Informs Annual Meeting Phoenix 2018

INFORMS Phoenix – 2018

MD76

3 - Robustness and Prediction Error in Multi Response Parameter Design Optimization Melis Ozates, Middle East Technical University, Industrial Engineering Dept, Office Room 318, Ankara, 06800, Gulser Koksal, Murat Mustafa Koksalan Parameter design optimization that involves two or more responses of products or processes is a well-known research problem. This problem involves multiple objectives typically formulated as response surface models using regression. These models are constructed with some prediction error even though the model structure is appropriate. In comparing alternative solutions of the model, the decision maker (DM) has difficulty to comprehend closeness of mean response is to its target, variance of true response (robustness) and magnitude of prediction error. In this study, these components are analyzed under certain conditions and guidance is provided for the DM to facilitate the comparison. 4 - An Interactive Approximation Algorithm for Multi-objective Integer Programs Banu Lokman, Middle East Technical University, Department of Industrial Engineering, Universiteler Mahallesi, Ankara, 06800, Turkey, Murat Mustafa Koksalan, Pekka J. Korhonen, Jyrki Wallenius We develop an interactive algorithm that approximates the most preferred solution for any multi-objective integer program with a desired level of accuracy. We assume that the preferences of the decision maker (DM) are consistent with a quasiconcave value function unknown to us. Based on the pairwise comparisons of the DM, we construct convex cones and eliminate the inferior regions that are close to being dominated by the cones in addition to the regions dominated by the cones. Our computational experiments show that the algorithm performs very well in terms of the quality of the solution found, the solution time, and the required preference information. 5 - A Multi-objective Approach for Magnetic Resonance Imaging/ultrasonography Fusion-guided Targeted Prostate Biopsies Utku Lokman, TOBB University of Economics and Technology, Ankara, Turkey, Banu Lokman, Sukru Ali Altan, Oztug Adsan Magnetic resonance imaging (MRI) of the prostate has recently gained substantial attention in the diagnostic workup of prostate cancer (PCa), which is the most common cancer in males. The standard prostate biopsy method is 12-core blind systematic sampling of the whole prostate. An innovative fusion of previously acquired MRI images with the real-time ultrasound (US) may lead to improved PCa detection, by identifying the suspicious areas in the prostate and targeting them accurately under MRI/US-fusion guidance, thus necessitating fewer biopsies with fewer side effects. In this study, we develop a multi-objective model to decide on the quantity of tissues obtained per each suspicious lesion. n MD75 West Bldg 212B INFORMS-MAS Awards Session Sponsored: Military and Security Sponsored Session Chair: Andrew Oscar Hall, United States Military Academy, West Point, NY, 10996, United States 1 - MAS President Remarks The current president of MAS will provide an overview of the annual society awards, motivate future applicants, and introduce the most recent award winners who will, respectively, share an overview of their ongoing and planned work (Bonder Scholarship), present the results of their award-winning paper (Koopman Prize), and share selected insights and observations learned over a career of meaningful contributions to the discipline (J. Steinhardt Prize). 2 - 2018 Seth Bonder Scholarship The purpose of the Seth Bonder scholarship for applied operations research in military applications is to promote the development and application of process modeling and operations research analysis to military issues. The scholarship provides funding to support the development of highly qualified individuals and promote the interchange of military O.R. research knowledge in conjunction with INFORMS. 3 - 2018 Koopman Prize This award for the best published paper or report on military operations research topics directly related to the goals of MAS. The award honors the memory of Bernard Koopman (1900-1981), who was a pioneer in the field of operations research. He was active in the founding of the Operations Research Society of America (ORSA), later merged with TIMS to form INFORMS, and served as its president in 1956. Andrew Oscar Hall, United States Military Academy, 86 B. Patridge Pl, West Point, NY, 10996, United States

4 - 2018 J. Steinhardt Prize The J. Steinhardt Prize is sponsored by the CNA Corporation. The prize is awarded for outstanding contributions to Military Operations Research and is awarded for life work rather than for any particular contribution.

