2016 INFORMS Annual Meeting Program

TD65

INFORMS Nashville – 2016

TD63 Cumberland 5- Omni Location Analysis II Sponsored: Location Analysis Sponsored Session Chair: Zvi Drezner, California State University-Fullerton, 800 N. State College Blvd., Fullerton, CA, 92834-3599, United States, zdrezner@fullerton.edu 1 - Optimal Placement Of M Finite-size Rectangular Facilities In An Existing Layout Rakesh Nagi, University of Illinois, Urbana-Champaign, 117 Transportation Building, MC-238, 104 South Mathews Avenue, Urbana, IL, 61801, United States, nagi@illinois.edu, Ketan Date We study the problem of placing M new finite size rectangular facilities (NFs) in a layout with N existing rectangular facilities (EFs). Interactions are present between different pairs of EFs and NFs, serviced through the Input/output points located on the facility boundary. The objective is to minimize the total weighted rectilinear distance between the interacting facilities by optimally placing the NFs. Main contribution of this paper is an analytical framework that unifies and generalizes the facility location/layout problems for minisum objective and rectilinear distance metric. 2 - Solving The Quadratic Assignment Problem Using Graphics Processing Unit Clusters On The Blue Waters Supercomputer In this work, we discuss a parallel branch-and-bound algorithm for solving the Quadratic Assignment Problem (QAP). Our parallel architecture is comprised of CUDA enabled NVIDIA Graphics Processing Unit (GPU) clusters on the Blue Waters supercomputer at the University of Illinois at Urbana-Champaign. For obtaining a lower bound, we adopt the RLT2 formulation of the QAP, and we propose a novel parallelization of the Dual Ascent algorithm on the GPUs, which shows excellent parallel speedup for large problems. We show that this GPU- accelerated approach is extremely effective in solving large QAPs to optimality. 3 - Discrete Budget Allocation In Competitive Facility Location. Tammy Drezner, Cal State Fullerton, tdrezner@fullerton.edu, Zvi Drezner We apply the gravity-based model for estimating the market shares attracted by competing facilities. We assume that a budget is available for expanding existing facilities and building new ones. We assume that the investments for improving existing facilities or constructing new ones are an integer multiple of a basic value such as 0.1% of the available budget. 4 - An Iterative Procedure For Solving Non-Convex Non-Linear Programs Zvi Drezner, Cal State Fullerton, zdrezner@fullerton.edu, Pawel J. Kalczynski Non-linear programming problems of minimizing a convex objective function subject to convex constraints are convex, and can be optimally solved by numerous approaches and canned programs. Non-convex programs such as a maximization of a convex objective subject to constraints which are outside of convex regions usually have many local optima and are generally difficult to solve. We found that such problems can be heuristically solved by a multi-start approach based on solving a sequence of linear programs. Our iterative approach is much faster than a direct multi-start approach (one to three orders of magnitude) and provided better results on four test problems and 116 instances. TD64 Cumberland 6- Omni Multiple Criteria Decision Making Applications 2 Sponsored: Multiple Criteria Decision Making Sponsored Session Chair: Murat Mustafa Koksalan, Middle East Technical University, Ankara, Turkey, koksalan@metu.edu.tr Ketan Date, University of Illinois at Urbana-Champaign, Urbana, IL, United States, date2@illinois.edu, Rakesh Nagi

