2016 INFORMS Annual Meeting Program

WC79

INFORMS Nashville – 2016

WC77 Legends E- Omni Opt, Large Scale I Contributed Session Chair: Gerdus Benade, Carnegie Mellon University, 6235 Fifth Avenue, Apt B17, Pittsburgh, PA, 15232, United States, gerdusbenade@gmail.com 1 - An Agglomerative Clustering Based Large-Scale Minimum Cost Flow Algorithm Yuan Zhou, PhD Candidate, University of Memphis, 174 Windover Cove, Apt 3, Memphis, TN, 38111, United States, yuanzhou0924@gmail.com, Stephanie Ivey When the size of an input network exceeds computer hardware capabilities, minimum cost flow (MCF) becomes problematic in terms of computational and memory requirements. This research focuses on the large-scale MCF problem by adopting a divide-and-conquer policy during the optimization process. We propose an agglomerative clustering based tiling strategy which can ensure consistency between local and global optimum solutions, decomposes the input network into sub-networks, and then solves MCF in each sub-network independently to reduce peak memory consumption and improve efficiency. 2 - Large Scale Scheduling Problems On Internet Of Things Carlos Eduardo De Andrade, Senior Inventive Scientist, AT&T Labs Research, 200 S Laurel Avenue, Room A5-1E33, Middletown, NJ, 07748, United States, cea@research.att.com The number of connected devices has been increasing exponentially over past years. Such devices do not only use the network for their main purposes, such as collect and share data but also in the maintenance mode for updates. We present a scheduling problem to deal with massive download updates on millions of devices connected to radio networks. One of the main challenges of this problem is the fact of the devices are mobile and can connect to several access points in the network over the time, and the server usually can not control the download process other than suggesting a start time to the download. The problem also includes other constraints such as network and server capacities, and devices limitations. 3 - Optimum Product Introduction And Strategic Capacity Planning Including Demand And Price Cannibalization Gorkem Yilmaz, Assistant Professor, Ozyegin University, Cekmekoy, Istanbul, 34794, Turkey, gorkem.yilmaz@ozyegin.edu.tr, Amaia Lusa Garcia, Ernest Benedito In literature, product introduction and strategic capacity planning typically address optimal timing, pricing decisions and capacity management independently regardless of demand and price cannibalization effects. We develop a Mixed Integer Linear Program (MILP) model for solving strategic capacity planning and product introduction problems, simultaneously, including demand and price cannibalization. With dynamic demand and pricing senario, we analyze several numerical examples to display the interaction between demand and price. 4 - Benders Decomposition And Column-and-row Generation For Solving Large-scale Linear Programs With Column-dependent-rows Kerem Bulbul, Associate Professor, Sabanci University, Faculty of Engineering & Natural Sciences, Orhanli, Tuzla, Istanbul, 34956, Turkey, bulbul@sabanciuniv.edu, Ilker S Birbil, Ibrahim Muter We study a general class of LP problems - known as problems with column- dependent-rows (CDR-P). These LPs feature two sets of constraints with mutually exclusive groups of variables in addition to a set of structural linking constraints, in which variables from both groups appear together. The number of linking constraints grows very quickly with the number of variables, which motivates generating both columns and their associated linking constraints simultaneously on-the-fly. The structure of CDR-P is amenable to Benders decomposition and leads to an effective approach. The unavailability of a full description of the Benders subproblem over the iterations is our main theoretical challenge. 5 - The Branching Dual Of A Discrete Optimization Problem Gerdus Benade, Carnegie Mellon University, 6235 Fifth Avenue, Apt B17, Pittsburgh, PA, 15232, United States, gerdusbenade@gmail.com, John Hooker We formulate the branching dual of a discrete optimization problem as maximizing an inferred lower bound over the space of partial search trees. We identify a class of optimization problems with limited information transfer for which a greedy node selection rule is approximately optimal. A computational study compares the efficiency of the branching dual with other methods for proving bounds on the minimum bandwidth problem. It is found that the branching dual requires significantly fewer nodes than the alternatives to prove a given bound.

