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SA08 4 - Predictive Modeling of Glycosylation Modulation Dynamics in Cardiac Electrical Signaling Hui Yang, Assistant Professor, University of South Florida, Tampa, FL, 33620, United States of America, huiyang@usf.edu The cardiac action potential is produced by the orchestrated functions of ion channel dynamics. This investigation is aimed at modeling the variations of cardiac electrical signaling due to remodeling of a K+ channels. This multi-scale modeling investigation reveals novel mechanisms of hERG channel modulation by regulated glycosylation that also impact cardiac myocyte and tissue functions. It can potentially lead to new pharmaceutical treatments and drug designs for cardiac arrhythmia. ■ SA08 08- West 104 B- CC Inventory Management for Global Health Sponsor: Public Programs, Service and Needs Sponsored Session Chair: Jeremie Gallien, London Business School, Regent’s Park, London, NW14SA, United Kingdom, jgallien@london.edu 1 - Improving the Public Distribution of Essential Medicines in Sub-Saharan Africa: The Case of Zambia Zachary Leung, Massachusetts Institute of Technology, 77 Massachusetts Ave., Cambridge, MA, United States of America, zacleung@mit.edu, Jeremie Gallien, Prashant Yadav Despite remarkable and successful improvements efforts by the government and its partners, the current public distribution system of essential medical drugs in Zambia still results in low availability to patients relative to private sector standards. We present an alternative design involving mobile devices and optimization and evaluate this proposal via a simulation model built with field data. Our results suggest that this proposal would improve drug availability and reduce inventory costs. 2 - Malaria Treatment Distribution in Developing World Health Systems and Application to Malawi Hoda Parvin, Research Analyst, CNA, 4825 Mark Center Dr., Alexandria, VA, 22311, United States of America, parvinh@cna.org, Shervin AhmadBeygi, Mark Van Oyen, Peter Larson, Jonathan Helm We present stochastic models to address malaria treatment distribution in developing world under demand uncertainty. We first analyze a two-stage stochastic programming approach to make aggregate-level nation-wide decisions. We then present a Markov decision model to develop operational-level transshipment strategies within a cluster of clinics. 3 - Public Health Impact of the Global Fund’s Performance-based Financing Process: A Queueing Analysis Iva Rashkova, London Business School, London, United Kingdom, irashkova.phd2009@london.edu, Jeremie Gallien, Prashant Yadav The Global Fund is the largest financier of programs against HIV, Malaria and Tuberculosis. We study its disbursement process through (i) an econometric analysis of disbursement and procurement data between 2003 and 2012; and (ii) a queueing model predicting national stock levels of medicines. This model quantifies the impact of the current process on central drug availability in 48 African countries and potential interventions that include bridge financing and an international buffer stock. 4 - Inventory Management in Humanitarian Supply Chains: The Role of Schedules and Uncertainty in Funding Karthik V. Natarajan, University of North Carolina, Chapel Hill, NC, United States of America, karthik_natarajan@unc.edu, Jayashankar M. Swaminathan Motivated by the ready-to-use therapeutic food (RUTF) supply chain in Africa, we study the problem of managing inventory of a nutritional product in the presence of variable funding constraints. We derive the structure of the optimal inventory and allocation policy and computationally analyze the impact of different funding schedules on the operating costs andwaiting times in the system. INFORMS Phoenix – 2012 58 ■ SA09 09- West 105 A- CC Evolutionary Multi-Criterion Optimization - I Sponsor: Multiple Criteria Decision Making Sponsored Session Chair: Kalyanmoy Deb, Professor, Indian Institute of Technology Kanpur, Department of Mechanical Engineering, Kanpur, UP, 208016, India, deb@iitk.ac.in 1 - A Decentralized Bicriteria Timeshare Exchange Algorithm Bahriye Cesaret, PhD Student, Jindal School of Management, University of Texas at Dallas, 800 West Campbell Road, Richardson, TX, 75080, United States of America, bahriye.cesaret@utdallas.edu, Milind Dawande, Tharanga Rajapakshe Timeshare Exchange refers to the trading of vacation timeshare weeks among owners, so that they can interchange their respective vacation homes and thereby experience new destinations. We consider two objectives to capture the notions of “efficiency” and “fairness” in an exchange solution. Our main contributions include (i) a structural analysis of the resulting bicriteria timeshare exchange problem and (ii) a polynomial-time, decentralized algorithm for “good” bicriteria solutions. 