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Plenary speakers
 | Prof. Eva K. Lee
Director of the Center for Operations Research in Medicine and HealthCare, at Georgia Tech
Optimizing and Transforming the Healthcare System
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 | Prof. Yurii Nesterov
CORE, Université catholique de Louvain
Convergent subgradient methods for nonsmooth convex minimization
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 | Prof. Laurence Wolsey
CORE, Université catholique de Louvain
MIP Reformulations: From Lot-Sizing to Inventory Routing
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Abstracts
Eva K. Lee, Optimizing and Transforming the Healthcare System
Risk and decision models and predictive analytics have long been cornerstones for advancement of business analytics in industrial, government, and military applications. They are also playing key roles in advancing and transforming the healthcare delivery system. In particular, multi-source data system modeling and big data analytics and technologies play an increasingly important role in modern healthcare enterprise. Many problems can be formulated into mathematical models and can be analyzed using sophisticated optimization, decision analysis, and computational techniques. In this talk, we will share some of our successes in early disease diagnosis, treatment planning design, and healthcare operations through innovation in decision and predictive big data analytics.
Yurii Nesterov, Convergent subgradient methods for nonsmooth convex minimization
In this talk, we present new subgradient methods for solving nonsmooth convex optimization problems. These methods are the first ones, for which the whole sequence of test points is endowed with the worst-case performance guarantees. The methods are derived from a relaxed estimating sequences condition, and ensure reconstruction of an approximate primal-dual optimal solutions.
Our methods are applicable as efficient real-time stabilization tools for potential systems with infinite horizon. As an example, we consider a model of privacy-respecting taxation, where the center has no information on the utility functions of the agents. Nevertheless, by a proper taxation policy, the agents can be forced to apply in average the socially optimal strategies.
Preliminary numerical experiments confirm a high efficiency of the new methods.
Laurence Wolsey, MIP Reformulations: From Lot-Sizing to Inventory Routing
Starting from some basic results on simple lot-sizing problems both uncapacitated and with constant production capacity over time, we show how preprocessing, decomposition based on structure, and techniques
such as multi-commodity reformulation of fixed charge networks and echelon stock reformulations allow one to significantly strengthen the formulation of more complicated problems such as inventory routing.
For a particular inventory routing problem involving a single production facility with initial stock, inventory constraints and capacitated production, multiple clients with initial stocks, deterministic demands and inventory constraints, served by a fleet of vehicles whose routes must also be determined, we demonstrate the
use of these reformulations as well as developing new inequalities combining aspects of distribution, routing and inventory management. Some initial computational results will be presented.
The latter part of the talk is based on joint work with Pasquale Avella and Maurizio Boccia.
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