WC78 Legends F- Omni Health Care, Modeling Contributed Session

Chair: Harshal Lowalekar, Indian Institute of Management-Indore, Prabandh Shikhar, Rau-Pithampur Road, Indore, 453331, India, lwlherschelle@gmail.com 1 - A Microsimulation Cost-effectiveness Analysis Of Nalmefene To Reduce Heavy Alcohol Consumption In An Alcohol-dependent Canadian Population Estefania Ruiz Vargas, Ivey Business School, 1255 Western Rd, London, ON, N6G 0N1, Canada, eruizvar@uwo.ca, Richard Zur, Greg Zaric Nalmefene is an opioid antagonist believed to reduce the analgesic and positive reward effect of alcohol. A microsimulation of alcohol consumption was developed to determine if nalmefene combined with psychosocial support is cost- effective for reducing alcohol consumption in an alcohol-dependent Canadian population. We considered a number of different nalmefene treatment approaches, and evaluated the public health benefit of implementing nalmefene as a pharmacological intervention for alcohol dependence in Canada. 2 - Managing A Multi-physician Clinic With Heterogeneous Patients This paper focuses on the physician panel design problem of determining the patient composition (i.e. panel size and types of patient) for physician panels. In particular, we investigate how clinics would allocate a given pool of two types of patient flows that have different appointment patterns (i.e. low frequent/routine service vs. high frequent/complex care needs). We identify the characteristics of patient and capacity allocation strategies from the perspectives of the clinics and the patients 3 - A Markovian Model For Blood Components Production System Harshal Lowalekar, Indian Institute of Management-Indore, Prabandh Shikhar, Rau-Pithampur Road, Indore, 453331, India, lwlherschelle@gmail.com We develop a Markovian model for the production of major components such as red blood cells, platelets and plasma from whole blood at a blood bank. The supply quantity of whole blood is assumed to be a random variable. Using the Markovian approach we determine the optimal target collection level for whole blood and the percentage of whole blood to be separated into components. WC79 Legends G- Omni Opt, Combinatorial I Contributed Session Chair: Christopher John Wishon, Ph.D. Candidate, Arizona State University, Tempe, AZ, United States, cwishon@asu.edu 1 - Reformulation-linearization Technique Application On Integer Programming Models For Organ Exchange Program Seokhyun Chung, Korea University, Seoul, Korea, Republic of, tcheong@korea.ac.kr, Junsang Yuh, Taesu Cheong Kidney exchange allows a potential living donor whose kidney is incompatible with the intended recipient to donate a kidney to another patient so that the donor’s recipient receives a compatible kidney from another donor. This can be modeled as maximum weight cycle packing problems in a directed graph. In this study, we verify relationship between existing models via reformulation- linearization technique (RLT), and develop a new integer programming model for kidney exchange program by implementing the RLT. In addition, we devise new integer programming model for liver exchange program, which has distinct character. Moreover, we enhance the integer programming model by applying RLT. 2 - Solution Approaches To Network Design Problems With Decision Dependent Uncertainty Nathaniel Richmond, University of Iowa, 446 4th Avenue, Iowa City, IA, 52241, United States, nathaniel-richmond@uiowa.edu, Pavlo A Krokhmal, Dmytro Matsypura Robust network design problems have many important practical applications, and for this reason are well-represented in the operations research literature. However, until the last decade very little research has examined stochastic robust network design problems in which the user’s decisions affect the underlying probability distribution of the random outcome. In this talk, we present a model for the stochastic network design problem with decision-dependent uncertainties. We discuss computational challenges and explore different solution approaches. In particular, we present a metaheuristic algorithm that draws from strengths of GRASP and genetic algorithms. Wen-Ya Wang, San Jose State University, San Jose, CA, United States, wenya.wang@sjsu.edu, Hao-Wei Chen

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