2 - Cooperative Surrogate Models Improving Multi-objective Evolutionary Algorithms Saúl Zapotecas Martìnez, CINVESTAV-IPN, Mexico, D.F., Mexico, saul.zapotecas@gmail.com, Carlos A. Coello Coello We present a multi-objective evolutionary algorithm (MOEA) assisted by two different meta-models: a local and a global one. The global model is constructed by using different surrogate models. The local model is generated by using the solutions in the current population of the MOEA. The training set for each model is updated along the search of the MOEA. The two different predicted values of the functions provided by the meta-models are used to estimate the final value of each function. 3 - Diversity Enhancing Evolutionary Multiobjective Search Brian Piper, North Carolina State University, 2500 Stinson Drive, Raleigh, NC, United States of America, bepiper@ncsu.edu, Ranji Ranjithan, Hana Chmielewski Besides obtaining Pareto-optimal solutions, decision diversity is an important consideration in real-world multiobjective problems. Diversity can be defined as both the spread of solutions in the decision space and degree to which alternative solutions are available. We present algorithms for obtaining diverse Paretooptimal sets and compare results for several test problems to demonstrate the effectiveness of the algorithms. 4 - Satisfying Multiple Objectives in Infrastructure Management Planning Hana Chmielewski, North Carolina State University, Campus Box 7908, Raleigh, NC, United States of America, htchmiel@ncsu.edu, Ranji Ranjithan Evolutionary algorithms lend themselves to solving cross-disciplinary problems with multiple objectives. In infrastructure planning, for example, it may be useful to consider the resource and performance objectives of engineers, land-use planners, and emergency managers. An algorithm that offers maximally different non-inferior management plans by preserving the diversity of solutions in the decision space is applied to aid decision-making inwastewater treatment planning. ■ SA10 10- West 105 B- CC Risk in Stochastic Programming Sponsor: Optimization/Stochastic Programming Sponsored Session Chair: Sitki Gulten, PhD Candidate, Rutgers University, Department of Management Science & Infor, 1 Washington Park, Newark, NJ, 07102, United States of America, sgulten@rutgers.edu 1 - Two-stage Portfolio Optimization with Higher-order Conditional Measures of Risk Sitki Gulten, PhD Candidate, Rutgers University, Department of Management Science & Infor, 1 Washington Park, Newark, NJ, 07102, United States of America, sgulten@rutgers.edu, Andrzej Ruszczynski Optimization of a portfolio with an option to rebalance over multiple periods from is a major problem in modern portfolio theory. In this research, different forward and backward scenario tree generation techniques are applied on multivariate GARCH-generated scenarios to construct scenario trees. Next, mean-
semideviation risk function with higher-orders is used to formulate the two-stage portfolio problem in which there are two periods and an option to rebalance between those two periods. 2 - Complexity of Bilevel Coherent Risk Programming Jonathan Eckstein, Rutgers University, 640 Bartholomew Road, Piscataway, NJ, 08854, United States of America, jeckstei@rci.rutgers.edu We describe a bilevel programming approach to applying coherent risk measures to three-stage stochastic programming problems. This formulation technique avoids the time-inconsistency difficulties of naive models and the incomposability issues causing time-consistent formulations to have complicated, hard-to-explain objective functions. Unfortunately, we show that such bilevel formulations, when using the standard mean-semideviation and average-value-at-risk measures, are NP-hard. 3 - Scenario Generation and Reduction Applied to Long-term Power Generation Expansion Planning with Risk Yonghan Feng, Iowa State Univseristy, 3024 Black Engr, Ames, IA, United States of America, yhfeng@iastate.edu, Sarah Ryan A stochastic program minimizes the combined expected cost and CVaR of long term power generation expansion planning. Scenarios are generated from stochastic process models of demand and natural gas price and then reduced by a new heuristic called forward selection in wait-and-see clusters (FSWC). Numerical results in a twenty-year case study indicate substantial computational savings while identifying similar solutions as those obtained by applying forward selection alone. ■ SA11 11- West 105 C- CC Advances in Stochastic Programming Contributed Session Chair: Alper Murat, Assistant Professor, Wayne State University, 4815 4th Street, Detroit, MI, 48202, United States of America, amurat@wayne.edu 1 - Simulation-based Two-stage Stochastic Programming with Recourse and Endogenous Uncertainty Tahir Ekin, PhD Candidate, The George Washington University Department of Decision Sciences, 2201 G St Funger Hall 415 NW, Washington, DC, 20052, United States of America, ekin@gwu.edu, Refik Soyer, Nicholas Polson We consider two stage stochastic recourse problems with decision dependent (endogenous) uncertainty. We develop an augmented probability simulation approach via Markov Chain Monte Carlo methods. Our approach is illustrated with a production planning problem involving continuous uncertainty and continuous first stage decision variables. 