2016 INFORMS Annual Meeting Program

TA76

INFORMS Nashville – 2016

TA76 Legends D- Omni

TA77 Legends E- Omni Opt, Integer Programing I Contributed Session Chair: Ed Klotz, IBM, PO Box 4670, Incline Village, NV, 89450, United States, klotz@us.ibm.com 1 - Solving Large Scale Grid-based Location Problems Noor E Alam, MD, Assistant Professor, Northeastern University, 334 SN, 360 Huntington Avenue, Boston, MA, 02115, United States, mnalam@neu.edu, John Doucette This talk will present mathematical models for grid-based location problems (GBLPs) with two case studies. Apart from presenting computational difficulty of the GBLPs, it will also discuss two problem-specific integer linear program (ILP) based decomposition algorithms to solve large-scale instances. 2 - A Mixed-integer Programming Approach To Optimize Typing Method And Design Of Touchscreen Keyboards On Smartphones Mohammad Ali Alamdar Yazdi, PhD Student, Auburn University, 354 W Glenn Ave, Auburn, AL, 36830, United States, mza0052@auburn.edu, Ashkan Negahban, Fadel Mounir Megahed Millions of people use smartphones for different typing purposes. There are significant differences in the design of keyboards for smartphones. Optimization of typing method and improvements in the design of touchscreen keyboards on smartphones is expected to have a significant impact on the total typing time. A MIP model is developed to optimize typing zones for fingers in two-thumb typing with the objective to minimize the total typing time. Through extensive experimentation, different keyboard dimensions are also compared to find the optimal keyboard design. The results shows that the best keyboard design has square keys with minimum possible horizontal and vertical spaces between the keys. 3 - Solution Value Contingent Cuts For Solving Hard Generalized Assignment Problems Robert M Nauss, Professor, University of Missouri - St Louis, 3816 Boca Pointe Dr., Sarasota, FL, 34238, United States, robert_nauss@umsl.edu, Jeremy William North Define hard generalized assignment problems (GAP) to be those that take more than one hour CPU time to prove optimality. Tremendous strides have been made in the capability of “off the shelf” software, such as GUROBI, to solve general integer linear programs (ILP). However, some classes of ILPs remain difficult to solve to optimality in a reasonable amount of time. Certain instances of the GAP exhibit this behavior. While good feasible solutions are found relatively quickly, the issue remains in proving optimality. We introduce some novel cuts (and methods for deriving said cuts) that are applied with an “off the shelf” solver through the use of CALLBACK functions. Computational results are presented. 4 - Improved Analysis Of Infeasible Mixed-integer Linear And Quadratic Programs Ed Klotz, IBM, P.O. Box 4670, Incline Village, NV, 89450, United States, klotz@us.ibm.com, John W Chinneck, Andrew Scherr Analysis of infeasible MILPs and MIQPs is complicated by the integer restrictions (IRs). Current techniques return a minimal infeasible subset of the linear constraints and variable bounds by solving a series of MILPs. They do not find a minimal subset of the IRs, because of the significant additional computational cost. We develop efficient ways to find a minimal subset of the IRs and use this to speed the isolation of a true Irreducible Infeasible Subset for MILPs and MIQPs. This is helpful when a variable is accidentally specified as integer.

