2015 Informs Annual Meeting

TC30

INFORMS Philadelphia – 2015

TC31 31-Room 408, Marriott

2 - Appointment Scheduling and Overbooking to Improve Patient Access and Reduce Patient Backlog Linda Laganga, Vp Of Quality Systems, Mental Health Center of Denver, 4141 East Dickenson Place, Denver, CO, 80302, United States of America, linda.laganga@mhcd.org, Stephen Lawrence Patient no-shows continue to trouble outpatient clinical service delivery. We continue our piloting and implementation of scheduling models developed in our earlier research to develop new techniques to assist clinics in meeting their goals to improve patient flow and reduce backlog in scheduling. We utilize medical practice experience to develop realistic estimates of costs and their effect on the selection of high-performing scheduling alternatives. 3 - Improving HIV Early Infant Diagnosis Supply Chains in Sub-Saharan Africa: Models and Application to Mozambique Jonas Jonasson,Student, London Business School, Regent’s Park, London NW1 4SA, United Kingdom, jjonasson@london.edu, Sarang Deo, Jérémie Gallien Most countries in sub-Saharan Africa experience delays in HIV early infant diagnosis (EID). We develop a two-part modeling framework to generate operational improvements in EID networks and evaluate their impact on public health. For the case of Mozambique, we estimate that the interventions of optimally re-assigning clinics to labs and optimally re-allocating diagnostic capacity would result in 11% and 22% shorter turnaround times and 4% and 7% more infants starting treatment, respectively. Chair: Mohamad Hasan, Associate Professor, Kuwait University, Department of Quantitative Methods &IS, CBA,Kuwait University, Kuwait City, 13055, Kuwait, mkamal@cba.edu.kw 1 - Review of Consistency Among Pairwise Comparisons: Relationship Between Indices and Human Perception Yuji Sato, Graduate School of Management, Chukyo University, 101 Yagotohonmachi, Showa, Nagoya, 466-8666, Japan, ysatoh@1988.jukuin.keio.ac.jp This paper reviews the Consistency Index (CI) of AHP. Since AHP requires redundant pairwise comparisons, transitivity in judgment is often violated. The review focuses on the detection capability of CI, and the relationship between the size of CI and the goodness-of-fit of weight to decision maker’s perception. The results imply that CI may not distinguish the consistency of judgment nor the size may have no relation with the degree of goodness-of-fit of weight to decision maker’s perception. 2 - Transformations and Materializations of Uncertainty Sets in Robust Optimization Abhilasha Aswal, International Institute of Information Technology, Bangalore, 26/C Electronics City, Bangalore, KA, 560100, India, abhilasha.aswal@iiitb.ac.in, Prasanna Gns We present a polyhedral representation of uncertainty for robust optimization and a volume based uncertainty measure for it. Our decision support framework enables easy transformations and materializations of a given uncertainty set and also easy set-theoretic operations on alternative uncertainty sets. These operations are quite useful in practice and are more difficult with probabilistic representations of uncertainty and non-polyhedral robust uncertainty sets. 3 - Open Source or Proprietary? A Study on Software Diffusion in a Competitive Market Chao Ding, Assistant Professor, University of Hong Kong, KKL 807, Hong Kong, Hong Kong - PRC, chao.ding@hku.hk When choosing between open source software and proprietary software, consumers will consider software quality, cost, consumer reviews, promotions, compatibility, technical support, ease of use, etc. In this paper, we consider three important decision making factors as identified in literature: external influence, internal influence and ownership cost and study their impact on consumers’ adoption decision. 4 - A Decision Support System for Predicting International Freight Flows for Trade Mohamad Hasan, Associate Professor, Kuwait University, Department of Quantitative Methods &IS, CBA, Kuwait City, 13055, Kuwait, mkamal@cba.edu.kw A decision support system is developed that can help decision makers to take right decisions about the country international trade system. It helps them to evaluate deferent scenarios to improve the multimodal Transport system and import, export, re-export, and transit operations. These improvements will enhance the competitiveness and integration of this system.The overall results will help in increasing the international trade share for the country. TC30 30-Room 407, Marriott Decision Support Systems I Contributed Session

Joint Session DM/QSR: Quality and Statistical Decision Making in Health Care Applications Sponsor: Data Mining Sponsored Session Chair: Shuai Huang, University of Washington, Dept. of Industrial and Systems Eng., Seattle, WA, United States of America, shuai.huang.ie@gmail.com 1 - Social Media Analytics for the Promotion of Mental Health Qingpeng Zhang, Assistant Professor, City University of Hong Kong, Kowloon, Hong Kong - PRC, qingpeng.zhang@cityu.edu.hk The digital footprints of Web users left on social media present important mental health proxies. In this work, we aim to characterize the dynamics of the online social groups for the mutual help of people suffering from depression. We identified unique features in both language and social interaction patterns, and interesting relationship between the two, which could have important implications of the causes and factors of depression. 2 - Adaptive Cluster-based Oversampling Method: Application to Gynecological Surgery Failure Prediction Iman Nekooeimehr, PhD Candidate, University of South Florida, 14304 Wedgewood Ct., Apt. 201, Tampa, FL, 33613, United States of America, nekooeimehr@mail.usf.edu, Stuart Hart, Allison Wyman, Susana Lai-yuen A new oversampling method called Adaptive Semi-Unsupervised Weighted Oversampling is presented for imbalanced dataset classification. It is adaptive, and avoids overgeneralization and overfitting. The method was used with Support Vector Machines to predict surgical failure after gynecological repair operations. Results show 76% weighted accuracy and improvement over other oversampling methods. 3 - Real-time Detection of System Change Points via Graph Theoretic Sensor Fusion Prahalad Rao, SUNY Binghamton, 4400 Vestal Pkwy. E, Binghamton, NY, United States of America, prao@binghamton.edu, Chou-An Chou, Samie Tootooni We propose a novel graph theoretic approach for detection of system change points from multidimensional sensor data. The approach is based on transform time series data into an un-weighted and undirected planar graph, and subsequently extracting topological invariants. This approach outperforms conventional statistics-based monitoring techniques. We demonstrate the effectiveness of the approach based on experimentally acquired sensor data from advanced manufacturing processes and healthcare. 4 - High-throughput Screening for Rule Discovery from High- dimensional Datasets Mona Haghighi, University of South Florida, 3202 E Fowler Avenue, Tampa, FL, 33620, United States of America, monahaghighi@mail.usf.edu, Shuai Huang, Xiaoning Qian, Bo Zeng We propose a rule-based methodology to Identify risk-predictive baseline patterns of Alzheimer’s disease through a network-based mathematical model. We apply data-mining techniques to reduce dimensionality while taking care of synergistic interaction of variables. Selecting a set of rules to monitor the progression of the disease is the second part of this study.

TC32 32-Room 409, Marriott Decision Support Systems for Data Mining Contributed Session

Chair: Zhiguo Zhu, Associate Prof., Dongbei University of Finance and Economics, No. 217 JianShan St., Shahekou District, Dalian, 110625, China, zhuzg0628@126.com 1 - A Mixture Method of Multivariate Time Series Clustering Cheng-Bang Chen, Penn State University, 233 Leonhard Building, University Park, PA, 16802, United States of America, czc184@psu.edu, Soundar Kumara Time series clustering is widely used in different domains. Although much literature is available on time series clustering, only a few articles relate to multivariate time series clustering. This research developed a clustering methodology and applies different similarity/dissimilarity measures to multivariate time series datasets. It can reduce the data size and has good clustering performance.

328