n MD76 West Bldg 212C MIF Paper Competition Award Sponsored: Minority Issues Sponsored Session Chair: Lauren Berrings Davis, North Carolina A&T State University, Greensboro, NC, 27411, United States Co-Chair: Sean Barnes, Univ of Maryland-College Park, College Park, MD, 20742, United States 1 - A Decomposition-based Heuristic for Stochastic Emergency Routing Problems Belleh Fontem, University of Mary Washington, Fredericksburg, VA, 22407, United States, Sharif Melouk, Burcu B. Keskin, Naeem Bajwa This paper proposes a decomposition-based heuristic for a network delivery problem in which relief workers acquire valuable emergency supplies from relief warehouses, and transport them to meet the urgent needs of distressed population centres. The problem context dictates that the relief items reach these population centres before critical deadlines. However, coordination challenges and random disruptions introduce uncertainty in both network travel times and the destination deadlines. Hence, relief workers have to negotiate the tension between ensuring a high probability of punctual delivery and maximising the combined value of the relief supplies delivered. For an arbitrary routing scheme which guarantees punctual delivery in an uncertainty-free state of nature, the heuristic yields an upper bound on the probability that, under uncertainty, the routing scheme described will lead to tardy delivery. We demonstrate our solution approach on a small numerical example and glean insights from experiments on a realistically sized problem. Overall, our central model and proposed solution approach are useful to managers who need to evaluate routing options and devise effective operational delivery plans in humanitarian crisis situations. 2 - Demand Fulfillment Probability in a Multi-item Inventory System with Limited Historical Data Canan Gunes Corlu, Boston University, 808 Commonwealth Avenue, Boston, MA, 02215, United States, Bahar Biller, Sridhar Tayur In a budget-constrained multi-item inventory system with independent demands, we consider the case of unknown demand parameters that are estimated from limited amounts of historical demand data. In this situation, the probability of satisfying all item demands, as a measure of demand fulfillment, is a function of the finite-sample estimates of the unknown demand parameters; thus, the demand fulfillment probability is a random variable. First, we characterize the properties of an asymptotical approximation to the mean and variance of this random variable due to the use of limited data for demand parameter estimation. Second, we use the characterization of the variance of the demand fulfillment probability for quantifying the impact of demand parameter uncertainty on demand fulfillment via numerical experiments. Third, we propose an inventory optimization problem that minimizes the variance of the demand fulfillment probability due to demand parameter uncertainty subject to a budget constraint on the total inventory investment. Our numerical experiments demonstrate that, despite the availability of limited amounts of historical demand data, it is possible to manage inventory with significantly reduced variance in the demand fulfillment probability. 3 - Exploring the Value of Waiting during Labor Karen T. Hicklin, University of North Carolina at Chapel Hill, B-24 Hanes Hall, Chapel Hill, NC, 27599-3260, United States, Julie Ivy, Fay Cobb Payton, Meera Viswanathan, Evan Myers Of the nearly 4 million births that occur each year in the United States, almost one in every three is a cesarean delivery. Despite the increasing C-section rate over the years, there is no evidence that the increase has caused a decrease in neonatal or maternal mortality or morbidity. Bayesian decision analysis is used to model the decision between classifying a patient as “failure-to-progress, which is cause for a C-section, using either current information (prior probability) or information gathered (posterior probability) as labor continues. The Bayesian decision models determine the conditions under which it is appropriate to gather additional information (i.e., take an observation) prior to deciding to end labor and perform a C-section based on the decision maker’s belief of successful labor. During an observation period, the decision maker learns more about the patient and her medical state and the likelihood of a successful vaginal delivery is updated. This study determines the conditional value of information (conditional on the decision maker’s prior belief) and determines the conditions under which information has positive value. This model can be used to facilitate shared decision making for labor and delivery through communicating beliefs, risk perceptions, and the associated actions.

241