Informs Annual Meeting 2017

MC05

INFORMS Houston – 2017

3 - Joint Carbon Abatement Efforts in Supply Chains Tarkan Tan, Eindhoven University of Technology, School of Industrial Engineering, Den Dolech 2 Paviljoen F-07, Eindhoven, 5612AZ, Netherlands, t.tan@tue.nl, Mohsen Reisi, Behnam Fahimnia We consider carbon emission abatement decisions taking manufacturer-supplier interactions into account. We present a set of models to identify the situations where cooperation can help invest in joint emissions abatement projects. Various approaches are used to allocate project costs based on the role and bargaining power of each project participant. 4 - Supplier Centrality and Auditing Priority in Socially-responsible Supply Chains Jiayu Chen, The University of Texas at Dallas, 800 W. Campbell Road, Richardson, TX, 75080, United States, jxc144030@utdallas.edu, Anyan Qi, Milind Dawande In a supply network with multiple buyers and multiple suppliers, we explore how the degree centralities of the suppliers (i.e., the number of buyers they supply to) affect the social-responsibility decisions of both the buyers and the suppliers, and offer several novel and useful insights. For instance, while prioritizing the compliance audit of suppliers with a high degree centrality is better for the aggregate profit of the firms, downstream competition between the firms conspires to make them audit only their respective independent suppliers. This inefficiency can be corrected if the firms cooperate to jointly audit the suppliers. 5 - Should Brands Tighten Certification Standards? the Effect of Supplier-certifier Collusion Shiqing Yao, Chinese Univ of Hong Kong, Flat C.8th floor, 320B Applied Operations in Health Services: Research by Bonder Scholars Sponsored: Health Applications Sponsored Session Chair: Pooyan Kazemian, Harvard Medical School, 50 Staniford St, 930B, Boston, MA, 02114, United States, pooyan.kazemian@mgh.harvard.edu Co-Chair: Alba Rojas-Cordova, Virginia Tech, Blacksburg, VA, 24060, United States, albarc@vt.edu 1 - Modeling Joint Inpatient and Outpatient Decisions to Reduce Hospital Readmissions Xiang Liu, University of Michigan, 322 Village Green Boulevard, Apt 204, Ann Arbor, MI, 48105, United States, liuxiang@umich.edu, Jonathan Helm, Mariel Sofia Lavieri, Ted Skolarus Hospital readmissions affect hundreds of thousands of patients, placing a tremendous burden on the healthcare system. We develop a two-stage stochastic dynamic programming framework that spans the inpatient stay and the post- discharge outpatient monitoring to reduce readmissions. By reducing readmission risk in the 1st stage, and monitoring the patient’s condition in the 2nd stage, our goal is to jointly guide discharge and post-discharge decisions. This could lead to reduced readmissions. 2 - Efficient and Fair Allocation Rules for Heart Transplantation Amin Khademi, Clemson University, 1267 Madden Bridge Rd Apt B, Central, SC, 29630, United States, khademi@clemson.edu, Farhad Hasankhani The optimal allocation of limited donated hearts to patients on the waiting list is one of the top priorities inheart transplantation management. In order to design an efficient and fair system for allocating donor heartsto patients waiting for transplantation, we model the problem as a constrained stochastic dynamic programto maximize total quality-adjusted life years (QALYs) of the population. Sunning Building, Shatin Ce, N.T., Hong Kong, yaoshiqing@gmail.com, Kaijie Zhu, Li Chen While enjoying low costs in sourcing from emerging economies, buyers also face serious quality risks in the form of supplier noncompliance. Supplier certification has been used widely to tackle such challenge in practice. In this paper, we study how supplier-certifier collusion can affect the buyer’s optimal contracting and certification strategy. MC05

3 - Resource Allocation Decision-making in Sequential Adaptive Clinical Trials Alba Rojas-Cordova, Assistant Professor, Southern Methodist University, Caruth Hall 339. Southern Methodist University.,

