2015 Informs Annual Meeting

SB13

INFORMS Philadelphia – 2015

2 - Reducing Healthcare Spending through Electronic Information Exchange: Aligning Provider and Insurer Idris Adjerid, Assistant Professor, Notre Dame, 358 Mendoza College of Business, Notre Dame, United States of America, Idris.Adjerid.1@nd.edu Health information exchanges (HIEs) are entities that enable the electronic sharing of patient information between disparate healthcare providers and other stakeholders but little evidence that speaks to whether promised gains to quality and efficiency have been realized. Leveraging a unique national panel dataset, we find significant cost reductions in healthcare markets that have established operational HIEs with an average reduction in spending of $139 per Medicare beneficiary per year. 3 - The Spillover Effects of Health it Investments on Regional Health Care Costs Hilal Atasoy, Assistant Professor, Temple University, Fox School of Business Alter Hall 445, Philadelphia, PA, United States of America, hilal.atasoy@temple.edu, Pei-yu Chen, Kartik Ganju Electronic medical records (EMR) are often presumed to reduce the significant health care costs in the US. However, evidence on the impact of the EMR on costs is mixed, leading to skepticism about their effectiveness. We argue that the benefits EMR can go beyond the adopting hospital due to patient and physician sharing. We find that EMR investments have significant spillover effects by reducing costs of neighboring hospitals, which suggests that EMR can reduce aggregate health care costs. 4 - Show Me the Way to Go Home: An Investigation of Ride Sharing and Alcohol Related Vehiclar Homicide Brad Greenwood, Assistant Professor, Temple University, 1810 North 13th Street, Philadelphia, PA, 19122, United States of America, brad.n.greenwood@gmail.com, Sunil Wattal We investigate how the entry of the driving service Uber affects the rate of alcohol related motor vehicle homicides. Using a difference in difference approach, the entry of Uber into markets between 2009 and 2013, results suggest a significant drop in the rate of homicides during that time. Furthermore, results suggest that not all services offered by Uber have the same effect, insofar as the effect for the Uber Black car service is intermittent and manifests only in selective locations. SB11 11-Franklin 1, Marriott Combinatorial Network Optimization Sponsor: Optimization/Integer and Discrete Optimization Sponsored Session Chair: Cole Smith, Professor And Chair, Clemson University, Freeman Hall, Clemson, SC, United States of America, jcsmith@clemson.edu 1 - On the Lagrangian Dual of the Maximum Quasi-clique Problem Zhuqi Miao, Doctoral Candidate, Oklahoma State University, 322 Engineering North, Stillwater, OK, 74078, United States of America, zhuqi.miao@okstate.edu, Baski Balasundaram Quasi-clique is a useful model in cluster detection. The maximum quasi-clique problem (MQCP) can be formulated as a binary quadratically constrained program (BQCP). In this talk, we present new computational techniques based on the Lagrangian dual of the BQCP formulation that can provide both good feasible solutions and tight upper bounds for MQCP. We report results from our empirical studies, which show that the proposed approaches outperform the leading approaches for solving the MQCP. 2 - A Semi-Continuous Formulation for Maximizing Wireless Sensor Network Lifetime Rob Curry, Graduate Research Assistant, Clemson University, Freeman Hall, Clemson, SC, 29634, United States of America, rmcurry@g.clemson.edu, Cole Smith A wireless sensor network consists of battery-powered sensors that collect and transmit data from a set of targets. Receiving and transmitting data consumes battery life; hence, multiple routes are employed to balance energy utilization and maximize network lifetime. Also, a routing configuration may need to remain stable for a minimum operating time if it is used at all. We formulate a semi- continuous linear program for this problem, along with a branch-and-price algorithm for its solution.

3 - A Branch and Price Algorithm for Solving the Hamiltonian P-median Problem Ahmed Marzouk, Texas A&M University, 3131, TAMU, College Station, TX, 77843, United States of America, ambadr@email.tamu.edu, Erick Moreno-centeno, Halit Uster Given an undirected graph, G, the Hamiltonian p-median problem is to find p cycles partitioning G with minimum cost. We present a Branch & Price algorithm that solves instances up to 129 nodes (state-of-the art is 40-nodes). To solve the pricing problem we developed 1) a new efficient algorithm to find the least cost cycle in undirected graphs with arbitrary edge costs but no negative cycles; and 2) an algorithm to find the most negative cycle in undirected graphs with arbitrary edge costs. 4 - A Backward Sampling Framework for Interdiction Problems with Fortification Leonardo Lozano, PhD Student, Clemson University, 129 Freeman hall, Clemson, SC, 29634, United States of America, llozano@g.clemson.edu, Cole Smith Three-stage sequential defender-attacker-defender problems are notoriously difficult to optimize, especially when the third-stage (recourse) problem is nonconvex. Our approach allows the third-stage problem to take any form. The algorithm restricts the defender to select a recourse decision from a sample space, and iteratively refines the sample to force convergence. We show the algorithm’s effectiveness on various such problems, including games played over the TSP and CLSP. SB13 13-Franklin 3, Marriott Recent Advances in Stochastic Integer Programming Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session Chair: Yongjia Song, Virginia Commonwealth University, 1015 Floyd Ave, 4140 Grace Harris Hall, Richmond, VA, 23284, United States of America, yjsong.pku@gmail.com 1 - On The Quantile Cut Closure of Chance-constrained Problems Weijun Xie, Georgia Institute of Technology, School of ISYE, 755 Ferst Drive, NW, Atlanta, GA, 30332-0205, United States of America, wxie33@gatech.edu, Shabbir Ahmed Quantile cuts for a chance-constrained problem (CCP) are projections of a subset of the well-known mixing set inequalities onto the original problem space. We study the closure of quantile cuts, and show that iterative application of the closure recovers the convex hull of a CCP. 2 - A Decomposition Framework for Stochastic Mixed Integer Programming Suvrajeet Sen, Professor, University of Southern California, University Park Campus, Los Angeles, CA, 90089, United States of America, s.sen@usc.edu We will summarize decomposition-based algorithms for SMIP problems. The key to these algorithms are parametric cutting planes which are developed from one of three principles: Gomory Cuts, Disjunctive Cuts, and Value Function Cuts. These concepts lead to some of the most effective schemes for SMIP problems. In a companion presentation, we will present computational results with a variety of instances. 3 - A Polyhedral Study of Multistage Stochastic Unit Commitment Polytope Yongpei Guan, Professor, University of Florida, Weil 413, In this study, we investigate a scenario-tree based multistage stochastic integer programming formulation for the unit commitment problem under uncertainty. By exploring its polyhedral structure, several families of strong valid inequalities are generated. In particular, we obtain convex hull presentations for certain special cases and facets for the general polytope. Finally, the computational results verify the effectiveness of the proposed cutting planes. Gainesville, FL, 32611, United States of America, guan@ise.ufl.edu, Jean-paul Watson, Kai Pan

69