Informs Annual Meeting 2017

TB42

INFORMS Houston – 2017

TB39

4 - Float Nursing Strategy: An Alternative Short Term Strategy in Hospitals to Minimize Nurse Shortage and Decrease Total Labor Cost Kamil Ciftci, PhD Candidate, Lehigh University, 200 West Packer Avenue, Bethlehem, PA, 18015, United States, kac208@lehigh.edu Float nursing strategy has been discussed in literature to reduce labor cost and provide more scheduling flexibility. However, float nursing strategy may decrease service quality because of changing work environment and location and no direct responsibility to assigned department. In this study, we investigate the effects of float nursing strategy using different regular nurse levels. While we find optimal number of nurses need to be hired as a float nurses, we determine how much more than based salary health Network should offer to them in order to decrease total labor cost. A case study with real data from a local hospital is used to prove efficiency of proposed approach. 5 - Adaptive Appointment Scheduling for Patient Centered Medical Homes Ali Kemal Dogru, University of Alabama, Box 870226, Tuscaloosa, AL, 35487, United States, akdogru@crimson.ua.edu, Sharif Melouk Incorporating patient centered medical home (PCMH) principles, we develop an adaptive appointment scheduling model for a primary care setting. We propose a simulation optimization approach to sequentially schedule appointments to provide desirable schedules from the perspective of both patients and the medical practice. Our data-driven algorithm is efficient and takes patient preferences into account. We benchmark against myopic and optimal algorithms. Computational results show that the adaptive scheduling approach provides significant value. Key Words: Appointment Scheduling, Simulation, Patient Centered Medical Home 6 - Sequential Clinical Scheduling with Patient Re-entrant and No-show: the Case with MOHS Micrographic Surgery for Skin Cancer Haolin Feng, Sun Yat-sen University, Lingnan College, Guangzhou, 510275, China, FengHaoL@mail.sysu.edu.cn, Mark Alan Lawley, Stephen Steidle Mohs Micrographic Surgery (MMS) is a surgical method for the excision of skin cancers. It repetitively removes one skin layer at a time, with each removed layer sent for pathology examination until a cancer free layer is found. This results in same-day patient re-entrants. Currently patients often experience long in-clinic waiting due to the stochastic nature of layer removals and pathology. To improve patient experience and clinic revenues, we develop a model for MMS clinic scheduling. The model captures the key characteristics of the surgery-pathology and stochastic re-entrants. Theoretical results and numerical study demonstrating the improvement are provided. 360A Precision Manufacturing Invited: Invited OR and Advanced Manufacturing Invited Session Chair: Prahalad Rao, University of Nebraska-Lincoln, University of Nebraska-Lincoln, Liconoln, NE, 68506, United States, prahalad.k.rao@gmail.com 1 - Quantifying Geometric Accuracy with Unsupervised Machine Learning: using Self-Organizing Map on Fused Filament Fabrication Additive Manufacturing Parts Mojtaba Khanzadeh, Mississippi State University, Industrial and Systems Engineering Department, P.O. Box 9542, Mississippi State University, MS, 39762, United States, mk1349@msstate.edu, Linkan Bian Complex geometries are attainable with additive manufacturing (AM), a major barrier preventing its use in mission-critical applications is the lack of geometric accuracy of AM parts. The objective of this work is to quantify the geometric deviations of additively manufactured parts from a large data set of laser-scanned coordinates using an unsupervised machine learning approach called the self- organizing map. The outcomes of this research are: (1) visualizing and quantifying the link between process conditions and geometric accuracy in FFF; and (2) significantly reducing the amount of point cloud data required for characterizing of geometric accuracy. TB42

352D Collaborations in OR Practice Sponsored: The INFORMS Section on Practice Sponsored Session Chair: Michael F. Gorman, University of Dayton, Dayton, OH, 45469, United States, michael.gorman@udayton.edu 1 - Collaborations in OR Practice Michael F.Gorman, University of Dayton, 300 College Park, Dayton, OH, 45469, United States, michael.gorman@udayton.edu Practitioners and academics share their best-practices and their mistakes in collaboration between academia and the private sector. The audience should expect to find out how to best structure such relationships. This Session has an open question and answer format. 2 - Panelist Jeffrey D.Camm, Wake Forest University, School of Business, PO.7897, Winston-Salem, NC, 27109, United States, cammjd@wfu.edu 3 - Panelist Glenn Wegryn, Executive Director, Center for Business Analytics, University of Cincinnati, Cincinnati, OH, United States, wegryngn@ucmail.uc.edu 4 - Panelist Pooja Dewan, BNSF Railway, 2650 Lou Menk Drive, #2, Fort Worth, TX, 76131, United States, pooja.dewan@bnsf.com 5 - Panelist Michael Rappa, North Carolina State University, Raleigh, NC, 27606, United States, mrappa@ncsu.ed Kjartan Kastet Klyve, PhD Student, Norwegian University of Science and Technology, Prinsens gate 61, Trondheim, 7011, Norway, kjartan.klyve@ntnu.no, Henrik Andersson, Marielle Christiansen, Anders Nordby Gullhav We present a tactical staff-scheduling problem at the neonatal intensive care unit at a Norwegian hospital, with a planning horizon of 6 months. We expand the scope of the typical tactical scheduling problem by formulating a scheduling model that produces schedules that are robust, i.e. well prepared for operational disruption. A simulation model, simulating staff absence and requirement at the ward, is also developed. We can thus test different strategies for robust scheduling in the scheduling model and evaluate the robustness of them using extensive historical data and the simulation model. 2 - Optimal Elective Surgery Scheduling Subject to Disruptions of Emergent Cases Xiaoqiang Cai, Chinese University of Hong Kong, Dept of Systems We consider the scheduling of elective surgeries in an operation theater that has to treat emergent patients with priority when they arrive (randomly). The problem is to find the optimal policy to determine the sequence of all operations in waiting. Optimal policies are derived based on the theory and techniques of stochastic scheduling and open bandit processes. 3 - Nursing Skill-mix Optimization in Inpatient Care Units using a Queueing Theory Approach Parisa Eimanzadeh, Wichita State University, 1845 N.Fairmount,, Hospitals often use nursing staff with different skill levels in inpatient units for cost-saving purposes. Highly trained nurses can perform all types of tasks while lower-skilled staff can only perform limited tasks. Additionally, the care intensity for each patient involves uncertainty and varies depending on their acuity. This research aims at finding optimal skill-mix configurations that minimize staffing costs while ensuring timely delivery of nursing care. A finite-source queueing model with multiple patient classes and server types is developed to analyze the nursing care delivery. Eng & Eng Management, Shatin NT, Hong Kong, xqcai@se.cuhk.edu.hk, Xianyi Wu, Xian Zhou Wichita, KS, 67260-0035, United States, p.eimanzadeh@gmail.com, Ehsan Salari TB41 352F Health Care, Modeling and Optimization Contributed Session Chair: Haolin Feng, Sun Yat-sen University, Guangzhou, China, FengHaoL@mail.sysu.edu.cn 1 - Robust Nurse Scheduling

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