2016 INFORMS Annual Meeting Program

SC66

INFORMS Nashville – 2016

2 - A General Approximation Method For Bicriteria Minimization Problems

3 - Airbnb And Hotel Latent Quality Uttara Ananthakrishnan, Carnegie Mellon University, uttara@cmu.edu

Stefan Ruzika, Department of Mathematics/Natural Sciences University of Koblenz-Landau Universitätsstraße 1 56070 Koblenz (Germany), ruzika@uni-koblenz.de We present a general technique for approximating bicriteria minimization problems with positive-valued, polynomially computable objective functions. Given 0 < ≤ 1 and a polynomial-time -approximation algorithm for the corresponding weighted sum problem, we show how to obtain a bicriteria ( · (1 + 2 ), · (1 + 2/ ))-approximation algorithm for the budget-constrained problem whose running time is polynomial in the encoding length of the input and linear in 1/ . Moreover, we show that our method can be extended to compute an ( · (1 + 2 ), · (1 + 2/ ))-approximate Pareto curve under the same assumptions. 3 - Bicriteria Analysis Of The Fixed-charge Network Flow Problem - Separating Fixed Costs And Flow Costs Michael Stiglmayr, University of Wuppertal, Wuppertal, Germany, stiglmayr@math.uni-wuppertal.de The fixed-charge network flow problem is an inherently biobjective optimization problem: Minimize fixed (design) costs and minimize flow costs. In its classical form the sum of these two objectives is minimized which corresponds to the weighted-sum scalarization of the associated biobjective problem. However, design costs and flow costs are not directly comparable, since design costs occur once, while flow costs are due periodically. In this talk we present heuristic and exact solution approaches based on the two-phase method and ranking algorithms. 4 - Multi-objective Optimization Of Coupled Systems George Fadel, Mechanical Engineering Department Fluor Daniel Engineering Building Clemson University, Clemson SC 29634 USA, fgeorge@clemson.edu An engineering problem consists of two multi-objective problems that must be coordinated. The top level focuses on the optimal placement of components under the hood of a car, with design variables which specify the location of the various non-convex components in a non-convex volume, and non-overlap constraints. Then, the optimization of shape and size of a battery pack that is one of the components placed under the hood is conducted. We show how the two problems can be assigned to separate teams, and their optimizations can be coordinated, enabling the chief designer to allow the sub-problem or the upper level design team to be driving the solution. SC65 Mockingbird 1- Omni Machine Learning, Big Data and Economics Sponsored: Information Systems Sponsored Session Chair: Beibei Li, Carnegie Mellon University, Heinz College, Pittsburgh, PA, 15213, United States, beibeili@andrew.cmu.edu 1 - Modeling User Engagement In Mobile Content Consumption With Tapstream Data Yingjie Zhang, Carnegie Mellon University, yingjie2@andrew.cmu.edu Low engagement rate and high attrition rate have been major challenges for the success of mobile apps. To date, little is known towards how companies can improve user engagement and business revenues through designing effective in- app pricing strategies. We propose a structural model by accounting for time-varying nature of engagement and consumer forward-looking behavior. We analyze mobile tapstream data from a popular mobile reading app. Our results enable us to tailor optimal pricing strategy to each consumer based on their engagement status. Interestingly, we found such engagement-specific pricing strategy leads to lower average price for consumers and higher overall business revenues. 2 - Examine Large Scale App Usage Structure By Graphical Model Jinyang zheng, University of Washington, Seattle, zhengjy@uw.edu Identifying an app which generates usage to other app(s) is not only a crucial task for industrial practitioner, but also challenging for researchers given the larger scale of App network. With a state of art graphical model method, we overcome the limitation of traditional econometric causal inference models and examine the causal relationship in emerging App market among Chinese users. Our model generates a causal diagram displaying usages of what app would that of each specific app leads to. Spillover effects of certain app and sequential causal effects can be easily identified, suggesting significant role of graphical model in business analytics and big data related research.

Sharing economy has empowered consumers to communicate their needs with one another and thus has helped them to assume the role of both suppliers and producers seamlessly. In this paper, using a natural experiment set up and a novel dataset, we analyze how Airbnb has impacted the traditional way of conducting the hotel business. We study if the hotels have responded to the increasing number of Airbnbs by increasing their quality and whether this response varies across different types of hotels. We analyze the hotel industry’s response across different dimensions in quality by not only considering star ratings, but also user sentiments and latent quality expressed in textual content of reviews. 4 - How Much Is An Image Worth? An Empirical Analysis Of Property’s Image Aesthetic Quality On Demand At Airbnb Shunyuan Zhang, Carnegie Mellon University, Pittsburgh, PA, United States, shunyuaz@andrew.cmu.edu Dokyun Lee, Param Vir Singh, Kannan Srinivasan Sharing economy platforms such as Airbnb are challenged with product quality uncertainty. To solve the issues, Airbnb has implemented strategies such as professionally taking high quality photos for hosts and calling them verified. This paper studies the impact of having verified photos. To assess the aesthetic quality of images, we use machine learning techniques. Employing Difference-in- Difference method we find rooms with verified photos are on average 9% more frequently booked. We separate the effect of photo verification from photo quality and find an extra $2,455 in yearly earnings brought by high photo quality. We find asymmetric spillover effects across rooms in the same neighborhood. SC66 Mockingbird 2- Omni Additive Manufacturing Sponsored: Quality, Statistics and Reliability Sponsored Session Chair: Prahalad Krishna Rao, Assistant Professor, Binghamton University, P.O. Box 6000, Binghamton, NY, 13902-6000, United States, prao@binghamton.edu 1 - Accelerated Process Optimization For Laser-based Additive Manufacturing By Leveraging Similar Prior Studies Amir M Aboutaleb, Mississippi State University, Industrial & Systems Engineering Department, Mississippi State University, MS, 39762, United States, aa1869@msstate.edu, Linkan Bian Manufacturing parts with target properties and quality in Laser-Based Additive Manufactured (LBAM) parts is crucial towards enhancing the “trustworthiness” of this emerging technology. We propose a novel process optimization method by directly utilizing experimental data from previous studies as the initial experimental data to guide the sequential optimization experiments of the current study. We conduct a real-world case study that optimizes the relative density of parts manufactured using a Selective Laser Melting system. A combination of optimal process parameters is achieved within 5 experiments. 2 - Online Detection For Cyber Attacked Additive Manufactured Parts By Real-time Sensing And Analysis Chenang Liu, Virginia Tech, Blacksburg, VA, 24061, United States, lchenang@vt.edu, Tomilayo Komolafe, Zhenyu Kong, Jaime Camelio Cyber security of additive manufacturing (AM) is important for some critical applications such as defense industry. This work focuses on the online detection of attacked AM parts by real-time sensing using network analysis based data fusion techniques. Using the effective features extracted from multiple sensor data, the discrepancy between normal and attacked AM parts can be detected effectively. The case study show that the proposed method can successfully detect the attacked parts, but does not cause false alarm for the sample normal part. 3 - Laplacian Eigen Compressive Sensing For Dimensional Integrity Classification In Additive Manufacturing Prahalad Rao, Binghamton University, prahalad.k.rao@gmail.com This work relates the effect of parameters, namely, infill and extrusion temperature in fused filament fabrication (FFF) additive manufacturing (AM) process on pre-selected geometric dimensioning and tolerancing (GD&T) features. Next, a method is proposed to classify the part quality in terms of the geometric integrity using minimal number of laser-scanned point cloud data. The proposed method combines spectral graph theory with compressive sensing, as a means of supervised classification of part geometric integrity.

91