2016 INFORMS Annual Meeting Program

SB15

INFORMS Nashville – 2016

SB13 104C-MCC Advances in Structured Nonconvex Optimization Sponsored: Optimization, Global Optimization Sponsored Session Chair: Fatma Kilinc Karzan, Assistant Professor, Carnegie Mellon University, 5000 Forbes Ave, Pittsburgh, PA, 15213, United States, fkilinc@andrew.cmu.edu 1 - Solving Standard Quadratic Programming By Cutting Planes Andrea T. Lodi, École Polytechnique de Montréal, andrea.lodi@polymtl.ca Standard quadratic programs are non-convex quadratic programs with the only constraint that variables must belong to a simplex. By a famous result of Motzkin and Straus, those problems are connected to the clique number of a graph. We propose cutting planes to obtain strong bounds: our cuts are derived in the context of Spatial Branch & Bound, where linearization variables represent products. Their validity is based on Motzkin-Straus result. We study the relation between these cuts and the ones obtained by the first RLT level. We present extensive computational results using the cuts in the context of the Spatial Branch & Bound implemented by the commercial solver CPLEX. 2 - Some Cut-generating Functions For Second-order Conic Sets Asteroide Santana, Georgia Institute of Technology, Atlanta, GA, 30308, United States, asteroidemtm@gmail.com Santanu Subhas Dey In this paper, we study cut generating functions for conic sets. Our first main result shows that if the conic set is bounded, then cut generating functions for integer linear programs can easily be adapted to give the integer hull of the conic integer program. Then we introduce a new class of cut generating functions which are non-decreasing with respect to second-order cone. We show that, under some minor technical conditions, these functions together with integer linear programming-based functions are sufficient to yield the integer hull of intersections of conic sections in R2. 3 - Polynomial Dc Decompositions And Applications Georgina Hall, Princeton University, Princeton, NJ, United States, gh4@princeton.edu, Amir Ali Ahmadi Difference of Convex (DC) programming is a class of optimization problems where the objective and constraints are given as the difference of convex functions. Although several important problems (e.g., in machine learning) already appear in DC form, such a decomposition is not always available. We consider this decomposition question for polynomial optimization and present some new applications, primarily to distance geometry problems. 4 - A Second-order Cone Based Approach For Solving The Trust Region Subproblem And Its Variants Nam Ho-Nguyen, Carnegie Mellon University, Pittsburgh, PA, United States, hnh@andrew.cmu.edu, Fatma Kilinc-Karzan We study the trust region subproblem (TRS) of minimizing a nonconvex quadratic function over the unit ball with additional conic constraints. We follow a second-order cone based approach to derive an exact convex formulation of the TRS, and under slightly stronger conditions, give a low-complexity characterization of the convex hull of its epigraph without any additional variables. Our study highlights an explicit connection between the nonconvex TRS and smooth convex quadratic minimization, which allows for the application of cheap iterative methods to the TRS. SB14 104D-MCC OR In Agriculture Invited: Agricultural Analytics Invited Session Chair: Margarit Khachatryan, Monsanto, United States, margarit.khachatryan@monsanto.com 1 - Government Interventions In Promoting Sustainable Practices In Agriculture Duygu Akkaya, Stanford Graduate School of Business, Stanford, CA, United States, duygug@stanford.edu, Hau Lee, Kostas Bimpikis Sustainable practices in agriculture such as organic farming have attracted immense attention lately due to the increase in environmental and health concerns. Government support is often used to incentivize producers to convert to sustainable practices. We investigate the effectiveness of government interventions including tax, subsidy and hybrid policies in terms of their impact on sustainable practice adoption, producers’ profits, consumer welfare, and return on government spending using a setting in which producers with traditional and sustainable production options serve consumers that have a high valuation for

sustainable production. 2 - Accelerating Digital Agriculture Through Automated R&D Trial Placement Into Field Zones Qinglin Duan, Monsanto, St. Louis, MO, United States, qinglin.duan@monsanto.com, David Ciemnoczolowski The trend towards Digital Agriculture requires increasing information on conditions within fields and corresponding decisions about product selection and management. To provide placement and management prescriptions, products must be tested across differing conditions within fields. We formulate the zone mapping problem as a 2D bin-packing model with trials of known dimensions and operational constraints. The model is integrated into Monsanto’s geospatial field platform with analytics relating climate, soils, and topography to crop performance. Optimized placement has enabled representative testing across environments and set the foundation for advancements in digital agriculture. 3 - Combining Expert Estimates With Data To Obtain Hybrid Yield Distributions Saurabh Bansal, Penn State University, sub32@psu.edu, Genaro J Gutierrez We discuss a Copula based approach to combine expert judgments for yield distributions with data, and illustrate its application for the seed corn business. 4 - A Mathematical Model For Farm Scale Land Management Considering Uncertainty Qi Li, Iowa State University, qili@iastate.edu, Guiping Hu Farmers make decisions on types of crops to plant and irrigation frequency and pattern on an annual basis. This is often done under various uncertainties, such as precipitation amount, crop prices, and soil profile. In the study, a farm level precision farmland management model based on stochastic programming is proposed. The model focuses on the uncertainties in weather, yield and market prices. Advanced statistical methods such as time series analysis and spatial analysis are also investigated to generate representative realizations for the uncertainties. SB15 104E-MCC Building Better Models: Innovations in Predictive Analytics Invited: Modeling and Methodologies in Big Data Invited Session Chair: CP Teo, NUS, 1 Business Link, Singapore, 598727, Singapore, bizteocp@nus.edu.sg 1 - Multi-product Pricing Problem Using Experiments Zhenzhen Yan`, National University of Singapore, Singapore, Singapore, a0109727@u.nus.edu, Cong Cheng, Karthik Natarajan, Chung-Piaw Teo We study the multi-product pricing problem using pricing experiments. In particular, we develop a data driven approach to this problem using the theory of marginal distribution. We show that the pricing problem is convex for a large class of discrete choice models, including the classical logit and nested logit model. Our model remains convex as long as the marginal distribution is log-concave. More importantly, by fitting data to optimize the selection of choice model, we develop an LP based approach to the semi-parametric version of the pricing problem. Preliminary tests using a set of automobile data show that this approach provides near optimal solution, even with random coefficient logit model. 2 - Disruption Risk Mitigation In Supply Chains – The Risk Exposure Index Revisited Sarah Yini Gao, NUS, 1, Singapore, Singapore, yini.gao@nus.edu.sg, Chung-Piaw Teo, David Simchi-Levi We proposed a new method to integrate probabilistic assessment of disruption risks into the REI approach, and measure supply chain resiliency by analyzing the Worst-case CVaR of total lost sales under disruptions. We show that the optimal emergency inventory positioning strategy in this model can be fully characterized by a conic program. Moreover, the optimal primal and dual solutions to the conic program can be used to shed light on comparative statics in the supply chain risk mitigation problem. 3 - Provably Data-Driven Approximation Schemes For Joint Pricing And Inventory Control Models Hanzhang Qin, Massachusetts Institute of Technology, Cambridge, MA, United States, hqin@mit.edu, Davis Simchi-Levi, Li Wang We propose a data-driven algorithm to solve the joint inventory and pricing problem for a single-product, multi-period model under independent demand. Our algorithm provides a near-optimal solution under any degree of accuracy and pre-specified confidence probability and requires polynomial number of sample data and is polynomial in the number of time periods. This algorithm differs from other online data-driven counterparts in the sense that we make all decisions based on past data only.

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