2016 INFORMS Annual Meeting Program

TA33

INFORMS Nashville – 2016

TA33 203B-MCC

1 - Ed Physician Staffing Via Multi-stage Multi-class Network Caglar Caglayan, Georgia Institute of Technology, Atlanta, GA, 30318, United States, ccaglayan6@gatech.edu, Mustafa Y Sir, Kalyan Pasupathy, Turgay Ayer, Yunan Liu We propose an “intuitive”, “realistic” and “tractable” model of the emergency department (ED) by a multi-class multi-stage queuing network with multiple targeted service levels. Based on infinite-server approximation and offered load analysis, we employ a modified version of square-root safety principle to determine the right number of physicians in the ED. Our model is detailed enough to capture the key dynamics of the ED but simple enough to understand, infer results and implement in a clinical setting. 2 - Discrete Event Simulation Of Outpatient Flow In A Phlebotomy Clinic Elizabeth Olin, University of Michigan, 1205 Beal, Ann Arbor, MI, 48109, United States, genehkim@umich.edu, Amy Cohn, Ajaay Chandrasekaran The University of Michigan Comprehensive Cancer Center handles approximately 97,000 outpatient visits annually, with most including a blood draw, clinic appointment, preparation of infusion drugs, and an infusion appointment. The goal of our project is to reduce patient waiting times at the phlebotomy (blood draw) clinic, which appears to be a primary bottleneck in the patient experience. In order to accomplish this goal, we developed a discrete-event simulation of the clinic’s patient and work flow. By adjusting the various simulation parameters, we can evaluate alternative methods to improve turnaround time, patient wait time, and phlebotomist utilization. 3 - Quantifying Provider’s Schedule Effects On Patient’s Length-of-Stay Kimia Ghobadi, MIT, Cambridge, MA, United States, kimiag@mit.edu, Andrew Johnston, Retsef Levi, Walter O’Donnell We identify a natural randomized control setting between providers’ schedule and patients arrival in a congested Department of Medicine in a large academic hospital. We use this setting to build a predictive model and quantify the impact of care team handoff on patients’ length-of-stay. TA35 205A-MCC Online Services: Learning and Pricing Sponsored: Manufacturing & Service Oper Mgmt, Service Operations Sponsored Session Chair: Yash Kanoria, Columbia University, Graduate School of Business, New York, NY, 10027, United States, ykanoria@columbia.edu Co-Chair: Vijay Kamble, Stanford University, Stanford, CA, 9, United States, vijaykamble.iitkgp@gmail.com 1 - Efficiency And Performance Guarantees For Network Revenue Management Problems With Customer Choice David Simchi-Levi, Massachusetts Institute of Technology, Dept of Civil and Environmental Engineering, 77 Massachusetts Avenue Rm 1-171, Cambridge, MA, 02139, United States, dslevi@mit.edu, Wang Chi Cheung We consider the network revenue management problem with customer choice. While the solution to the Choice-based Deterministic Linear Program (CDLP) can be used to design a near-optimal policy, CDLP has an exponential size. We propose algorithms that solves CDLP with polynomially many elementary operations and invocations to an oracle that solves the underlying single period problems. Next, we design an efficient online algorithm for the problem with MNL choice models, where the parameters are unknown. The algorithm achieves a regret of O(T2/3), where T is the length of the time horizon. 2 - Optimal Version Updates Mobile apps have become an economy with a market size of $25 Billion in 2013 and with a projected market size of $77 Billion by 2017. One of the key features that distinguishes mobile apps from other types of digital goods (such as movies, songs or books) is the they have versions. A developer can release an app into a mobile app store, and can then keep adding, removing or editing features of the app with subsequent version updates. We study empirically and theoretically the optimal strategy for such updates. Gad Allon, Northwestern University, g-allon@kellogg.northwestern.edu

Queueing Models Contributed Session

Chair: Pedro Cesar Lopes Gerum, PhD Student, Rutgers University, 96 Frelinghuysen Road, CoRE Building, Room 201, piscataway, NJ, 8854, United States, pedro.gerum@rutgers.edu 1 - Mean Value Analysis Of Mixed Queuing Networks Ivo Adan, Eindhoven University of Technology, Den Dolech 2, Eindhoven, 5600 MB, Netherlands, I.Adan@tue.nl, Vidyadhar Kulkarni We study a mixed queuing network with multi-server stations. The mixed network has both closed and open network components: it has a fixed number of customers (called permanent customers) that circulate among the service stations indefinitely, and it also serves customers (called transient customers) who enter from outside, visit the stations in a random order and leave. We develop novel mean-value equations for recursively computing the mean queue lengths and mean waiting waiting times, and we study the asymptotic behavior of these quantities in the presence of multiple bottle-neck stations as the number of permanent customers tends to infinity. 2 - Simple And Efficient Ways For Discrete GI/G/1 Queues Winfried K Grassmann, Professor emeritus, University of Saskatchewan, 110 Science Place, 176 Thorvaldson Building, Saskatoon, SK, S7N 5C9, Canada, grassman@cs.usask.ca We present a number of simple and not so simple methods to find the distribution of the number of elements in a GI/G/1 queue and related problems. As it turns out, many methods described in literature are mathematically challenging, but this does not imply that they are numerically efficient numerically. In fact, it is our experience that the simpler methods tend to be the most efficient ones, while also easy to understand. This leads to the suspicion that criteria for publication typically favor mathematical elegance over practical usefulness. 3 - A Queueing System With On-demand Servers: Local Stability Of Fluid Limits Lam M Nguyen, PhD Student, Lehigh University, 200 West Packer Avenue, Room 362, Bethlehem, PA, 18015, United States, lmn214@lehigh.edu, Alexander Stolyar We consider a system, where a random flow of customers is served by agents invited on-demand. Each invited agent arrives into the system after a random time, and leaves it with some probability after each service completion. Customers and/or agents may be impatient. The objective is to design a real-time adaptive invitation scheme that minimizes customer and agent waiting times. We consider a queue-length-based feedback scheme; study it in the asymptotic regime where the customer arrival rate goes to infinity; and derive a variety of sufficient conditions for the system local stability at the desired equilibrium point. Under these conditions, simulations show good overall performance of the scheme. 4 - Traffic Density Analytical Model Validation And Applications Pedro Cesar Lopes Gerum, PhD Student, Rutgers University, 96 Frelinghuysen Road, CoRE Building, Room 201, piscataway, NJ, 08854, United States, pedro.gerum@rutgers.edu, Melike Baykal- Gursoy, Marcelo Ricardo Figueroa This paper compares a general equation for the probability generating function of density for a general road system, discovered by W. Xiao and Baykal-Gursoy, with real data from Milwaukee, Wisconsin. Furthermore, once shown the analytical model is valid, this paper presents some insights in possible applications taken from these formulations. These insights include improving efficiency of evacuation in extreme scenarios, such as flooding or other weather conditions; providing useful information to decision-makers on how to better invest their money in infrastructure; allowing the end-user of a routing system to choose between routes according to the risk of delay he is willing to take. Provider Staffing and its Impact on Patient Flow Sponsored: Manufacturing & Service Oper Mgmt, Healthcare Operations Sponsored Session Chair: Retsef Levi, MIT, 100 Main Street, Building E62-562, Cambridge, MA, 02142, United States, retsef@mit.edu Co-Chair: Cecilia Zenteno, Massachusetts General Hospital, 55 Fruit Street, White 400, Boston, MA, 02114, United States, azentenolangle@mgh.harvard.edu TA34 204-MCC

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