2015 Informs Annual Meeting

WC16

INFORMS Philadelphia – 2015

4 - Multi-stage Stochastic Lot Sizing Problem with Nervousness Considerations Esra Koca, Asst. Prof., Sabanci University, Department of Industrial Engineering, Istanbul, 34956, Turkey, esrakoca@gmail.com, M. Selim Akturk, Hande Yaman We study the multi-stage stochastic lot sizing problem with controllable processing times and nervousness considerations. In multi-stage stochastic programming, one can obtain different production decisions for different scenarios and this situation may lead to lack of coordination. We formulate the problem by considering this drawback of the approach. Some mixing set structures are observed as relaxations of our formulation, and valid inequalities are developed based on these structures. 5 - Chance Constrained Optimization for Pari-mutuel Horse Race Betting Michael Metel, PhD Student, McMaster University, 1280 Main St.

4 - Robust Empirical Optimization is Almost the Same as Mean-variance Optimization Jun-ya Gotoh, Chuo university, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan, jgoto@indsys.chuo-u.ac.jp, Michael Kim, Andrew Lim We consider a distributionally robust optimization (DRO) problem in which the decision maker optimizes against a worst-case distribution, where a penalty on phi—divergence controls the amount that the alternative can deviate from the nominal. Our main finding is that robust empirical optimization is essentially equivalent to solving an in-sample mean-variance problem, which provides insight into the mechanism by which empirical DRO achieves its “robustness.”

WC16 16-Franklin 6, Marriott Game Theory IV Contributed Session

West, Hamilton, ON, L8S4M4, Canada, Michael Metel, michaelmetel@gmail.com, Kai Huang, Antoine Deza

We consider the time horizon of a gambler in the optimization of horse race betting through the use of chance constrained programming. A novel approach to estimating superfecta payouts is presented using maximum likelihood estimation. A computational substantiation with historical race data found an increase in return of over 10% using the chance constrained model.

Chair: Anastasia Nikolaeva, PhD Student, University at Buffalo, 342 Bell Hall, Amherst, NY, 14260, United States of America, aanikola@buffalo.edu 1 - Bandwagon Investment Equilibrium of a Preemption Game Kihyung Kim, Postdoc Research Associate, Purdue University, 315 N. Grant Street, West Lafayette, In, 47907, United States of America, kihyung.kim.1@purdue.edu, Abhijit Deshmukh Given a first mover’s advantage in a competitive market, theoretical research supports the preemption strategy that invests earlier than competitors. However, empirical research shows that the preemption strategy is seldom successful, and simultaneous investments are frequently observed. This research addresses this gap by deriving the bandwagon investment equilibrium that elucidates how a Sercan Demir, University of Miami, 1251 Memorial Drive, Department of Industrial Engineering, Coral Gables, FL, 33146, United States of America, s.demir@umiami.edu, Murat Erkoc We study contingency inventory reservation contracts across a single supplier and multiple buyers. Buyers are distributors operating in independent markets and are subject to adverse events. They enter into contractual agreement with the supplier for carrying contingency inventory. The supplier sets the reservation terms to which the buyers respond with their reservation decisions. We investigate equilibrium reservation policies under varying probabilities and market impacts of disruptions. 3 - Vendor Competition in Remanufacturing Debabrata Ghosh, Assistant Professor, IIM Calcutta, NAB, K-307, In this paper we study a case where vendors compete during re-manufacturing for an OEM. While studies have highlighted competition between retailers under product take-back, in several emerging economies third party vendors play an interesting role in re-manufacturing. Using game theoretic set up we study the decisions of the OEM and vendors under re-manufacturing. Our study reveals interesting pricing and re-manufacturing decisions under competition. 4 - Selling Format and Contract Type in Electronic Marketplaces with Product and Retail Competition Baixun Li, Guangdong University of Finance and Economics, 21 Chisha Road, Guangzhou, Guangzhou, 510320, China, libaixun2002qq@163.com This paper investigates the choices of selling format and contract type of two supply chains, which compete at both manufacturer and retailer levels. We consider two kinds of selling format: agency selling format and reselling format, and two kinds of contract: wholesale price contract and revenue sharing contract. We show that the equilibrium selling format of the online retailers depend on the contract type, channel power, and competitive intensity at both manufacturer and levels. 5 - Approximating Nash Equilibria in a Two Product Two Firm Oligopoly Anastasia Nikolaeva, PhD Student, University at Buffalo, 3 42 Bell Hall, Amherst, NY, 14260, United States of America, aanikola@buffalo.edu, Mark Karwan We consider an oligopoly industry and the Cournot-Nash model with two firms competing on the basis of quantity. In our application there are two homogeneous products of interest and customers sign a one or joint product supply contract. Existing customers and a set of new customers not currently served are competed for by each firm with a goal of profit maximization. We approximate Nash Equilibria using aspects of Game Theory, Simulation and Integer Programming and present empirical results. D.H. Road, Joka, Kolkata, WB, 700104, India, debabrata.ghosh@iimcal.ac.in, Sirish Gouda firm’s investment entices the competitor’s investment. 2 - Contingency Inventory Reservation Across Independent Distributors

WC13 13-Franklin 3, Marriott

Data Driven Optimization and Applications II Sponsor: Optimization/Optimization Under Uncertainty Sponsored Session Chair: Jun-ya Gotoh, Chuo university, 1-13-27 Kasuga, Bunkyo-ku, Tokyo, 112-8551, Japan, jgoto@indsys.chuo-u.ac.jp 1 - Performance Analysis of Stochastic Dynamic Programs via Information Relaxation Duality David Brown, Duke University Fuqua School of Business, 1 Towerview Rd, Durham, NC, United States of America, dbbrown@duke.edu, Santiago Balseiro A common technique in the analysis of stochastic systems is the use of “hindsight bounds,” in which decisions are made after uncertainties are revealed. In some applications, however, hindsight bounds may lead to very weak performance guarantees. We show how to obtain stronger guarantees by incorporating penalties that punish the use of additional information, and demonstrate the technique on several applications, including stochastic knapsack problems. 2 - A Unified Classification Algorithm Based on Accelerated Proximal Gradient Methods Akiko Takeda, The University of Tokyo, 7-3-1 Hongo, Bunkyo- ku, Tokyo, 113-8656, Japan, takeda@mist.i.u-tokyo.ac.jp, Naoki Ito, Kim Chuan Toh The goal of binary classification is to predict the class of a new sample. It is important to find suitable classification models for given datasets for good prediction performances. We design a unified model for various types of classification models and an efficient algorithm for solving the unified model, which speeds up the process of finding the best model. It is based on an accelerated proximal gradient method and performs better than specialized algorithms for specific models. 3 - Demand Clustering and Inventory Optimization for Fashion Products Tong Wang, Associate Professor, National University of Singapore, 15 Kent Ridge Drive, Singapore, SG, 119245, Singapore, tong.wang@nus.edu.sg We study demand estimation and inventory optimization for fashion products. Based on historical product covariates and sales, we construct Bayesian structure model to cluster products that share commonality in covariates and sales pattern. At the beginning of a new season after covariates of new products are observed, they are assigned into the clusters, and their demand distributions are estimated. Inventory decisions are made accordingly. Clustering is updated at the end of the season.

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