2 - Properties Of Optimal Stochastic Programming Solutions In Portfolio Optimization With Different Criteria And Planning Periods Ceren Tuncer Sakar, Hacettepe University, cerents@hacettepe.edu.tr, Murat Mustafa Koksalan Incorporating multiple criteria, considering different risk measures and using multiple-period models have been recent important developments in portfolio optimization. We make a detailed analysis of the properties of optimal stochastic programming solutions for portfolio optimization problems. We work with models that have different criteria and planning periods. We compare the solutions of single and multiple-period models using expected return and Conditional Value at Risk as criteria, and demonstrate our results with tests performed with stocks traded on Istanbul Stock Exchange (Borsa Istanbul). We also consider rolling horizon settings. 3 - An Interactive Approach For Biobjective UAV Route Planning In Continuous Space Murat Mustafa Koksalan, Middle East Technical University, Indus Engineering Department, Ankara, 06531, Turkey, koksalan@metu.edu.tr, Diclehan Tezcaner Ozturk, Hannan Tureci We consider the route planning problem for Unmanned Air Vehicles (UAVs) which we formulate as finding the path that the UAV follows in a continuous terrain visiting all target points. We consider two criteria: minimization of distance traveled and radar detection threat. We develop an interactive algorithm that finds the most preferred point of a route planner (RP). We assume that the RP has an underlying linear preference function whose parameters are unknown to us. We ask the RP to compare pairs of tours and his/her preferences guide us to his/her most preferred nondominated point. 4 - Interactive Algorithms For a Wide Variety Of Preference Functions Gulsah Karakaya, Middle East Technical University, Ankara, Turkey, kgulsah@metu.edu.tr, Murat Mustafa Koksalan, Selin Damla Ahipasaoglu In this study, we introduce a broad family of preference functions that can represent a wide variety of preference functions. We develop interactive algorithms that guarantee to find the most preferred solution of a decision maker whose preferences are consistent with such functions. Our algorithms converge to the most preferred solution of the decision maker by reducing the solution space based on the preference information obtained from the decision maker and the properties of the assumed preference functions. We demonstrate the algorithms on an example problem. Social Media and Health 2.0 Sponsored: Information Systems Sponsored Session Chair: Lu Yan, Indiana University, Indiana University, Bloomington, IN, 47405, United States, yanlucy@indiana.edu 1 - How Online Comments And Government Ratings Affect Patients’ Opinion Of Medical Providers Weiguang Wang, University of Maryland, 3330 B Van Munching Hall, College Park, MD, 20742-1815, United States, weiguangwang@rhsmith.umd.edu, Niam Yaraghi, Guodong (Gordon) Gao, Ritu Agarwal One critical decision for every patient is to choose a high quality doctor. In recent years, new online channels have profoundly changed how patients access physician quality information. Most notably are the government-led efforts such as PhysicianCompare, and the grass-root movement by voluntary patient reviews such as those on Yelp.com. However, little is known how these two channels affect patient decision making. Using experimental designs, we examine patient’s choice of primary physicians with quality information from both Yelp and the government website. Our study provides the first empirical evidence of how patients weigh different information sources to inform their decision making. 2 - Modeling Dynamics Of Service Mechanism, Feedback Mechanism, And Sharing Mechanism: An Empirical Analysis Using Vector Autoregression Liuan Wang, Harbin Institute of Technology, Harbin, China, wangliuan1973@163.com, Xitong Guo With the utilization of social media in healthcare online healthcare communities has become an integral part of people’s daily lives. In this study, we explore how the interdependencies among service mechanism, feedback mechanism, and sharing mechanism affect physicians and patients in the online healthcare communities. We use vector auto-regression to model the co-movements of service mechanism, feedback mechanism, and sharing mechanism and provide evidence of strong Granger-causal interdependencies. In addition, we also investigate the effect of values in the online healthcare communities. Our results provide both theoretical and practical implications. TD65 Mockingbird 1- Omni

1 - Probabilistic Algorithms For Multiple Criteria Sorting Sinem Mutlu, Roketsan, sinemmutlu01@gmail.com, Murat Mustafa Koksalan, Yasemin Serin

We develop interactive approaches to place alternatives that are defined by multiple criteria into preference-ordered classes. Our approaches place alternatives into classes either deterministically or probabilistically with a desired level of accuracy. We also control the magnitude of misclassification regarding the number of classes between the true and placed classes of a misplaced alternative. We demonstrate the approach on a variety of problems.

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