2016 INFORMS Annual Meeting Program

WB80

INFORMS Nashville – 2016

4 - Biased Randomization: Heuristics In Transportation, Logistics, And Production Alex Grasas, EADA Business School, C/ Arago 204, Barcelona, 08011, Spain, agrasas@eada.edu, Angel A Juan, Javier Faulin, Jesica De Armas, Helena Ramalhinho This paper reviews heuristics that contain biased-randomized procedures (BRPs). A BRP is a procedure that uses a biased probability distribution to select the next constructive movement at each algorithm’s iteration. BRPs can be categorized into two main groups according to how choice probabilities are computed: (i) BRPs using an empirical bias function; and (ii) BRPs using a skewed probability distribution. This paper analyzes the second group and reviews the use of these BRPs in some applications in transportation, logistics, and production problems. 5 - A Parallel Dynamic Programming Solution For The Dynamic Facility Layout Problem We develop a parallel approximate dynamic programming solution to the Dynamic Facility Layout Problem (DFLP) using OpenMP. We experiment with data sets from Dr. Balakrishnan’s repository. Including a relatively small set of feasible solutions, the accuracy and speed of our method is very satisfactory if contrasted to simulated annealing, hybrid genetic algorithm, and ant systems. In the DFLP, the flow of materials between departments is known but it varies over time due to changes in demand and introduction of new products. The trade-off costs are material handling and relocation costs. Clara Novoa, Associate Professor, Texas State University, 601 University Dr, San Marcos, TX, 78666, United States, cn17@txstate.edu, Apan Qasem, Chandra Kolla Chair: Churlzu Lim, Associate Professor, University of North Carolina at Charlotte, Systems Engineering & Engineering Management, 9201 University City Boulevard, Charlotte, NC, 28223, United States, clim2@uncc.edu 1 - A Fast Socp-based Method For Optimal Selection Problem In Tree Breeding Makoto Yamashita, Associate Professor, Tokyo Institute of Technology, W8-29 2-12-1 Oh-Okayama, Meguro, Tokyo, 152-8552, Japan, makoto.yamashita@c.titech.ac.jp, Tim J Mullin, Sena Safarina One of new frontiers for optimization methods is to solve practical problems arising from breeding. A purpose of an optimal selection problem in tree breeding is to determine the contributions of candidate genotypes that attains the highest profit subject to a constraint on genetic diversity. We propose a fast numerical method based on second-order cone programming by exploiting the structural sparsity in the problem. The proposed method reduced the computation time from 39,000 seconds of an existing method to just 2 seconds. 2 - Shape Constrained Data Smoothing With Penalized Splines Yu Xia, Professor, Lakehead University, Business Administration, 955 Oliver Rd, Thunder Bay, ON, P7B 5E1, Canada, yxia@lakeheadu.ca, Farid Alizadeh We consider fitting noisy data to a smooth function by penalized B-splines. The underling function is assumed to have some shape properties, such as non- negative, monotonic, convex. We solve the data smoothing problem by convex optimization methods. 3 - On The Convexity Of Optimal Decentralized Control Problem And Sparsity Path Salar Fattahi, PhD Student, University of California, Berkeley, 4141 Etcheverry Hall, Berkeley, CA 94720-1777, Berkeley, CA, 94702, United States, fattahi@berkeley.edu, Javad Lavaei This talk is about an important special case of the optimal stochastic decentralized control problem, where the objective is to design a static structured controller for a stable stochastic system. We show that if either the noise covariance or the input weighting matrix is not too small, the problem is locally convex. In the case where these conditions are not satisfied, we modify the problem by a penalization term to convexify it, leading to a near-global solution. We also study the problem of designing a sparse controller using a regularization technique. Under some genericity assumptions, we prove that this method is able to design a controller with any arbitrary sparsity level. WB79 Legends G- Omni Opt, Convex Contributed Session

4 - Snug Circumscribing Simplexes For Convex Hulls Ghasemali Salmani Jajaei, PhD Student, Virginia Commonwealth University, 1015 Floyd Avenue, Harris Hall, Richmond, VA, 23284-3083, United States, salmanijajaeg@vcu.edu We propose procedures for enclosing convex hulls of finite m-dimensional point sets with simplexes. These are snug in since they intersect the hull in some way. We report on experimental results. 5 - Volume Allocation Optimization For Space Mission Tasks Churlzu Lim, Associate Professor, University of North Carolina at Charlotte, Systems Engineering & Engineering Management, 9201 University City Boulevard, Charlotte, NC, 28223, United States, clim2@uncc.edu, Simon M Hsiang, Sherry Thaxton, Maijinn Chen, Jerry G Myers Volume in space missions is costly and often must be traded with competing resources and mission needs, such as launch mass, systems/hardware requirements, and consumables. Spacecraft and habitat designers must allocate sufficient volume for different tasks without incurring excessive cost penalties or failure modes. How a volume can be optimized should be based on a balancing of risk and benefits. In this talk, we present a mathematical optimization model that maximizes the total value of tasks of astronauts given a limited volume in the spacecraft. An illustrative example will be demonstrated. WB80 Broadway E- Omni Health Care, Public II Contributed Session Chair: Samantha Meyer, Assistant Professor, Ross School of Business, 701 Tappan Ave., Ann Arbor, MI, 48109, United States, srmeyer@umich.edu 1 - How Much Sleep Do You Need?: Evidence From Public Health Philip F. Musa, Associate Professor and Programs Director, University of Alabama-Birmingham, PO Box 55544, Birmingham, AL, 35255, United States, musa@uab.edu Could the amount of sleep people get be associated with hypertension? This presentation outlines an epidemiological cross-sectional study to shed some light on this important Public Health chronic matter. We present a background from the literature using a population based sampling. Our proposed study will employ the previously validated Pittsburgh Sleep Quality Index and Berlin questionnaire. The inclusion/exclusion criteria and the strengths and limitations are presented. 2 - A Little Empathy Goes A Long Way In Disease Dynamics On A Network Game Ceyhun Eksin, Georgia Tech, 85 5th St. NW, Atlanta, GA, 30332, United States, ceyhuneksin@gatech.edu, Jeff S Shamma, Joshua Weitz Individuals change their behavior during an epidemic in response to whether they and those they interact with are healthy or sick. Healthy individuals may utilize protective measures while sick individuals may adopt preemptive measures to stop disease spread to their contacts. Yet, in practice both protective and preemptive behavior come with costs. We propose a stochastic network disease game that captures the self-interests of individuals during disease spread on a network. We show that there is a critical level of concern, i.e., empathy, by the sick individuals that eradicates the disease fast while the protective measures cannot eradicate the disease without the preemptive measures. 3 - Spatial Evolutionary Game For Changes Of Human Behaviors In Epidemic Songnian Zhao, Kansas State University, 2037 Durland Hall, Manhattan, KS, 66502, United States, songnian@ksu.edu, Yan Kuang, John C Wu, David Ben-Arieh Spatial evolutionary game, used to study multiple players’ behaviors in a spatial structure, was incorporated into the epidemic models for sake of evaluating spontaneous changes of human behaviors when individuals acquire information about the spread of infectious disease and make a tradeoff between costs and benefits. Through the comparison between two different mechanisms, a spatial game model in epidemic is validated to generate the consistent results with the traditional dynamic systems in this paper.

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