Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

WA75

3 - A Multi-attribute Optimization Approach for Post-acute Provider Selection Ashkan Hassani, Texas A&M University, 4050 ETB, College

heuristic will then determine when, and how much of, the collected information will be sent back to the control station, either directly or via other resources as relays.

Station, TX, 77840, United States, Hossein Badri, Maryam Khatami, Kai Yang, Mark Alan Lawley

n WA76 West Bldg 212C Academic Job Search Panel Sponsored: Minority Issues Sponsored Session Chair: Eduardo Perez, Texas State University, San Marcos, TX, 78666, United States 1 - Job Search Strategies for Academic Positions Eduardo Perez, Texas State University, Roy F. Mitte Complex, 749 N. Comanche St., San Marcos, TX, 78666, United States Five panelists will be sharing their experience as chair of faculty search committees at their respective institutions. The panel is integrated by: Dr. Mingzhou Jin from The University of Tennessee, Dr. Emmett J. Lodree from The University of Alabama, Dr. Clara Novoa from Texas State University, Dr. Lewis Ntaimo from Texas A&M University, and Dr. Chiwoo Park from Florida State Mingzhou Jin, University of Tennessee-Knoxville, 525D John D. Tickle Engineering Building, Industrial and Systems Engineering, Knoxville, TN, 37996, United States Emmett J. Lodree, University of Alabama, Info Systems, Statistics, and Mgt Science, Box 870226, Tuscaloosa, AL, 35487-0226, United States Clara Novoa, Texas State University, 120 Azolar Drive, San Marcos, TX, 78666, United States Lewis Ntaimo, Texas A&M University, 3131 TAMU, College Station, TX, 77843, United States Chiwoo Park, FAMU-FSU College of Engineering, 2525 Pottsdamer St, Tallahassee, FL, 32310-6046, United States n WA77 West Bldg 213A Matchings and Assignments with Societal Impact Sponsored: Public Sector OR Sponsored Session Chair: Andrew C. Trapp, Worcester Polytechnic Institute, Worcester, MA, 01609, United States 1 - Machine Learning and Optimization for Service Classification and Assignment of Information Technology Services IT help desks assist users with a variety of services. However, ad-hoc, inconsistent handling of help desk tickets can lead to issues with customer experience and increasing cost. We propose a decision support system that embeds machine learning techniques to classify incoming tickets according to service categories. Based on the service category, along with available technician capacity and skill sets, it uses combinatorial optimization techniques to assign tickets to technicians. Our system ensures that the right tickets get to the right technicians, resulting in improved use of resources. 2 - Optimization-based Mechanism for Matching Students to International Projects Hoda Atef Yekta, University of Connecticut, Storrs, CT, 06066, United States, Andrew C. Trapp, Pitchaya Wiratchotisatian Global projects are a cornerstone of the project-based curriculum at Worcester Polytechnic Institute (WPI), which has been recognized with the Bernard M. Gordon Prize from the US National Academy of Engineering. Recent increases in applications have caused the manual process of placing students (nearly 1,000) into international project centers (nearly 50) to become increasingly complex. We propose an optimization-based approach that matches students to project centers by considering student preferences, as well as center capacities and priorities over student skills. We compare the results of our model with other matching mechanisms based on efficiency, stability and strategy-proofness. University. Panelists Pitchaya Wiratchotisatian, Worcester Polytechnic Institute, Worcester, MA, 01609, United States, Andrew C. Trapp, Christopher J. Chagnon, Soussan Djamasbi

In this study a decision making approach is developed for Post-Acute Care Provider (PACP) selection problem. This approach includes two stages. In the first stage, some quality metrics are used in a TOPSIS method to calculate the closeness coefficients of each candidate PACP to short-stay patient’s and long-stay patients. In the second stage, these coefficients are used in a mathematical programming model, along with some other cost and service level metrics to select the best PACP’s for both short-stay and long-stay patients. The proposed approach is implemented for PACP selection problem in the city of Houston, TX, and the results are presented and analyzed. 4 - Using Pareto-efficient Analysis to Incorporate the Six Aims for Quality in the Analysis of Trauma Care Services Lucy Aragon, Wichita State University, 1845 Fairmount Street, Wichita, KS, 67260, United States, Laila Cure The Institute of Medicine proposed six aims to guide healthcare quality improvement efforts. However, most healthcare improvement programs still evaluate quality along one aim at a time, effectiveness. This research proposes a Pareto-efficient analysis to incorporate all six aims in the evaluation of healthcare quality. A trauma care setting is used to investigate data requirements, develop the methodology and evaluate its implications. n WA75 West Bldg 212B Planning for Asessments and Analyses Sponsored: Military and Security Sponsored Session Chair: Andrew Keith, Air Force Institute of Technology, Dayton, OH, 45424, United States 1 - Operations Assessment Planning Using Robust Partially Observable Markov Decision Processes Andrew Keith, Air Force Institute of Technology, Dayton, OH, United States, Darryl K. Ahner Military operations assessment planning involves sequential decision making under model uncertainty and partial state observability. Robust partially observable Markov decision processes extend traditional Markov decision processes to address this complex setting. We present a novel robust partially observable Markov decision process formulation and solution technique. Then, we compare the performance of the new solution technique to existing methods using classic problems from the literature. We demonstrate the application of the new methods to a notional operations assessment planning scenario. 2 - Designing the Bayesian Enterprise Analysis Model (BEAM) Mark Gallagher, Randy Saunders Bayesian Enterprise Analysis Model (BEAM) is an enterprise model that models the probability distribution of quantities of assets in theater. BEAM is intended to search strategy tradeoffs where assets are assigned to different missions. BEAM also models Red assets to account for an adversary adapting their strategy. BEAM calculates the distribution of modeled results with Bayesian methods applied to the objects and missions in each cycle’s plan. We describe the theoretic results, analysis challenges, and questions being explored in the prototype. 3 - Weighted Laplacian-regularized Optimal Experimental Design for Expensive Tests with Outliers: An Application in Linear-elastic Fracture Mechanics Stanford Martinez, Graduate Research Assistant, University of Texas at San Antonio, 1 Utsa Circle, San Antonio, TX, 78249, United States, Adel Alaeddini In the design of experiments, traditional space-filling methods do not preserve the geometry of the design vectors involved in the experiment. This can be mitigated using Laplacian Regularization (LR), and rare events present in the experiment may be discovered via outlier detection. Such events may be caused by material defects or other occurrences during an experiment, as occasionally encountered in fracture mechanics. The effects of this combination of LR and outlier detection are studied alongside traditional methods in terms of design efficiency and practicality-of-use. 4 - Optimization of Bandwidth in Constrained Networks of Cross-domain Unmanned Systems McKenzie Worden, University at Buffalo-CUBRC, 4455 Genesee Street, Buffalo, NY, 14225, United States Using a multi-phase, iterative heuristic approach, we aim to define information collection and exchange between unmanned resources and control stations in a bandwidth constrained network. Initial decisions will be made determining the areas from which resources’ sensors will collect information. Using these decisions, and considering bandwidth and communication range restrictions, the

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