Dallas, TX, 75205, United States, alba@smu.edu, Ebru Korular Bish, Niyousha Hosseinichimeh

Adaptive clinical trials promise significant savings to the pharmaceutical industry and an expedited access to new drugs and therapies. Certain designs allow decision makers to alter the course of a trial based on interim results on a new drug’s performance. We develop: 1) a simulation model to study and quantify the risk of false negatives, and the amplification thereof as a result of a behavioral phenomenon, and 2) a stochastic dynamic programming model to derive an optimal resource allocation strategy, based on Bayesian updates on the estimate of a drug’s efficacy. We propose an augmented methodology for optimal stopping and optimal resource allocation in sequential adaptive clinical trials. 4 - Ambulance Emergency Response Optimization in Developing Urban Centres Justin James Boutilier, University of Toronto, 532 Palmerston Boulevard, Apartment 6, Toronto, ON, M6G 2P5, Canada, j.boutilier@mail.utoronto.ca, Timothy Chan Time sensitive medical emergencies are a major health concern comprising one third of all deaths in low and middle income countries (LMICs). Despite evidence that ambulance services can save lives, poor access and availability of emergency medical care in LMICs continues to be a widespread problem. In this paper, we develop a novel ambulance location-routing model, tailored to address the challenges faced by urban areas in LMICs. We use extensive field data from Dhaka, the capital of Bangladesh and one of the most densely populated cities on earth, to apply our modelling framework. 320C Hospital Operations Management I Sponsored: Health Applications Sponsored Session Chair: Hui Zhang, University of Chicago, Chicago, IL, 60637, United States, corinnaz@uchicago.edu Co-Chair: Thomas Best, tbest3@bsd.uchicago.edu 1 - Improving Hospital Operations using a Real-time Locating System Hyojung Kang, University of Virginia, 1557 Montessori Terrace, Charlottesville, VA, 22911, United States, hkang@virginia.edu, Ethan Haswell Long waiting times indicate costly inefficiencies for healthcare facilities and influence patient satisfaction and outcomes. Hospitals have adopted a Real-Time Locating System (RTLS) technology to identify bottlenecks in a system and improve its operations by tracking entities at all times throughout a facility. However, limited studies have shown how the RTLS data has been utilized to support operational decision-makings. This study demonstrates how the RTLS data combined with electronic health records and scheduling data can be used to provide hospitals insights into patient flow, patient-provider interactions, and scheduling. 2 - Predicting if New Patients in the Comprehensive Care Physician Program will Attend Most of their Appointments with their Comprehensive Care Physician Thomas J. Best, University of Chicago, Chicago, IL, United States, tbest3@bsd.uchicago.edu, Arnab Bose, Devdeep Banerji, Reelina Sircar, David O.Meltzer The Comprehensive Care Physician (CCP) program at the University of Chicago strives to provide more cost-effective healthcare for patients at high risk of hospitalization by enrolling them with a CCP who manages all their hospitalizations and outpatient care. Initial data suggests that the program can be especially cost-effective when enrollees attend most of their clinic appointments with their CCP. We describe our application of machine learning techniques to improve predictions of whether a new enrollee will attend most of their clinic appointments with their CCP in the first six months of enrollment. We also describe how we use the predictions to target efforts to improve attendance. 3 - Distance, Quality or Relationship? Interhospital Transfer of Heart Attack Patients Susan F. Lu, Purdue University, Krannert 441, West Lafayette, IN, 47907, United States, lu428@purdue.edu, Lauren Xiaoyuan Lu We empirically investigate the pattern of where heart attack patients are transferred between hospitals. Using 2011 Florida State Emergency Department and Inpatient Databases, we demonstrate the relative importance of three key factors in determining transfer destinations: (1) the distance between sending and receiving hospitals, (2) publicly-reported quality measures of receiving hospitals, and (3) the relationship between sending and receiving hospitals as indicated by whether they are affiliated with the same multihospital system. MC06

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