2016 INFORMS Annual Meeting Program

TD20

INFORMS Nashville – 2016

2 - Connected Zero Forcing Of A Graph Boris Brimkov, Rice University, boris.brimkov@rice.edu Zero forcing is a dynamic graph coloring process whereby a colored vertex with a single uncolored neighbor forces that neighbor to be colored. This talk introduces the connected forcing process - a restriction of zero forcing in which the initially colored set of vertices induces a connected subgraph. The connected forcing number - the cardinality of the smallest initially colored vertex set which forces the entire graph to be colored - can be used to bound various linear algebraic and graph parameters, as well as to model the spread of diseases and information in social networks. Other properties of the connected forcing number are discussed, and closed formulas are given for several families of graphs. 3 - An Integer Programming Approach To Finding Minimum Zero-forcing Sets Caleb Fast, Rice University, caleb.c.fast@rice.edu, Illya V Hicks In this talk, we introduce an integer programming approach for finding minimum zero-forcing sets. Zero-forcing is a graph coloring process that, in addition to applications in power networks and quantum computing, is used to bound the minimum rank of matrices characterized by the given graph. Straightforward integer programming formulations of this problem do not perform well; however, we incorporate new bounds on the zero-forcing iteration index and zero-forcing number to improve the performance of the integer program. 4 - Mathematical Programming Approaches To Influence Maximization Problems On Social Networks Rui Zhang, University of Colorado - Boulder, Boulder, CO, United States, rui.zhang@colorado.edu, Subramanian Raghavan We study influence maximization problems on social networks from an integer programming perspective. In this talk, we focus on the weighted target set selection problem. Motivated by the desire for exact approaches, a tight and compact extended formulation is presented on trees. A complete description of its polytope is given as well. Furthermore, based on the extended formulation, a branch-and-cut approach is proposed for general networks. Computational results based on large scale graphs are discussed. Lastly, we present our results for different variants and generalizations of influence maximization problems.

health care services. Successfully modeling of their demands will facilitate decision makings in health care management. Existing approaches mainly utilized aggregate-level data from single type of health care facility and studied the observed factors’ influence. In this study, a Bayesian data analytics approach accounting for competing risk of different facilities is proposed to characterize individualized health care demand and to jointly quantify both unobserved individual heterogeneity and observed factors’ influence. A real case study is further provided to demonstrate the effectiveness of proposed method. 2 - Harnessing The Power Of Twitter With Offline Contact Networks For Probabilistic Flu Forecasting Kusha Nezafati, University of Texas, Dallas, TX, United States, Kusha.Nezafati@utdallas.edu, Qingpeng Zhang, Yulia Gel, Leticia Ramirez-Ramirez The prompt detection and forecasting of infectious diseases are critical in the defense against these diseases. Despite many promising approaches, the lack of observations for near real-time forecasting is still the key challenge for operational disease prediction and control. In contrast, online social media has a great potential for real-time epidemiological forecasting and could revolutionize modern biosurveillance capabilities. We investigate utility of Twitter to serve as a proxy for unavailable data on flu occurrence and propose a predictive platform for disease dynamics by accounting for heterogeneous social network interactions, space-time, and socio-demographic information. . 3 - The Diffusion Of User Behavior via Social Network In Online Health Communities Xi Wang, University of Iowa, xi-wang-1@uiowa.edu, Kang Zhao, Gautam Pant As a major source of social support for people with health problems, Online Health Communities (OHCs) have attracted a great number of members. Prior research has examined that users involved in online community motivated either by community-interest or by self-interest. Using text mining and unsupervised machine learning techniques, we revealed that users of a popular breast cancer OHC acting different roles corresponding to their motives. We also found user role can diffuse via social ties. Our research has implications to for OHC operators to track users’ behaviors in order to manage an OHC. TD22 107B-MCC Decision Making in Healthcare Supply Chain Sponsored: Health Applications Sponsored Session Chair: Mili Mehrotra, University Of Minnesota, 321 19th Ave S, Minneapolis, MN, 55455, United States, milim@umn.edu 1 - Gatekeeper Or Roadblock: Optimizing Evidence Generation Andgatekeeper Or Roadblock: Optimizing Evidence Generation And Access To New Drugs Liang Xu, Pennsylvania State University, 419A Business Building, Penn State University, State College, PA, 16801, United States, lzx103@psu.edu, Hui Zhao In 1992, the accelerated approval pathway (AP) is was instituted to speed up the development of new drugs but failed to be effective due to sponsors’ lack of incentives to complete post-market study. We propose and analyze three mechanisms, i.e., extra market exclusivity, pay for evidence, and augmented user fee to incentivize post-market study. Our results provide insights for policy makers on granting accelerated approval with the consideration of post-market study. 2 - Hospital Quality, Medical Charge Variation, And Patient Care Efficiency: Implications For Bundled Payment Reform Models Seokjun Youn, Texas A&M University, College Station, TX, United States, syoun@mays.tamu.edu, Gregory R Heim, Subodha Kumar, Chelliah Sriskandarajah We examine how unwarranted variation in hospital medical charges relates to patient-centric goals. From a policy maker’s viewpoint, the results imply that managerial incentives based on process quality (rather than outcome quality) may be more effective for changing operational behaviors that lead to lower variation and higher efficiency. We investigate these implications for bundled payment programs. Empirical results suggest that the current bundled payment provider selection mechanism does not consider the degree of unwarranted variation in charges, which we claim to be the improvement opportunity for each participating provider.

TD20 106C-MCC Optimality Conditions for Inventory Control

Invited: Tutorial Invited Session

Chair: Eugene A Feinberg, Stoney Brook University, Department of Applied Mathematics, Stoney Brook, NY, 11794, United States, eugene.feinberg@stonybrook.edu 1 - Optimality Conditions For Inventory Control Eugene A Feinberg, Stoney Brook University, Department of Applied Mathematics & Statistics, Stoney Brook, NY, 11794, United States, eugene.feinberg@stonybrook.edu This tutorial describes recently developed general optimality conditions for Markov Decision Processes that have significant applications to inventory control. In particular, these conditions imply the validity of optimality equations and inequalities. They also imply the convergence of value iteration algorithms. For total discounted-cost problems only two mild conditions on the continuity of transition probabilities and lower semi-continuity of one-step costs are needed. For average-cost problems, a single additional assumption on the finiteness of relative values is required. The general results are applied to periodic-review inventory control problems with discounted and average-cost criteria without any assumptions on demand distributions. The case of partially observable states is also discussed.

TD21 107A-MCC Data Analytics for Healthcare Sponsored: Health Applications Sponsored Session

Chair: Qingpeng Zhang, City University of Hong Kong, 83 Tat Chee Ave, Kowloon Tong, TX, 00000, Hong Kong, qingpeng.zhang@cityu.edu.hk 1 - Bayesian Data Analytics For Individualized Health Care Demand Modeling Of Aging Population Xuxue Sun, University of South Florida, 4202 E. Fowler Ave. ENB118, Tampa, FL, 33620, United States, xuxuesun@mail.usf.edu, Paul Cirino, Hongdao Meng, Nan Kong, Mingyang Li With high risk of having health problems, elderly people are mainly users of

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