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SB19 ■ SB19 19- West 211 A- CC Joint Session Healthcare Logistics/SPPSN: Routing Problems in Healthcare Operations I Cluster: Healthcare Logistics & Public Programs, Service and Needs Invited Session Chair: Burcu Keskin, University of Alabama, Alston Hall, Box: 870226, Tuscaloosa, AL, 3587-0226, United States of America, bkeskin@cba.ua.edu 1 - A Sequential GRASP for the Therapist Routing and Scheduling Problem Yufen Shao, Research Engineer, ExxonMobil Upstream Research Company, 3120 Buffalo Speedway, Houston, TX, 77098, United States of America, yufen.shao@exxonmobil.com, Ahmad Jarrah, Jonathan Bard This talk presents a sequential GRASP for solving a weekly routing and scheduling problem for therapists. The problem contains both fixed and flexible patients with respect to appointment times, and two grades of therapists. In Phase I, feasible solutions are constructed one therapist and one day at a time. In Phase II, a high-level neighborhood search is proposed to obtain local optima. Performance is demonstrated using real and randomly generated data sets. 2 - An Optimisation Model for Staff Planning in a Home Care Organization Pablo Andres Maya Duque, PhD, University of Antwerp/ Universidad de Antioquia, Ottoveniusstrat 26, box 6, Antwerp, 2000, Belgium, pmayaduque@gmail.com, Peter Goos, Kenneth Sörensen, Marco Castro In this talk, we present the core optimization component of a decision support system that Landelijke Thuiszorg, a non-profit organization that provides home care services for several provinces in Belgium, will implement in order to assist the regional service planning. The optimization model takes into account assignment, scheduling and routing decisions simultaneously, while considering two objectives, namely the service level and the travelled distance. 3 - Incorporating Patient, Nurse, and Agency Considerations in Home Health Care Routing Ashlea Bennett Milburn, Assistant Professor, University of Arkansas, 4207 Bell Engineering Center, Fayetteville, AR, United States of America, ashlea@uark.edu, Jessica Spicer Home health routing and scheduling problems can be modeled as multi-objective optimization problems, as home health agencies are often interested in creating nurse routes that achieve a variety of goals. We use a multi-objective tabu search heuristic to study the relationship among travel cost, nurse consistency (a measure of patient satisfaction), and balanced workload (an indicator of nurse satisfaction) objectives. Computational results for a number of realistic scenarios are presented. 4 - A Multi-period Home Care Scheduling Problem with Work Balance Burcu Keskin, University of Alabama, Alston Hall, Box: 870226, Tuscaloosa, AL, 3587-0226, United States of America, bkeskin@cba.ua.edu, Shirley (Rong) Li, Charles Schmidt We consider a home health care scheduling problem for a local hospital with multiple types of caregivers. We determine the assignment of caregivers to the patients visited at their homes so that the total routing costs are minimized and the workload of caregivers are balanced over a planning horizon while satisfying synchronization, precedence, loyalty, and other practical constraints. We present a MILP formulation and a branch-and-price solution approach based on Dantzig- Wolfe decomposition. ■ SB20 20- West 211B- CC Software Demonstration Invited Session 1 - American Optimal Decisions - Portfolio Safeguard (PSG): Advanced Nonlinear Mixed-Integer Optimization Package Stan Uryasev,American Optimal Decisions, 5214 SW 91 Way, Ste. #130, Gainesville FL 32608, United States of America, uryasev@aorda.com Portfolio Safeguard is an advanced nonlinear mixed-integer optimization package used in risk management, financial engineering, military, medical and other applications. Design and solve complex optimization problems with built-in functions (maximum, StDev, variance, probability, VaR, CVaR, cardinality, fixedcharge, recourse etc.). See real-life case studies in Windows and MATLAB at www.aorda.com/aod/psg.action. INFORMS Phoenix – 2012 86 ■ SB21 21- West 212 A- CC Advances in Networks and Graphs Contributed Session Chair: Sivan Altinakar, École Polytechnique de Montréal, C.P. 6079, Succ. Centre-ville, Montreal, QC, H3C 3A7, Canada, sivan.altinakar@gerad.ca 1 - Approximating Precedence Network Structure with Incomplete Information Adam Graunke, The Boeing Company, P.O. Box 3707, MC 13-98, Seattle, WA, 98124, United States of America, adam.a.graunke@boeing.com, Gabriel Burnett Accurate precedence networks are highly useful for planning and analysis, yet in large scale production systems, they are difficult, if not impossible, to define. In this research we investigate precedence networks in the presence of incomplete precedence information, with the goal of estimating production-critical properties of the network. Specifically, we investigate critical path analyses and the level of confidence associated with the results. 