2 - A New Algorithm for Moment-matching Scenario Generation with Application to Financial Optimization Diana Roman, Brunel University, Uxbridge, London, United Kingdom, diana.roman@brunel.ac.uk, Paresh Date, Ksenia Ponomoreva This method produces scenarios that match exactly the given first moment, covariance matrix, average marginal skewness and kurtosis of a random vector. Unlike previous approaches, it does not use optimization in the scenario generation process; thus it is computationally much simpler. We generate scenarios for a portfolio optimization model. Desirable properties are satisfied, e.g. in and out-of-sample stability. Optimal solutions vary only marginally with increasing number of scenarios. 3 - Progressively Hedged Sample Average Approximation for Two-stage Stochastic Programs Alper Murat, Assistant Professor, Wayne State University, 4815 4th Street, Detroit, MI, 48202, United States of America, amurat@wayne.edu, Nezir Aydin We present a novel hybrid heuristic method (PHSAA) for solving two-stage stochastic programs which integrates deterministic Progressive Hedging method with Sample Average Approximation. PHSAA improves over SAA by exploiting sample solution information and reducing the need to use large sample sizes. PHSAA is significantly more efficient than SAA while attaining same or better solution quality than SAA. We report on extensive results using capacitated reliable facility location problem (CRFLP). INFORMS Phoenix – 2012 59 ■ SA12 SA13 12- West 106 A- CC Recent Advances in Integer Programming Sponsor: Optimization/Integer Programming Sponsored Session Chair: Sebastian Pokutta, Friedrich-Alexander-Universitat Erlangen-Nürnberg, Cauerstrasse 11, Erlangen, 91058, Germany, sebastian.pokutta@me.com 1 - Solving Mixed-Integer Semidefinite Programs Lars Schewe, Dr., FAU Erlangen-Nürnberg, Cauerstr. 11, Erlangen, 91058, Germany, lars.schewe@math.uni-erlangen.de, Sonja Mars We present practical solution approaches to solve mixed-integer semidefinite programs: We show that by integrating standard semidefinite solvers into the mixed-integer solver SCIP, we can harness standard methods from integer programming. We present computational results on a variety of instances. 2 - Comparing Symmetry Breaking Techniques James Ostrowski, University of Tennessee, 1425 S. Stadium Dr, Knoxville, TN, 37996, United States of America, jostrows@utk.edu There are a variety of ways to break symmetry in an MILP problem. We will discuss several of these methods, and test them on a variety of highly symmetric problems. 3 - The Structure of Reduced Bases for Kernel Lattices Karen Aardal, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, Netherlands, K.I.Aardal@tudelft.nl, Frederik von Heymann, Andrea Lodi We consider the structure of reduced bases for lattices consisting of all integer vectors belonging to the null space of a positive integer vector. We observe that when the length is measured as the Euclidean distance, as in the standard implementation of the LLL algorithm, then the reduced basis has an Identity matrix as a submatrix. We provide a theoretical analysis, implications, and computational illustrations. 4 - The Structure of Reduced Bases for Kernel Lattices: Theoretical Analysis Frederik von Heymann, Delft University of Technology, Mekelweg 4, Delft, 2628 CD, Netherlands, f.j.vonheymann@tudelft.nl, Karen Aardal, Andrea Lodi, Laurence Wolsey In this presentation we continue on the topic of the previous talk. Here we present the main ingredients of the theoretical analysis and give some computational examples. ■ SA13 13- West 106 B- CC Stochastic Integer Programming Applications Sponsor: Optimization/Linear Programming and Complementarity Sponsored Session Chair: Burak Buke, The University of Edinburgh, School of Mathematics, King’s Buildings, JCMB, Edinburgh, United Kingdom, B.Buke@ed.ac.uk 1 - A Novel Scenario Reduction Method for Two-stage Stochastic Programs Ali Koc, IBM Watson Research Center, 1101 Kitchawan Road, Yorktown Heights, NY, 10598, United States of America, akoc@us.ibm.com We present a novel scenario reduction method for two-stage stochastic programs. The objective is to minimize the bound on the worst case performance of the optimal value of the reduced problem under the original problem. The method considers both the uncertainty and the underlying optimization problem, as opposed the methods that just consider the uncertainty. We show that both the theoretical bounds and the numerical results obtained by the method are tighter than the methods in literature. 2 - Reclaimed Water Network Design via Stochastic Integer Programming Weini Zhang, Systems and Industrial Engineering, University of Arizona, Tucson, AZ, 85721, United States of America, wzhang@email.arizona.edu, Guzin Bayraksan, Gunhui Chung, Kevin Lansey We model a reclaimed water distribution network for supplying treated water to the public users or agricultural areas using two-stage stochastic binary programming with random recourse. Both construction and energy costs expanded are considered. The network has spatial growth and reclaimed water demands are uncertain. A specialized algorithm is developed to exploit the
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