Informs Annual Meeting 2017

SD43

INFORMS Houston – 2017

SD42

4 - Loss Aversion and Uniform Pricing: Are they Related? Ningyuan Chen, Hong Kong University of Science and Technology, Clear Water Bay, Kowloon, Hong Kong, nychen@ust.hk, Javad Nasiry We study the optimality of price markdowns when consumers are loss averse. We show that even if the overall demand function is more sensitive to losses than to gains, the firm may find it optimal to offer periodic discounts to maximize its profits.

360A Additive Manufacturing I Invited: Advanced Manufacturing Invited Session Chair: Qiang Huang, University of Southern California, Los Angeles, CA, 90089, United States, qiang.huang@usc.edu 1 - Predictive Model Building Across Different Process Conditions and Shapes in 3D Printing Arman Sabbaghi, Assistant Professor, Purdue University, 150 N. University Street, West Lafayette, IN, 47907, United States, sabbaghi@purdue.edu, Qiang Huang Predictive deformation models constitute an important component in quality control for 3D printing. However, model building is made difficult by the wide variety of process conditions and shapes. A methodology that can make full use of data on different shapes and conditions is necessary. We develop a new Bayesian procedure based on effect equivalence and modular deformation features that incorporates all data for the systematic construction of deformation models. Our method is applied to model the multiple deformation profiles in cylinders with different cavities. Ultimately, our Bayesian approach unifies quality control in 3D printing across different process conditions and shapes. 2 - A Convolution Formulation to Learn Complex Inter-layer Bonding Effects in Additive Manufacturing Qiang Huang, University of Southern California, Dept of Industrial & Systemes Engineering, 3715 Mcclintock Avenue, Ger 240, Los Angeles, CA, 90089, United States, qiang.huang@usc.edu, Yuan Jin, Zhengyu Zhang In the lay-to-layer Additive manufacturing (AM) processes, there is insufficient understanding of interactions among layers and its effect on geometry deformation. This study establishes a convolution formation to analyze the interlayer interactions through learning from experimental data. 3 - Quality Monitoring and Control in Additive Manufacturing for Metal Products Bianca Maria Colosimo, Professor, Politecnico di Milano, Milano, Italy, biancamaria.colosimo@polimi.it This contribution describes quality issues in metal additive manufacturing. Possible strategies for in-situ monitoring and process control are presented. 360B Financial Incentives and Health System Performance Sponsored: Public Sector OR Sponsored Session Chair: Dimitrios Andritsos, Paris, France, andritsos@hec.fr 1 - Incentive Programs for Reducing Readmissions when Patient Care is Co-produced Dimitrios Andritsos, HEC Paris, Departement MOSI, 1 Rue De La Liberation, Jouy-en-Josas, 78351, France, andritsos@hec.fr, Christopher S. Tang To compare the effectiveness of three different hospital reimbursement schemes (i.e., Fee-for-Service, Pay-for-Performance and Bundled Payment) in reducing readmissions, we develop a “health co-production” model in which the patient’s readmission is “jointly controlled” by the efforts exerted by both the hospital and the patient. 2 - Something from Nothing: Financial, Operational, and Social Benefits of Need-based Free Healthcare SD43

SD41

352F Health Care, Modeling and Optimization Contributed Session Chair: Liqun Lu, University of Illinois at Urbana-Champaign, Urbana, IL, United States, liqunlu2@illinois.edu 1 - Evidence Based Optimal Control Method with Heuristic in Clinical Treatment Kaiming Bi, PhD Student, Kansas State University, 2061 Rathbone Hall, 1701 D.Platt St., Manhattan, KS, 66502, United States, bikaiming@ksu.edu, John C.Wu, Yuyang Chen In real disease clinic dynamic system, both the system or measurment errors are inevitable, thus it is necessary to consider it into treatment strategy. This research addresses a general problem with considering uncertain errors in disease system, since the traditional control methods frequently is not able to provide precise control strategies for disease treatments or medigation when uncertain errors are involved. A new Evidence Based Optimal Control approach is presented combining both traditional optimal control and machine learning methods. Four machine learning algorithms were tested and the most suitable approach were then combined with the traditional optimal control. 2 - Improving Out-of-hospital Response Time with a Dynamic Ambulance Relocation Model: the Case of Emergency Medical Services in Antofagasta, Chile Hernan Caceres, Assistant Professor, Universidad Catolica del Norte, Avenida Angamos 0610, Antofagasta, 1270709, Chile, hcaceres@ucn.cl, Carlos Olivos, Rajan Batta, Qing He In this research, we focus on the problem of locating ambulances to improve out- of-hospital response time, and thus, survival rates. The emergency medical service in Antofagasta keeps its ambulances at their bases, and the effectiveness of their location has not been assessed. We developed a dynamic relocation model that considers stochastic demand dependent on the time of day, the day of the week, traffic and emergency type. Mathematical formulations for this problem are developed and analyzed. Results from a case study along with algorithmic computational results will be presented. 3 - Nonlinear Optimization for Nonparametric Image Segmentation Maduka R. Balasooriya, Southern Illinois University-Edwardsville, Box 1653, Edwardsville, IL, 62026, United States, mbalaso@siue.edu, Sinan Onal, Xin W. Chen Precise object boundary detection for automatic image segmentation is critical for image analysis. However, such detection traditionally uses model and model-free based approaches requiring selection of optimal parameters. Identifying optimal parameter values requires time-consuming multiple runs and provides results that vary by user expertise. We are proposing a nonparametric model, in which the problem is formulated as a nonlinear program and solved using brute force search technique. The proposed model can be applied to any image datasets without using any preprocessing steps. 4 - Epidemic Control with Targeted Screening Policy Liqun Lu, University of Illinois at Urbana-Champaign, 205 N.Mathews Ave., Urbana, IL, 61801, United States, liqunlu2@illinois.edu, Yanfeng Ouyang Contagious diseases always pose threats to human society, and high social connectivity in the modern era further exacerbates epidemic propagation. This study establishes a modelling framework to describe epidemic dynamics in a multi-group social network, and then proposes and optimizes a control strategy to mitigate disease propagation via targeted screening. The effectiveness of such a control policy is demonstrated with numerical examples.

Vikrant Vaze, Dartmouth College, 14 Engineering Drive, Murdough Center, Hanover, NH, 03755, United States, vikrant.s.vaze@dartmouth.edu, Srinagesh Gavirneni, Omkar D. Palsule-Desai, Sobhan Asian

Need-based free healthcare is an emerging business model that is delivering greater social benefits concurrently with higher financial gains due to its underlying innovative operational, marketing, and philanthropic features. Using Aravind Eye Hospital in India as the setting, we evaluate the role of operational learning, spill-over effects of marketing, and customer segmentation in enabling the success of this philosophy that is increasingly attractive in communities with rampant inequality commonly found in developing countries.

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