Decision Analysis Contributed Session Chair: Jordi Weiss, PhD Student, Unil, Lausanne, 1022, Switzerland, jordi.w@outlook.com 1 - Multi-modal Optimization Of WINTIME As A Game Performance Metric And Rankings Basis With An Application To College Football Christopher Keller, Assistant Professor, East Carolina University, Department of Marketing & Supply Chain, College of Business, Greenville, NC, 27858-4353, United States, kellerc@ecu.edu WINTIME is the elapsed clock time for the winning team’s score to exceed the losing team’s final score. WINTIMES can be used to generate a rating system and an estimated WINTIME. The resulting optimization problem is to minimize the errors between the observed and the estimated WINTIMEs. For college football, the model has 129 variables and is multi-modal. Excel Solver solutions are discussed. Accuracy is comparable to other systems with predictive accuracy above 70% and retrodictive accuracy above 85%. The system could also be applied to other sports like hockey or soccer. 2 - Should I Stay Or Should I Go? The Cognition Of Exploration And Exploitation S.S. Levine, University of Texas, Dallas, TX, 75080, United States, sslevine@gmail.com, Charlotte Reypens In many life situations, people choose sequentially between repeating a past action in expectation of a familiar outcome (exploitation), or choosing a novel action whose outcome is largely uncertain (exploration). For instance, in each quarter, a manager can budget advertising for an existing product, earning a predictable boost in sales. Or she can spend to develop a completely new product, whose prospects are more ambiguous. Using experiments in a lab and a labor market, we examine what affects these decisions. We investigate traits of the decision-makers, such as risk aversion, but also their history. We find that the past matters, greatly: What you experience counts as much as who you are. 3 - A POMDP Model For Personalized Depression Monitoring Jue Gong, Graduate Student, University of Washington, Industrial & Systems Engineering, Box 352650, Seattle, WA, 98195, United States, gongjue@uw.edu, Shan Liu Mitigating depression has become a national health priority as it affects 1 out of 10 American adults. We formulate a partially observable Markov decision process (POMDP) in order to find an optimal monitoring schedule for anindividual patient. The state of the POMDP combines the health state of the patient and the direction of health change. We estimated the transition and emission matrices by extending the Baum-Welch algorithm to include a mixture of multiple transition matrices. We solved the model using the Bellman Equation via dynamic programming algorithm. 4 - Remanufacturing Decisions In A Close-loop Supply Chain With Extended Warranty Options Kunpeng Li, Utah State University, 767 Eagle View Dr., Providence, UT, 84332, United States, kunpeng.li@usu.edu, Yang Li The study addresses the problem of choosing the appropriate reverse channel structure for collecting of used products. We consider a two-echelon supply chain with a single manufacturer and a single retailer. By comparing five different reverse channel formats, we intend to understand how close-loop supply chain structure influences the use-product collection. We also study the impact of extended warranty on the consumption of remanufactured products. 5 - Using Online Games To Develop Manager Intuition About Demand Randomness Jordi Weiss, PhD Student, Unil, Lausanne, 1022, Switzerland, jordi.w@outlook.com, Michael Bean, Suzanne de Treville Quantitative-finance methods applied to the supply chain dramatically improve incorporation of demand risk into supply-chain decisions. Use of these methods is hindered by managers lack of intuition about demand randomness. We use online games to allow managers to apply such methods in the face of randomness arriving from different forecast-evolution processes (instantaneous volatility or jump diffusion). We present the results from using these games at the policy level for three cantons in Switzerland, demonstrating how increased intuition about randomness helps decision makers to consider profitable options that are counterintuitive and non linear.

TA78 Legends F- Omni Opt, Network I Contributed Session

Chair: Seyed Mohammad Nourbakhsh, Sabre, 3150 Sabre Drive, Southlake, TX, 76092, United States, seyed.nourbakhsh@sabre.com 1 - Enhanced Robust Operational Aircraft Routing Seyed Mohammad Nourbakhsh, Sabre, 3150 Sabre Dr, Southlake, TX, 76092, United States, seyed.nourbakhsh@sabre.com, Dong Liang, Xiaodong Luo, Xiaoqing Sun, Sergey Shebalov Enhanced Robust Operational Aircraft Routing (E-ROAR) serves as an airline planning decision support solution that is used as a post-process to the Fleet Assignment Model (FAM) to bridge the chasm between Airline Planning and Airline Operations. E-ROAR considers connections, and maintenance requirements; and provides fleet assignments and through connections feasible for Crew Planning, Maintenance Planning, and Airline Operations. The output from E-ROAR is given to Airline Operations, where tail assignments are made. We propose an advanced solution approach by combining heuristic and optimization methodologies. Computational results demonstrate significant improvement.

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