2015 Informs Annual Meeting

TC34

INFORMS Philadelphia – 2015

2 - Mining Association between Promotions and Transactions to Find Optimum Time for Targeted Promotions Hari Koduvely, Dr, Samsung, # 2870,Orion Building, Outer Ring Road, Bangalore, K, 560037, India, hari.koduvely@gmail.com, Roshni Mohandas In the usual Targeted Promotion scenario one optimizes the promotion content tailored towards a consumer’s purchase interests to maximize response. However time of targeting a promotion also equally important. In this paper we present a new method, based on the temporal association patterns between promotion and transaction events, to find the optimum time for targeted promotions. We validate our method against existing approaches on a real retail data set and show significantly better results. 3 - The Effect of Non-local Diversity in Dynamic Class Prediction Senay Yasar Saglam, PhD Student, University of Iowa, 108 Pappajohn Business Building, S210, Iowa City, IA, 52242, United States of America, senayyasarsaglam@gmail.com, Nick Street Classifiers’ agreement in the region where a new data instance resides in has been considered a major factor in dynamic ensembles. We hypothesize that in this region the agreement among classifiers that are different is more important than among the similar ones. In other words, high local accuracy and confidence, and high diversity in other regions, is desirable. In this study, we check the validity of this hypothesis and verify that diversity still plays a role in the dynamic class prediction. 4 - Measuring Influence in Online Social Network Based on the User-content Bipartite Graph Zhiguo Zhu, Associate Prof., Dongbei University of Finance and Economics, No. 217 JianShan St., Shahekou District, Dalian, 110625, China, zhuzg0628@126.com How to precisely identify and measure influence has been a hot research direction. Differentiating from existing researches, we are devoted to combining the status of users in the network and the contents generated from these users to synthetically measure the influence diffusion. In this paper, we firstly proposed a directed user-content bipartite graph model. Finally, the experiment results verify our proposed model can discover most influential users and popular broads effectively. TC33 33-Room 410, Marriott Appointment Scheduling in Healthcare Sponsor: Health Applications Sponsored Session Chair: Armagan Bayram, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60201, United States of America, abayram@northwestern.edu 1 - Ensuring Timely Access and Adequate Capacity for an Endocrinology Clinic Moses Chan, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI, United States of America, mosesyhc@umich.edu, Amy Cohn, Amy Rothberg The weight management program was designed to promote weight loss for morbidly obese patients. Program participation is associated with reductions in BMI and improvements in cardiovascular risk factors and quality of life. Providers are booked weeks out, posing a challenge to schedule consecutive weekly visits. Non-adherence to schedule undermines the effectiveness of the program. The purpose of this study is to improve patient compliance with the program and to increase program access. 2 - An Online Appointment Scheduling Model Ali Kemal Dogru, Om PhD Student, University of Alabama, 315 Bidgood Hall, 361 Stadium Drive, Tuscaloosa, AL, 35487, United States of America, akdogru@crimson.ua.edu, Sharif Melouk Incorporating patient centered medical home (PCMH) principles, we develop an online appointment scheduling system (OASS) for a primary care setting. We propose a simulation optimization solution approach that uses two models working in concert to provide high quality solutions (i.e., schedules) in short time. We aim to minimize: 1) weighted cost of expected patient waiting time and 2) doctor idle time and overtime. Key Words: Online Appointment Scheduling, Simulation Optimization, PCMH 3 - Managing Series Patients in a Healthcare Facility Siyun Yu, STOR Department, UNC-Chapel Hill, B26 Hanes Hall, Chapel Hill, NC, 27514, United States of America, yusiyun@live.unc.edu, Vidyadhar Kulkarni, Vinayak Deshpande Series patients are scheduled for a series of appointments, such as patients in physical therapy clinic, dialysis center, etc. To balance the demands from different

types of new as well as returning patients and the available appointment slots, we develop stochastic models to determine the number of slots and the scheduling policy that optimize performance. Heuristic polices are proposed which share the same structural properties of the optimal policy and are more computationally efficient. 4 - Managing Virtual Appointments in Chronic Care Armagan Bayram, Northwestern University, 2145 Sheridan Road, Evanston, IL, 60201, United States of America, abayram@northwestern.edu, Seyed Iravani, Sarang Deo, Karen Smilowitz Virtual visits can assist in managing chronic conditions by providing low cost monitoring, treatment and education. Motivated by these benefits, we develop capacity allocation models that decide which patients to schedule given limited availability of both office and virtual visit slots. We model this problem using a dynamic programming framework over a finite horizon, and perform analytical and numerical analyses to identify policies for scheduling patients for different medical interventions.

TC34 34-Room 411, Marriott

Optimal Cancer Therapy Sponsor: Health Applications Sponsored Session

Chair: Kevin Leder, Assistant Professor, University of Minnesota, 111 Church St, Minneapolis, MN, 55455, United States of America, lede0024@umn.edu 1 - Nonstationary Spatiotemporally Integrated Fractionation Ali Ajdari, University of Washington, Industrial and Systems Engineering, Seattle, WA, 98195, ali.adr86@gmail.com, Archis Ghate We consider the optimal fractionation problem where the fluence-maps are allowed to change over treatment sessions. This results in a high-dimensional nonconvex dynamic optimization problem. We present an approximate solution method rooted in convex and dynamic programming. 2 - Treatment of Chronic Myeloid Leukemia with Multiple Targeted Therapies Qie He, University of Minnesota, 111 Church Street SE, Minneapolis, MN, United States of America, qhe@umn.edu, Junfeng Zhu, Kevin Leder, Jasmine Foo Recently several targeted therapies have been developed to treat Chronic Myeloid Leukemia (CML). A significant problem is the development of resistance to therapy in patients. Therapy combination can slow this development, but the number of combinations is huge. We develop a model to find combinations that are promising for clinical trials. The model captures cell evolution and toxicity constraints. Our optimal combinations are predicted to significantly outperform common clinical practice. 3 - Combined Therapy in Acute Lymphoblastic Leukemia Kevin Leder, Assistant Professor, University of Minnesota, 111 Church St, Minneapolis, MN, 55455, United States of America, lede0024@umn.edu Acute lymphoblastic leukemia is a cancer of the blood system. While successful treatment of the disease in juvenile patients is possible, it is difficult to treat in adult patients. One treatment modality is the use of the targeted therapy nilotinib. However, drug resistance is a serious issue. To avoid this drug resistance we consider the combination of nilotinib and radiation. We develop a mathematical model for this combined therapy and compare with experimental observations.

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