Informs Annual Meeting 2017

SD60

INFORMS Houston – 2017

SD60

3 - Standardized Patient Mentoring Program for Kidney Transplant Patients Yeojun Chun, The Ohio State University, 4438 Mobile Dr #306, Columbus, OH, 43220, United States, chun.159@osu.edu Approximately 1 in 3 post-transplant kidney recipients are readmitted to the hospital within 30 days of initial discharge. Patient anxiety and lack of adherence to post-transplant discharge instructions contribute to readmission rates. To minimize patient anxiety and occurrence of preventable readmissions, a standardized patient mentoring program was designed and tested in a randomized controlled experiment. Standardization focused on the interactions specific to post-transplant adherence to the medical regimen. Preliminary analysis of the first two months of data will be shared. 370C City Logistics Advances Sponsored: TSL, Freight Transportation & Logistics Sponsored Session Chair: Tom Van Woensel, Eindhoven University of Technology, Eindhoven University of Technology, Mb Eindhoven, NL5600, Netherlands, t.v.woensel@tue.nl 1 - Coordinated Delivery to Nanostores in Traditional Retail Channel Ruidian Song, Tsinghua University, Tsinghua University, Beijing, China, srd13@mails.tsinghua.edu.cn, Lei Zhao, Tom Van Woensel, Jan C.Fransoo Tens of thousands of traditional mom-and-pop grocery stores (nanostores) aggregately remain a major retail channel in megacities in emerging economies. However, these independently operated stores also create a large amount of high frequency, small size, and uncoordinated replenishment orders. We model, study, and compare various coordination strategies to delivery to these stores and study the impact of delivery coordination. 2 - The Pickup and Delivery Problem with Transfers Lucas Petrus Veelenturf, Eindhoven University of Technology, School of Industial Engineering (PAV.F.12), P.O. Box 513, Eindhoven, 5600 MB, Netherlands, l.p.veelenturf@tue.nl, Afonso Henrique Sampaio Oliveira, Martin W. P.Savelsbergh, Tom Van Woensel In this research we include transfer opportunities in the pickup and delivery problem with time windows to construct more cost-effective and robust transportation plans. Transfer points are locations in the network where requests can be transferred between vehicles and temporarily stored. Hence, more than one vehicle can be used to serve a request, e.g., a request may be picked up at its origin by one vehicle, then dropped off at a transfer point where another vehicle (with other characteristics) will pick it up and drop it off at its destination. The introduction of transfer opportunities allows serving more requests with a given set of vehicles and/or serving a given set of requests with fewer vehicles. 3 - Business-to-consumer E-commerce: Home Delivery in Megacities Martin W. P.Savelsbergh, Georgia Institute of Technology, School of Industrial and Systems Engineering, Atlanta, GA, 30332-0205, United States, martin.savelsbergh@isye.gatech.edu, Lei Zhao, Yixiao Huang We study the design and deployment of two-echelon logistics systems for home delivery in megacities. We show that by restricting the set of potential routes deployed in one or both of the echelons, it is possible to significantly reduce the complexity of the delivery operations without sacrificing performance. 4 - An Exact Solution Approach for the Multi-commodity Two-echelon Vehicle Routing Problem with Time Windows Tom Van Woensel, Eindhoven University of Technology, Industrial Engineering, Den Dolech 2, Pav F08, Mb Eindhoven, NL5600, Netherlands, t.v.woensel@tue.nl The two-echelon vehicle routing problem (2E-VRP) is a two-echelon distri- bution system where goods are transferred to customers by using intermediate facilities (called satellites). We take non-substitutable demands into account by introducing commodities: a commodity consists of the destination customer, origin depot, a specific volume, and a time window which the delivery should take place within. The described problem is referred as the multi-commodity two- echelon vehicle routing problem with time windows (MC-2E-VRPTW). Numerical results are presented. SD62

370A Joint session ANA/EDU: Building a Market-Relevant Analytics Program Sponsored: INFORMEd Sponsored Session Chair: Jeffrey D Camm, Wake Forest University, Wake Forest University, Winston-Salem, NC, 27109, United States, cammjd@wfu.edu 1 - Moderator Jeffrey D. Camm, Wake Forest University, School of Business, PO.7897, Winston-Salem, NC, 27109, United States, cammjd@wfu.edu In this panel, we discuss market-driven curricula in business analytics. We compare and contrast a variety of programs, including an undergraduate major and several different MS in Business Analytics programs. We will discuss what works and potential pitfalls to avoid in developing an analytics program. 2 - Panelist Melissa R. Bowers, University of Tennessee-Knoxville, 242 Stokely Management Center, Statistics, Operations, & Management Science, Knoxville, TN, 37996, United States, mrbowers@utk.edu 3 - Panelist Michael Fry, University of Cincinnati, Cincinnati, OH, United States, fryml@ucmail.uc.edu 4 - Panelist Goutam Chakraborty, Oklahoma State University, Stillwater, OK, United States, chakraborty@okstate.edu 370B Joint Session MIF/HAS: Decision Making in Healthcare Sponsored: Minority Issues Sponsored Session Chair: Shannon Harris, The Ohio State University, Columbus, OH, 43212-3085, United States, harris.2572@osu.edu 1 - Emergency Department Capacity Planning and Scheduling Decisions within a Large Multi-facility Network The growing trend of emergency department (ED) consolidation in the US has intensified the complexity of managing emergency physicians. Large-scale physician groups are simultaneously challenged with capacity planning and scheduling decisions across many facilities. We propose a model to optimally staff facilities throughout the network with emergency physicians and advanced practice providers. 2 - The Benefit of Waiting: A Bayesian MDP to Evaluate Mode of Delivery Karen T. Hicklin, University of North Carolina at Chapel Hill, 308 Bynum Hall, Chapel Hill, NC, 27599, United States, khicklin@email.unc.edu, Julie Simmons Ivy Optimal mode of delivery decisions lead to better short- and long-term health outcomes for mothers and children. When a woman enters labor, she will either have vaginal or cesarean delivery. However, at the onset of labor, the delivery outcome is unknown. Patients who have slow or stalled labor progression are considered failure-to-progress thus leading to the need for a C-section. In this model formulation, we combine Bayesian updating into a Markov decision process to determine under what circumstances it is appropriate to gather more information before making a decision regarding mode of delivery as a function of cervical dilation progression and patient type (i.e., failure-to-progress or not). SD61 Krista Foster, University of Pittsburgh, 1622 Beechwood Boulevard, Apt 1, Pittsburgh, PA, 15217, United States, kmf88@pitt.edu, Jennifer S.Shang

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