Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

TA73

We introduce a notion of trade-off preservation, which we use as a measure of similarity for approximating the input solution, and show its connection with minimizing an optimality gap. We demonstrate the proposed method using clinical data from prostate cancer radiation therapy. 4 - Estimating the Form of a Decision Maker’s Preference Function and Converging to Preferred Solutions Murat Mustafa Koksalan, Middle East Technical University, Indus Engineering Department, Ankara, 06531, Turkey, Gulsah Karakaya We estimate the form of an underlying preference function that is assumed to represent the preferences of a decision maker in a multi-objective environment. After estimating the form, we use an algorithm that utilizes the properties of the estimated form in order to efficiently converge to a preferred solution of the decision maker. We develop the necessary theory to estimate the form of the preference function. We test our approach on several instances and show that it works well. n TA75 West Bldg 212B Joint Session MAS/Practice Curated: Personnel and Network Applications Sponsored: Military and Security Sponsored Session Chair: Lee Evans, United States Military Academy, West Point, NY, 10996, United States 1 - Utilizing Healthcare Systems Engineering Tools to Reduce Healthcare Associated Infections Jose M. Jimenez, Assistant Professor, United States Military Academy, West Point, NY, United States Healthcare acquired infections (HAIs) have decreased over the last decade. Nevertheless, HAIs are still a burden for healthcare systems all over the world. In addition to the increase in morbidity and mortality of patients that are already at risk, HAIs create economic for healthcare facilities. Although many clinical methods are being utilized to combat HAIs, it is also important to apply holistic, non-clinical solutions. Multidisciplinary approaches provide some of the best tools to gather data, analyze, determine solutions, and apply them. This presentation highlights some of the healthcare systems engineering tools that have assisted in the reduction of HAIs. 2 - Operationalizing Open-source Intelligence on the Korean Pennisula Steven Song, USMA, West Point, NY, United States In today’s information age, the amount of publicly available information has grown significantly faster than the U.S. Army’s ability to fully exploit the potential of open-source intelligence (OSINT). While OSINT, or intelligence derived from publicly available information (PAI), has unique advantages, OSINT is still undervalued and underutilized. This presentation highlights the current state of the U.S. Army OSINT enterprise on the Korean peninsula, captures the gaps, and provides recommendations for better operationalizing OSINT. Key findings indicate additional personnel, training, and partnerships with Korean counterparts are necessary to harness the maximum potential of OSINT. Emergency Response (1) Sponsored: Public Sector OR Sponsored Session Chair: Laura Albert, University of Wisconsin-Madison, Madison, WI, 53706, United States Co-Chair: Forough Enayaty Ahangar Co- Chair: Suzan Iloglu, University of Wisconsin-Madison, Madison, WI, 53705, United States 1 - Ambulance Emergency Response Optimization in Developing Countries: Methodology Justin J. Boutilier, University of Toronto, Toronto, ON, Canada, Timothy Chan The lack of emergency medical transportation is viewed as the main barrier to the access and availability of emergency medical care in low and middle-income countries (LMICs). In this paper, we present a robust optimization approach to optimize both the location and routing of emergency response vehicles, accounting for uncertainty in travel times and spatial demand characteristic of LMICs. We then combine our robust optimization approach with two machine learning frameworks and real data from Dhaka, Bangladesh. The focus of this talk is to present our methodology and present insights on policy-related questions in LMICs . n TA77 West Bldg 213A

n TA73 West Bldg 211B JFIG Panel Discussion: Advising PhD Students Sponsored: Junior Faculty JFIG Sponsored Session Chair: Chrysafis Vogiatzis, North Carolina A&T State University, North Carolina A&T State University, Greensboro, NC, 27411, United States Co-Chair: Ehsan Salari, Wichita State University, Wichita, KS, 67260, United States 1 - JFIG Panel Discussion: Advising PhD Students Chrysafis Vogiatzis, North Carolina A&T State University, 1601 East Market Street, McNair 405, Greensboro, NC, 27411, United States In this session, we discuss advising PhD students, encouraging creativity and diversity, and guiding/mentoring with successful members of academia. Panelists will share their experiences with advising students who went on to pursue successful careers in academic institutions, research and development environments, and the industry. Panelists Anna B. Nagurney, University of Massachusetts Amherst, Isenberg School of Management, Dept of Operations & Information Mgmt, Amherst, MA, 01003, United States Joseph Geunes, Texas A&M University, College Station, TX, United States Sergiy Butenko, Texas A&M University, 4037 Emerging Technologies Building, Mail Stop 3131, College Station, TX, 77843-3131, United States n TA74 West Bldg 212A Joint Session MCDM/Practice Curated: Novel Applications of Multiple Criteria Decision Technology Sponsored: Multiple Criteria Decision Making Sponsored Session Chair: Jyrki Wallenius, Aalto University School of Business, Helsinki, Finland 1 - A Stepwise Benchmarking Approach to DEA with Interval Scale Data Jyrki Wallenius, Aalto University School of Business, Runeberginkatu 22-24, Helsinki, Finland, Nasim Nasrabadi, Akram Dehnokhalaji, Pekka J. Korhonen Conventional DEA models assume that all variables are measured on a ratio-scale. However, in many DEA applications we have to deal with interval-scale data. In an earlier paper, we proposed a model for efficiency analysis using interval-scale data. In this talk, we investigate the concept of benchmarking in the framework of interval-scale data. We propose an algorithm, which results in a path of targets for each inefficient unit. 2 - Helping Atrial Fibrillation Patients with Adherence to Anticoagulation Therapy: Design Framework and Intervention Strategies Adherence to therapy is one of the main determinants of treatment success and its poor level leads to a substantial worsening of patient’s condition. Research described here addresses issue of medication adherence of older adults with atrial fibrillation who are on anticoagulation therapy for primary stroke prevention. We present a design framework of mHealth solution that implements dominance- based rough set theory to identify behavioral triggers that might impact patients’ adherence. We induce rules from patient records capturing and analyze these rules for behavioral triggers. We illustrate proposed approach with simple clinical scenario developed using patient vignettes. 3 - Tradeoff Preservation in Inferring Objective Function Weights in Multiobjective Optimization Taewoo Lee, University of Houston, E209 Engineering Bldg 2, 4722 Calhoun Rd, Houston, TX, 77204-4008, United States Given an input solution that may not be Pareto optimal, we present a novel inverse optimization methodology for multi-objective optimization that determines a weight vector producing a weakly Pareto optimal solution that preserves the decision maker’s trade-off intention encoded in the input solution. Wojtek Michalowski, University of Ottawa, Telfer School of Management, 55 Laurier Avenue E, Ottawa, ON, K1N 6N5, Canada, Mor Peleg, Szymon Wilk, Szymon Wilk, Dympna O’Sullivan, Martin Michalowski, Enea Parimbelli, Marc Carrier

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