | Departments | Penn State
Daniel E. Rivera
Department of Chemical Engineering
Arizona State University
Tuesday, March 18, 2008
102 Chemistry Building
10:00 a.m. - 11:00 a.m.
Abstract
Process control systems are widely used in the chemical industries to adjust flows in order to maintain inventories at desired levels.
Inventory and material flows in supply chains can be modeled using this fluid flow analogy, and as a result it becomes possible to develop decision frameworks based on control engineering principles which have an impact on operational problems in supply chain management.
In this talk, decision policies based on Internal Model Control (IMC) and Model Predictive Control (MPC) are presented as appealing alternatives to traditional Economic Order Quantity and mathematical programming approaches to inventory management.
As control-oriented decision policies, IMC and MPC can be tuned to achieve acceptable performance despite the presence of plant/model mismatch,
forecasting error, and (in the case of MPC) constraints on starts, inventories, and Work-in-Progress (WIP).
Our work in supply chains has led to current efforts (in collaboration with Linda M. Collins, Director of the Methodology Center at Penn State) on the design of optimized adaptive interventions for the prevention and treatment of chronic, relapsing disorders. This is an important emerging topic in the field of behavioral health that is relevant to many problems of public concern, among them drug and alcohol abuse, HIV/AIDS, cancer, mental health, diabetes, obesity, and cardiovascular health. Adaptive interventions differ from fixed interventions in that they systematically individualize therapy through decision rules that determine intervention dosages and forms of treatment using measurements of tailoring variables over time. Adaptive interventions constitute a form of feedback control system in the context of behavioral health; consequently drawing from principles in control engineering can significantly inform the analysis, design, and implementation of adaptive interventions, leading to improved adherence, better management of limited resources, a reduction of negative effects, and overall more effective interventions. These ideas are illustrated with a hypothetical example inspired by a real-life preventive intervention to reduce conduct disorder in families with at-risk children.