2 - Shortest Path with Secure Multipary Computation Abdelrahaman Aly, Universite Catholique de Louvain, 34, Voie du Roman Pays, Louvain-la-Neuve, 1348, Belgium, abdelrahaman.aly@uclouvain.be, Mathieu Van Vyve In various applications, i.e. elections, auctions, the computation of a global optimum requires input data from competing parties. A trusted third party to perform these computations is not guaranteed. Secure Multiparty Computation (SMC) is an encryption method which does not rely on such third party. The present research expands SMC’s scenario to Shortest Path problem. We describe SMC variants of Bellman-Ford and Dijkstra algorithms and compare their performance with the traditional variants. 3 - Reconstruction of Three Dimensional Objects from Three Orthogonal Cartesian Bi-plane Projections Siddhartha Sampath, Arizona State University, 849 W Elna Rae, Tempe, AZ, United States of America, Siddhartha.Sampath@asu.edu, Pavithra Ramamoorthy, Pitu Mirchandani This application describes a network flow model for reconstructing the threedimensional shape of a three dimensional object from biplane x-ray readings for all three orthogonal Cartesian planes.Each two-dimensional cross-section consists of one region or view of the object whose image is to be reconstructed.We can use apriori information to constrain the number of solutions, and then to obtain most likely reconstruction. 4 - Breaking Symmetry in Consecutive Edge-coloring Sivan Altinakar, École Polytechnique de Montreal, C.P. 6079, Succ. Centre-ville, Montreal, QC, H3C 3A7, Canada, sivan.altinakar@gerad.ca, Alain Hertz, Gilles Caporossi Consecutive edge-coloring, a special case of edge-coloring in graph theory, aims to minimize the sum of the span of colors (integers) incident to each node. This has applications in scheduling. It is also NP-hard, and may be difficult to solve exactly even for a very small number of vertices. We compare different modeling approaches in Mixed Integer Programming and Constraint Programming, with an emphasis on the latter, and subsets of Lex-Leader constraints for efficient symmetry breaking. ■ SB22 22- West 212 B- CC COIN-OR Multi-Threading Software Sponsor: Computing Society Sponsored Session Chair: Bradley Bell, Senior Research Scientist/Enginer, University of Washington, IHME, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, United States of America, bradbell@seanet.com 1 - Could We Use a Million Cores to Solve a Single Integer Program? Ted Ralphs, Associate Professor, Lehigh University, 200 West Packer Avenue, Bethlehem, PA, 18015, United States of America, ted@lehigh.edu, Thorsten Koch, Yuji Shinano Given the steady increase in cores per CPU, it is only a matter of time before supercomputers will have a million or more cores. In this talk, we investigate the opportunities and challenges that will arise when trying to utilize the distributed multi-core architectures that have recently become pervasive to solve a single integer linear optimization problem. We raise the question whether best practices in sequential solution of ILPs will be effective in massively parallel environments.
2 - A Multi-core Benchmark used to Improve Algorithmic Differentiation Bradley Bell, Senior Research Scientist/Enginer, University of Washington, IHME, 2301 Fifth Ave., Suite 600, Seattle, WA, 98121, United States of America, bradbell@seanet.com We present an application of Newton’s method that finds all the zeros of a nonlinear function in an interval. This is being used as a benchmark to improve the multi-core performance of the COIN-OR open source AD software package CppAD. Different threading systems, and different versions of this software package, are easily compared for speed of execution. As an example of the use of this benchmark, we compare the results for three versions of the software package. 3 - Parallelization of a Primal-dual IPM for SDP using OpenMP Brian Borchers, New Mexico Tech, Mathematics Department, 801 Leroy Place, Socorro, NM, 87801, United States of America, borchers@nmt.edu CSDP is software package for semidefinite programming that uses a primal-dual interior point method. We discuss how the software was parallelized using OpenMP, and discuss the parallel performance of the algorithm on problems of varying size. 4 - Application of SIMD Instructions in Solving Random 3-dimensional Assignment Problems Mohammad Mirghorbani, The University of Iowa, 3131 Seamans Center, Iowa City, IA, 52242, United States of America, seyedmohamma-mirghorbaninokandeh@uiowa.edu, Pavlo Krokhmal We show how SIMD instructions can be utilized to vectorize an existing branch and bound method, BitCLQ, designed to enumerate k-cliques in special k-partite graphs. The k-cliques found map to guaranteed high-quality solutions of random 3-dimensional assignment problems. We observed up to 100% improvement in the performance of BitCLQ on the same machine after vectorization. ■ SB23 23- West 212 C- CC