2015 Informs Annual Meeting

MB19

INFORMS Philadelphia – 2015

MB20 20-Franklin 10, Marriott Decision Analytics in Cloud Cluster: Cloud Computing Invited Session Chair: Yue Tan, The Ohio State University, 1971 Neil Ave, Columbus, OH, 43210, United States of America, tanyue01@gmail.com 1 - Cyber Vulnerability Maintenance Policies for Universities Chengjun Hou, Graduate Research Associate, The Ohio State University, 1971 Neil Ave., Columbus, Oh, 43210, United States of America, hou.91@buckeyemail.osu.edu, Theodore Allen The case study application of Markov decision processes and generalizations to a real world University policy design problem are described. Related mathematical issues are briefly explored. The derived policy includes incentives for not continuing the use of vulnerable software. The magnitude of saving in dollars is estimated. 2 - throughput Scalability of Fork-join Queueing Networks with Blocking Yun Zeng, The Ohio State Univesity, 1971 Neil Ave, Columbus, OH, United States of America, zeng.153@buckeyemail.osu.edu, Augustin Chaintreau, Don Towsley, Cathy Xia With emerging applications such as cloud computing and big data analytics, modern information networks are growing increasingly complex. A critical issue concerns the throughput performance as the system expands to large scale. This paper models the distributed information processing systems as fork-join queueing networks with blocking. We present necessary and sufficient conditions to ensure throughput scalability. Algorithms to check these features for given networks are proposed. 3 - Data-driven Decision Making via Adaptive Control for Cyber Password Management Yue Tan, The Ohio State University, 1971 Neil Ave, Columbus, OH, 43210, United States of America, tanyue01@gmail.com, Cathy Xia Cyber attacks have been widely recognized as a major international and domestic cyber security threat. Although an increasing number of high technology mechanisms have been developed, passwords remain as the frontline against cyber attacks both for personal and organizational security settings in cloud services. In this talk, we present a data-driven adaptive control framework that converges to the optimal password expiry duration which balances between accounts safety and user experience. MB21 21-Franklin 11, Marriott Re-Designing the (US) Healthcare System Sponsor: Health Applications Sponsored Session Chair: Aurelie Thiele, Lehigh University, 200 W Packer Ave, Bethlehem, PA, 18015, United States of America, aut204@lehigh.edu 1 - Designing Narrow Network Plans for Healthcare: A Bi-objective Optimization Approach We build a quantitative decision model for healthcare payers willing to offer Narrow Network (NN) plans to customers. NN have received significant attention in the implementation of the Affordable Care Act. A payer selects a limited list among all possible providers, and steers patients to these providers by limiting coverage to this list. Our research question is: how to select a limited number of providers so as to reduce the cost for the payer without decreasing the utility for customers? 2 - The Effects of Ambulatory Surgery Centers on Hospital’s Financial Performance Victoire Denoyel, ESSEC Business School, Avenue Bernard Hirsch, Cergy, 95000, France, victoire.denoyel@essec.edu, Aurelie Thiele, Laurent Alfandari

4 - Applications of Big Data Summarization through Polyhedral Uncertainty Sets Anushka Chandrababu, Research Scholar, IIITB, 26/C, Electronic City, Bangalore, India, anushka.babu@iiitb.org, Prasanna Gns We present our works of summarizing structured or unstructured big data into polyhedral uncertainty sets, orders of magnitude smaller than the original data using a generalized multi-dimensional German tank method. Relational algebraic operations to check disjointness, subset or intersecting relationships between such polyhedral objects can be performed. We show the results of such big data summarization using real world data to solve specific business needs. 5 - Assessing Demand Trends using Real Time Order Transaction Data Parvaneh Jahani, University of Louisville, 781 Theodore Burnett Ct., Apt. 2, Louisville, KY, 40217, United States of America, p0jaha01@louisville.edu, Suraj Alexander Assessing demand trends using real time order transaction data is essential aspect of warehouse management system. Selecting the method of demand forecasting differs for different demand trends. We propose a new approach for classification of Stock Keeping Units (SKUs) demand trends using Control Charts Pattern Recognition (CCPR). After demand trend class recognition, the best method of forecasting is selected. Bootstrapping method is used for forecasting intermittent demand time series. 6 - Unsupervised Ensemble, or Consensus Clustering, Consists in Finding the Optimal Combination Strategy Ramazan Ünlö, University of Central Florida, 12100 Sterling University Ln, Apt. 2-2419, Orlando, FL, United States of America, ramazanunlu@gmail.com Unsupervised ensemble, or Consensus clustering, consists in finding the optimal combination strategy of individual clusterings that is robust with respect to the selection of the algorithmic clustering pool. In this paper, we propose a weighting policy for this problem that is based on internal clustering quality measures and compare against other popular approaches. MB19 19-Franklin 9, Marriott OR and AI Sponsor: Computing Society Sponsored Session Chair: Scott Sanner, Asst. Professor, Oregon State University, 1148 Christian Tjandraatmadja, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, United States of America, ctjandra@andrew.cmu.edu, Willem-jan Van Hoeve Many enumerative techniques to solve discrete optimization problems benefit greatly from using bounds to prune the search tree. We study the application of pruning strategies to decision diagrams, which can be viewed as a compact form of enumeration trees. In particular, we discuss how pruning strategies can be incorporated in relaxed and restricted decision diagrams to obtain improved primal and dual bounds. 2 - Concise Representation of Near-optimal Solutions with Decision Diagrams Thiago Serra, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, 15213, United States of America, tserra@cmu.edu, John Hooker Decision diagrams have recently been used to compactly encode sets of solutions to discrete optimization problems. In this talk we study Sound Decision Diagrams (SDDs), which encode near-optimal solutions along with worse feasible and infeasible solutions. We provide a formal characterization of SDDs and algorithms to find those with minimum size. Empirical results show that SDDs are smaller than conventional decision diagrams representing the same near-optimal solution set as its gap increases. 3 - Stochastic Optimization of the Scheduling of a Radiotherapy Center Antoine Legrain, Polytechnique Montreal, C.P. 6079, Succursale Centre-ville, Montreal, QC, H3C 3A7, Canada, antoine.legrain@polymtl.ca, Marie-andrée Fortin, Nadia Lahrichi, Louis-Martin Rousseau, Marino Widmer Radiotherapy centers can improve their efficiency by optimizing the utilization of the linear accelerators. We propose an online method to schedule patients on such machines taking into account their priority, the maximum waiting time, and the preparation of this treatment (dosimetry). We have implemented a genetic algorithm and a constraints program, which schedule the dosimetry. This approach ensures the beginning of the treatment on time and thus avoids the cancellation of treatment sessions. Kelley Engineering Center, Corvallis, OR, 97331, United States of America, ssanner@gmail.com 1 - Pruning in Decision Diagrams for Optimization

Cheng Wang, Lehigh University, 621 Taylor Street, Department of Economics, Bethlehem, PA, 18015, United States of America, chw410@lehigh.edu

Ambulatory surgery centers (ASCs) , which treat surgical patients who do not need an overnight stay, are a health care service innovation that has proliferated in the U.S. in the past four decades. This paper examines the effect of ASCs on the net patient revenues and total operating costs of hospitals. Overall, results suggest that ASCs are competitors to general hospitals.

178