2016 INFORMS Annual Meeting Program

MB47

INFORMS Nashville – 2016

MB45 209A-MCC Advances in Simulation Optimization Sponsored: Simulation Sponsored Session Chair: Weiwei Chen, Assistant Professor, Rutgers Business Schoo l, 1 Washington Park, Newark, NJ, 07901, United States, wchen@business.rutgers.edu Co-Chair: Siyang Gao, City University of Hong Kong, City University of Hong Kong, Kowloon, Hong Kong, siyangao@cityu.edu.hk 1 - Generalized Likelihood Ratio Method For Stochastic Derivative Estimation Yijie Peng, Fudan University, pengy10@fudan.edu.cn, Michael Fu, Jianqiang Hu We propose a generalized likelihood ratio method for stochastic derivative estimation in a general framework that can handle discontinuities in both the sample performance and sample path. The classical likelihood ratio method is a special case where the parameter does not appear in the sample performance. In addition, the new method generalizes the push-out likelihood ratio method. The framework also includes most settings where infinitesimal perturbation analysis applies, although the actual estimator differs in general. Examples demonstrate the proposed method works for a broad set of applications, many of which cannot be handled by existing methods. 2 - Advanced Simulation Optimization Approach Loo Hay Lee, National University of Singapore, iseleelh@nus.edu.sg, Chun-Hung Chen In this talk, we will present some potential research topics in simulation optimization and discuss some of the preliminary work. 3 - Challenges In Applying Ranking And Selection After Search David Eckman, Cornell University, Ithaca, NY, United States, dje88@cornell.edu, Shane Henderson It is often appealing to reuse simulation replications taken during a simulation optimization search as input into a ranking-and-selection procedure. However, even when replications are i.i.d. and independent across systems, we show that when the search uses the observed performance of explored systems to identify new systems, conducting ranking-and-selection procedures that reuse the search replications can result in probabilities of correct (and good) selection below the prespecified level. We also show a similar deterioration in the guarantees of subset-selection procedures. 4 - A Partition-based Random Search For Stochastic Constrained Optimization Via Simulation This research focuses on the global optimization over finite solution space with deterministic objective function and stochastic constraints. Due to the random noise observed in the constraints, the feasibility of a solution is unknown and can be best evaluated by simulation. We propose a partitioning scheme to explore the solution space and develop a feasibility detection procedure for the sampled solutions. A partition-based random search approach with multi-constraint feasibility detection (PRS-MFD) is then proposed to search for the optimal solution. The efficiency of PRS-MFD is shown by numerical experiments, and it is proved to converge to the set of global optima with probability one. MB46 209B-MCC Revenue Management and Assortment Optimization Sponsored: Revenue Management & Pricing” Sponsored Session Chair: Hamid Nazerzadeh, University of Southern California, Marshall School of Business, Los Angeles, CA, 90089, United States, nazerzad@marshall.usc.edu 1 - Real-time Optimization Of Personalized Assortments Negin Golrezaei, University of Southern California, golrezae@usc.edu Motivated by the availability of real-time data on customer characteristics, we consider the problem of personalizing the assortment of products for each arriving customer. Using actual sales data from an online retailer, we demonstrate that personalization based on each customer’s location can lead to over 10% improvements in revenue compared to a policy that treats all customers the same. Weiwei Chen, Rutgers Business School, wchen@business.rutgers.edu, Siyang Gao

We propose a family of index-based policies that effectively coordinate the real- time assortment decisions with the back-end supply chain constraints. We allow the demand process to be arbitrary and prove that our algorithms achieve an optimal competitive ratio. 2 - Online Personalized Resource Allocation With Customer Choice Van-Anh Truong, Columbia University, New York, NY, United States, vt2196@columbia.edu, Guillermo Gallego, Anran Li, Xinshang Wang We introduce a general model of resource allocation with customer choice. This problem has a number of applications, including personalized assortment optimization, revenue management of parallel flights, and web- and mobile-based appointment scheduling. We derive online algorithms that are asymptotically optimal and achieve the best constant relative performance guarantees for this class of problems. 3 - Assortment Personalization In High Dimension Madeleine Udell, Cornell University, Ithaca, NY, United States, udell@cornell.edu, Nathan Kallus We show how to perform assortment personalization in sublinear time by imposing a natural low rank structure on the problem. In the static setting, we show that this model can be efficiently learned from surprisingly few interactions. In the dynamic setting, we show that structure-aware dynamic assortment personalization can have regret that is an order of magnitude smaller than structure-ignorant approaches. 4 - Position Auctions With Search Cost Heng Zhang, University of Southern California, Heng.Zhang.2019@marshall.usc.edu, Leon Yang Chu, Hamid Nazerzadeh Companies such as eBay, Amazon, and Google have created e-commerce platforms that connect online sellers and online users. In this work, we study how these platforms should rank the products displayed to their users. We present a general model that captures several important aspects of these environments including consumer’s search cost. Our analysis highlights the inefficiencies that can be created due to the asymmetry of information among the sellers and the platform. We present an optimal mechanism as well as a simple near-optimal heuristic. Multi-product Revenue Management Sponsored: Revenue Management & Pricing Sponsored Session Chair: David Simchi-Levi, Massachusetts Institute of Technology, Cambridge, MA, United States, dslevi@mit.edu Co-Chair: He Wang, MIT, 77 Massachusetts Ave, Cambrdige, MA, 02139, United States, wanghe@mit.edu 1 - Reaping The Benefits Of Bundling Under High Production Costs Will Ma, MIT, willma353@gmail.com, David Simchi-Levi It has long been known that selling different goods in a single bundle can significantly increase revenue, but that this is no longer the case if the goods have high production costs. We introduce a simple pricing scheme, called Pure Bundling with Disposal for Cost (PBDC), that captures the benefits of bundling under high costs, extracting all of the surplus in settings where previous simple mechanisms could not. We also prove a theoretical guarantee on the performance of PBDC that holds for arbitrary independent valuation distributions, by adopting and improving techniques from mechanism design literature. Finally, we perform extensive numerical experiments which support the efficacy of PBDC. 2 - New Algorithms And Guarantees For Assortment Optimization Under General Choice Clark Pixton, MIT, Cambridge, MA, United States, cpixton@mit.edu, David Simchi-Levi We present new algorithms for static assortment optimization which apply to general choice models. We show theoretical guarantees, and demonstrate performance via computational experiments. These algorithms sit between the work of Jagabathula (2016) and the choice model assortment optimization literature. MB47 209C-MCC

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