2015 Informs Annual Meeting

SC11

INFORMS Philadelphia – 2015

4 - The World at Our Fingertips: Consumer Conversion from Search, Click-through, to Book Karen Xie, Assistant Professor, University of Denver, Fritz Knoebel School of Hospitality Mgmt, Denver, CO, United States of America, Karen.Xie@du.edu, Young-Jin Lee We examine how informational cues displayed influence consumer conversion in a sequential process of search, click-through, and booking when shopping hotels online. Using an empirical analysis of a large online travel agent site that provides information of individual searches, we find consumers are likely to click through hotels with higher consumer ratings and the industry-endorsed star rating. However, when committing to booking consumers refer to consumer ratings rather than the star rating.

2 - Cost Effective Energy Optimization by Solving Facility Allocation on Riemannian Manifolds Iddrisu Awudu, Assistant Professor, Quinnipiac University, 275 Mount Carmel Ave., Hamden, CT, 06518, United States of America, Iddrisu.Awudu@quinnipiac.edu Delivering goods and services in an efficient (cost) and effective (value) manner is crucial to every supply chain. In this presentation, a recently developed technique for solving the Location Routing Problem on Manifold surfaces called Manifold Location Routing Problem (MLRP) is used to design a renewable energy distribution system for a single ethanol production facility. The results with the corresponding managerial insights of the model are discussed. Multi-armed Bandits and Online Optimization Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session Chair: Andrew Lim, National University of Singapore/Department of Decision Sciences, Mochtar Riady Building, BIZ1 08-69, 15 Kent Ridge Drive, Singapore, Singapore, andrewlim@nus.edu.sg 1 - Dynamic Pricing and Product Differentiation with Cost Uncertainty and Learning Bora Keskin, Duke University, Fuqua School of Business, 100 Fuqua Drive, Durham, NC, 27708-0120, United States of America, bora.keskin@duke.edu, John Birge Motivated by applications in the health insurance industry, we consider a seller who designs and sells a set of products to a population of quality-sensitive customers. The seller faces an uncertainty about production costs. We prove that, while a seller facing static cost uncertainty degrades the quality in its product offering, a dynamically learning seller improves the quality of its products to accelerate information accumulation. 2 - Efficient Experimentation via the Bootstrap Ian Osband, Stanford University, 450 Serra Mall, Stanford, CA, United States of America, iosband@stanford.edu, Benjamin Van Roy If you want to use data to make good decisions, you need good data. Often, the decisions you make influence both performance and the data collected. Balancing exploration with exploitation can be complex. We present a fully non-parametric version of Thompson sampling that uses the bootstrap to produce approximate posterior samples. We show that, under some assumptions, this simple heuristic satisfies strong performance guarantees. This algorithm performs well in simulation. 3 - Approximate Learning Trajectories for Bayesian Bandits Michael Kim, University of Toronto, 5 King’s College Road, Toronto, Canada, mikekim@mie.utoronto.ca, Andrew Lim It is known that the optimal policy for a multi-armed bandit problem is the Gittins index policy. For bandit problems with Bayesian learning (Bayesian bandits) however, computing the Gittins index is intractable due to the curse of dimensionality. In this talk, we introduce the concept of an approximate learning trajectory, which approximates the dynamics of future learning. We show how this can be used to simplify the DP equations, which allows for an efficient computation of the Gittins index. 4 - Bandits with Global Convex Constraints and Concave Objective Shipra Agrawal, Researcher, Microsoft Research, #9 Lavelle Road, Bangalore, India, ashipra@gmail.com I will present recent advances on multi-armed bandit problems that involve arbitrary concave objective functions and convex constraints on the aggregate of decisions across time, in addition to the customary limitation on the time horizon. SC14 14-Franklin 4, Marriott New Developments in Robust and Adaptive Optimization Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session Chair: Dimitris Bertsimas, Professor, MIT, 77 Massachusetts Ave., Cambridge, MA, 02139, United States of America, dbertsim@mit.edu 1 - Multistage Robust Mixed Integer Optimization with Adaptive Partitions Iain Dunning, PhD Candidate, MIT, 77 Massachusetts Ave, Building E40, Cambridge, MA, 02139, United States of America, idunning@mit.edu, Dimitris Bertsimas SC13 13-Franklin 3, Marriott

SC11 11-Franklin 1, Marriott Joint Session OPT/OPT Under Uncertainty: IP Methods for Stochastic Optimization Sponsor: Optimization/Integer and Discrete Optimization Sponsored Session

Chair: Siqian Shen, Assistant Professor, University of Michigan, 1205 Beal Avenue, Ann Arbor, MI, 48105, United States of America, siqian@umich.edu 1 - Scenario Decomposition of Risk Averse Stochastic 0-1 Programs Yan Deng, University of Michigan, 3730 Greenbrier Blvd, We present a scenario decomposition approach for risk averse stochastic 0-1 programs. The approach uses decomposition that processes subproblems easy to parallelize, and LP/IP techniques that combine subproblem results to yield strong valid inequalities. We also design various parallelization paradigms and demonstrate on stochastic server location problems and stochastic multiple 0-1 knapsack problems that the approach improves computational efficacy significantly. 2 - Polyhedral Study of Static Probabilistic Lot-sizing Problem Xiao Liu, Research Associate, The Ohio State University, 210 Baker Systems Building 1971 Neil Ave, Columbus, OH, 43210, United States of America, liu.2738@osu.edu, Simge Kucukyavuz We explore the polyhedral structure of the static probabilistic lot-sizing problem and propose new valid inequalities. We show the relationship between the proposed inequalities and those that would be obtained by projecting the natural or the extended formulation of the deterministic lot-sizing problem for each scenario on to the space of the production variables. We show that the proposed inequalities are facet-defining under certain conditions. We report preliminary computational results. 3 - Computational Experience with a Benders Algorithm for Two- stage Stochastic Integer Programming Ted Ralphs, Professor, Lehigh University, 200 W. Packer Ave., Bethlehem, PA, 18015, United States of America, ted@lehigh.edu, Anahita Hassanzadeh We briefly outline a Benders-type algorithm for solving two-stage stochastic programs with mixed integer recourse and describe our experience implementing the algorithm in practice, as well as its theoretical connection to bilevel optimization. Computational results with benchmark instances from the literature will be presented. SC12 12-Franklin 2, Marriott Mixed Integer Programming and Location Routing Problem Sponsor: Optimization/Mixed Integer Nonlinear Optimization and Global Optimization Sponsored Session Chair: Emre Tokgoz, Assistant Professor, Quinnipiac University, Ann Arbor, Mi, 48105, United States of America, yandeng@umich.edu, Siqian Shen, Shabbir Ahmed 275 Mount Carmel Ave. TH-ENR, Hamden, CT, 06518, United States of America, Emre.Tokgoz@quinnipiac.edu 1 - Manifold Location Routing Problem Emre Tokgoz, Assistant Professor, Quinnipiac University, 275 Mount Carmel Ave. TH-ENR, Hamden, CT, 06518, United States of America, Emre.Tokgoz@quinnipiac.edu A new Location Routing Problem named Manifold Location Routing Problem and the most recent developments to solve this problem will be explained in this talk.

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