Informs Annual Meeting 2017

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INFORMS Houston – 2017

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361B Practice/Healthcare III Contributed Session Chair: Najibe Sadatijafarkalaei, Wayne State University, 4815 Fourth Street, Detroit, MI, 48202, United States, fv0017@wayne.edu 1 - Managing Appointment Scheduling with Walk-ins under Two- agent Perspective Shenghai Zhou, Shanghai Jiao Tong University, Shanghai, China, zshsjtu2014@sjtu.edu.cn, Guohua Wan Consider the healthcare appointment system with walk-ins, we develop a two- agent optimization model to determine the optimal template. We demonstrate that, under a mild assumption, the optimal structures are fundamentally different from those existing literature which does not consider two-agent. For the general case, the problem can be formulated as a two stage stochastic optimization model. Numerical analysis is also used to demonstrated the effectiveness of the proposed method. 2 - Managing Operational Efficiency & Health Outcomes at Outpatient OB/GYN Clinics through Effective Scheduling Samira Fazel Anvaryazdi, PhD Candidate, Wayne State University, We discuss methods for improving flow at outpatient clinics through effective appointment scheduling policies by applying two-stage stochastic programming approach. The aim is to increase throughput per session while providing timely care, continuity of care, and overall patient satisfaction as well as equity of resource utilization. 3 - Fair Allocation of Donor Hearts for Transplantation Farhad Hasankhani Kohneh Shahri, PhD Student, Clemson University, 278 Freeman Hall, Clemson, SC, 29634, United States, fhasank@clemson.edu, Amin Khademi The optimal allocation of limited donated hearts to patients on the waiting list is one of the top priorities in heart transplantation management. In order to design an efficient and fair system for allocating donor hearts to patients waiting for transplantation, we model the problem as a constrained stochastic dynamic program to maximize total quality-adjusted life years (QALYs) of the population. Because solving the optimality equation is intractable in our setting, we use fluid approximation, which is an optimal control problem, to produce allocation rules. We assess the quality of any allocation rule by providing an upper bound to the optimal value function. 4 - Optimization of Inventory for Hip and Knee Joint Operations via Multistage Stochastic Programming Mohammad Pirhooshyaran, Lehigh University, 7 Duh Drive Apt.113, Bethlehem, PA, 18015, United States, mop216@lehigh.edu, Lawrence V. Snyder Knee and hip joint replacements are among the most common orthopedic operations. Supplying hospitals with prosthetic implants is difficult because the supply is limited, the demand is stochastic. Often the supply logistics fall to the manufacturer or a distributor. We present a multi-stage stochastic optimization model to optimize the distribution of implants from a central depot to hospitals. We find that our model can reduce costs and improve service compared to how our industry partner currently distributes implants. 5 - Interaction between Operational Efficiency and Doctor Incentives in Outpatient Services Xiaofang Wang, Renmin University of China, Mingshang Building, Room 1023, Beijing, 100872, China, xiaofang.wang@gmail.com, Guoming Lai This paper studies the interaction between a doctor and a population of patients in a congested health service delivery system. The doctor’s prescribing decisions depend on her diagnostic/treatment ability, level of altruism and the institutional framework. Some patients are strategic and decide to see this doctor based on perceived quality, congestion and monetary costs. Within such a setting, we study the socially optimal decisions and provide policy insights. 6 - Perspectives on Real Time Orchestration of Operations in Health Systems Najibe Sadatijafarkalaei, Wayne State University, 4815 Fourth Street, Detroit, MI, 48202, United States, fv0017@wayne.edu, Ratna Babu Chinnam, Alper E.Murat Most hospital systems in the U.S. have employed some form of an EHR system in recent years to improve health outcomes. While EHR systems form a critical data backbone for improving operational efficiencies, there is a need for platforms that can promote situational awareness, predictive operational intelligence, and eventually coordinated orchestration. We present findings from the literature and observations derived from major healthcare systems in SE-Michigan. Detroit, MI, United States, samira.fazel@wayne.edu, Saravanan Venkatachalam, Ratna Babu Chinnam

361C Autonomous Systems Optimization II Sponsored: TSL, Urban Transportation Sponsored Session Chair: Jian Wang, Purdue University, 4550, West Lafayette, IN, United States, wang2084@purdue.edu 1 - Stable User Matching for Autonomous Vehicle Co-Ownership Problem Anpeng Zhang, University at Buffalo, Buffalo, NY, 14228, United States, anpengzh@buffalo.edu We develop a user matching for autonomous vehicle co-ownership programs based on stable matching and single-vehicle Dial-A-Ride Problem (DARP). The optimization model will determine the optimal schedule of trips in the same car sharing group as well as a fair match for all participants in the program. Our model also utilizes nash bargaining game to consider the utility balance between the members in the same group. A case study based on the travel activity dataset of households in California will be present to show the matching performance. 2 - Commute Ridesharing with Autonomous Vehicles Considering Work End Time Uncertainty Yan Zhao, University of Michigan, Ann Arbor, MI, 48109, United States, zhaoyann@umich.edu, Xiaolei Guo, Henry Liu Uncertainty of work end time can prevent ridesharing between two people with identical home and work locations. When two people share a ride from home to work, if the realized work end time results in a long wait between the two, with traditional cars, one has to either bear the long wait or call expensive taxi service. We demonstrate that autonomous vehicles (AV) can increase ridesharing in the context of commute bottleneck congestion with random work end time, where one can share ride from home to work in the morning, and either share ride or call AV to pick her up from work to home in the afternoon, depending on the realized work end time. 3 - Exploring Braess’s Paradox for Congested Pedestrian Crowd in Continuous Space Zhaodong Wang, University of Illinois, Urbana, IL, United States, zwang137@illinois.edu, Yanfeng Ouyang This paper explains continuous Braess’s Paradox under congestion by proposing a continuous user equilibrium model describing the steady state of pedestrian flow. Moreover, the exact overall performance improvement can be quantitatively obtained by our framework, which enables us to develop an optimization-based approach seeking optimal infrastructural design. 4 - Optimal Deployment of Autonomous Vehicle Lanes under Network Equilibrium Jian Wang, Purdue University, West Lafayette, IL, United States, wang2084@purdue.edu, Srinivas Peeta, Sean X He This study develops a bi-level program to facilitate the deployment of autonomous vehicle (AV) lanes. The upper-level program seeks to reduce the total travel time, while the lower-level program formulates the network equilibrium with the mixed traffic of AVs and non-AVs. Numerical examples are used to investigate the optimal deployment of AV lanes that provides insights into network design to better accommodate AV-based trips. 361D Telecommunications Contributed Session Chair: Dawoon Jung, Korea University, Seoul, United States, bogsil@korea.ac.kr 1 - Evaluating Antecedents and Consequences of Telemedicine Adoption in Clinics Xiaojin Liu, University of Virginia, 100 Darden Blvd, Charlottesville, VA, 22903, United States, liux@darden.virginia.edu, Susan Goldstein, Karen Soderberg, Kingshuk K.Sinha Telemedicine, the technology-enabled remote delivery of clinical care, is one potential means to improve patient access to health care services. We investigate how socioeconomic, geographical, and organizational characteristics determine telemedicine adoption in clinics, and examine how telemedicine interacts with technological characteristics on health care delivery effectiveness. TE51

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