Edelman 2012 Finalists II Sponsor: CPMS, The Practice Section Sponsored Session Chair: Stephen Graves, Massachusetts Institute of Technology, Sloan School of Management, 77 Massachusetts Avenue, Cambridge, MA, 02139, United States of America, sgraves@MIT.EDU 1 - Carlson Rezidor Hotel Group Maximizes Revenue with Improved Demand Management and Price Optimization Pelin Pekgun, Assistant Professor, University of South Carolina, Moore School of Business, 1705 College Street, Columbia, SC, 29208, United States of America, Pelin.Pekgun@moore.sc.edu, Suresh Acharya, Kathleen Mallery, Kyle Christianson, Ronald Menich, Phillip Finch, Frederic Deschamps, James Van Sistine, James Fuller Carlson Rezidor Hotel Group collaborated with JDA Software Group on a highly innovative revenue optimization project that started with enterprise demand forecasting in 2007. It was followed by a large-scale network optimization solution to dynamically optimize stay night rates based on price elasticity, competitor rates, remaining inventory, demand forecast and business rules. Compliant hotels consistently showed a 2-4% revenue improvement,and increased revenue by more than $16 million annually. 2 - Optimizing Capital Investment Decisions at Intel Corporation Feryal Erhun, Stanford University, Stanford, CA, United States of America, ferhun@stanford.edu, Karl Kempf, Erik Hertzler, Timothy Rosenberg, Chen Peng Intel spends over $5B annually on manufacturing equipment. With increasing lead times from equipment suppliers and increasing difficulty in forecasting market demand, optimizing capital investment decisions is a significant managerial challenge. We describe the Velocity Program and the DMEP framework, which have benefited Intel hundreds of millions of dollars in documented cost savings and at least $2B in revenue upside for a manufacturing process transition during a period of economic crisis. INFORMS Phoenix – 2012 87 ■ SB24 SB24 24- West 213 A- CC Joint Session HAS/SPPSN: Pandemic Modeling and Planning Sponsor: Health Applications Society & Public Programs, Service and Needs Sponsored Session Chair: Dionne Aleman, University of Toronto, 5 King’s College Road, Toronto, On, M5S 3G8, Canada, aleman@mie.utoronto.ca 1 - Quantifying the Value of Information for Inferring Contagion Patterns in Social Activity Graphs Lauren Gardner, Lecturer, University of New South Wales, UNSW, Sydney, 2052, Australia, laurengardner84@gmail.com, David Fajardo The model presented here seeks to predict the infection pattern which depicts the current state of the network for an ongoing outbreak of a communicable disease. This is accomplished by inferring the most likely path of infection through a contact network under the assumption of partially available infection data. The problem is formulated as a bi-linear integer program, and heuristic solution methods are developed based on iteration of two sub-problems which can be solved much more efficiently. 2 - Developing Non-pharmaceutical Intervention Strategies for Pandemic Influenza Mitigation Tapas Das, Professor, Department of Industrial and Management Systems Engineering, University of South Florida, Industrial Engineering, Tampa, FL, United States of America, das@usf.edu, Dayna Martinez In this paper we examine the interactions between the parameters of virus epidemiology, social behavior, and non-pharmaceutical interventions (NPIs). This examination is conducted using an agent-based simulation model and designed statistical experiments. Results are used to design effective NPI strategies for pandemic influenza mitigation. Some numerical results are presented for various virus transmissibility scenarios. 3 - Evaluation of Strategies to Mitigate Contagion Spread using Social Network Characteristics Mario Ventresca, University of Toronto, 5 King’s College Road, Toronto, On, M5S 3G8, Canada, mventresca@gmail.com, Dionne Aleman We study the effects of graph-based mitigation strategies with respect to their ability to contain disease spread. Evaluation is performed on a social network constructed from census data of the Greater Toronto Area of 5 million individuals. One outcome of the analysis highlights the pitfalls of betweenness and centrality measures for maximum mitigation given an unknown initial spreader. Our analysis focuses on pandemic disease, but the approach is applicable to any network contagion. 4 - Prediction of Demand Surges for Immunization During Influenza Pandemics Michael Beeler, University of Toronto, 5 King’s College Road, Toronto, ON, M5S 3G8, Canada, mfbeeler@gmail.com, Michael Carter, Dionne Aleman A time-series model is constructed to predict surges in turnout at mass immunization clinics during influenza pandemics. Variables include media coverage, weather conditions, day of the week, and estimated weekly infection rate. The model is based on Toronto clinic turnout data during the H1N1 pandemic in 2009.
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