2016 INFORMS Annual Meeting Program

TC34

INFORMS Nashville – 2016

3 - Evaluating The Impact Of Adopting 3d Printing Services On The Retailers Sharareh Rajaei dehkordi, PhD Student of industrial engineering, New Jersey Institute of Technology, 10 Hill Street, Apt 2N, Newark, NJ, 07102, United States, sr552@njit.edu, Wenbo (Selina) Cai As 3D printing technology becomes more agile to react to customers’ demands, one important question for the retailers is whether they should provide 3D printing services in their brick-and-mortar store in addition to the traditional off- the-shelf product? If so, what is the pricing scheme that achieves the optimal profit? What is the optimal capacity of the 3D printers? In this study we answer these questions by examining retailer’s optimal joint decisions on pricing scheme and capacity while considering consumers preferences for self-designed, 3D printed products versus off-the-shelf products, using queueing systems and stochastic optimization models. 4 - Choice-based Revenue Management Under Online Reviews Dirk Daniel Sierag, CWI, Science Park 123, Amsterdam, 1098 XG, Netherlands, dirk@cwi.nl This article proposes a choice-based network revenue management model that integrates the effect of reviews. Application areas include airlines, hotels, and rental cars. The dependency between reviews and revenue is two-fold: the content of a review depends on the product the customer purchases, and reviews impact the demand. A complicating factor in this model is that the effects of reviews are delayed, i.e., by sacrificing revenue now in order to get better reviews, long-term revenue can be increased. Novel solution methods are proposed that exploit the presence of reviews in order to optimise revenue. 5 - Customer’S Strategic Behavior Using Thompson Sampling Sareh Nabi Abdolyousefi, University of Washington, 2727 NE 55th Street, Seattle, WA, 98105, United States, snabi@uw.edu Retailer is pricing dynamically in order to maximize his cumulative expected revenue in a multi-armed bandit setting. Retailer has no information regarding expected demand and type of customers he is facing, myopic or strategic. He is applying a machine learning technique, updated Thompson Sampling, to learn expected demand and customer’s type in an exploration vs exploitation fashion. We have proved analytically that retailer’s long run price is lower for strategic customers compared to myopic ones. We have also shown numerically that retailer can be better off with strategic customers. TC33 203B-MCC Queueing Models II Contributed Session Chair: Iqra Ejaz, Texas A&M University, College Station, TX, United States, iejaz@tamu.edu 1 - Multiple Server Preemptive Scheduling With Impatience Yang Cao, University of Southern California, Los Angeles, CA, 90089, United States, cao573@usc.edu We study a scheduling problem with n impatient customers to be served by m parallel servers (n>m). We assume that the impatience time of customers in queue and the service time on servers are all exponentially distributed, and the system earns a positive reward upon each service completion. We consider both the case of non-preemptive servers and the case of preemptive servers. The objective is to maximize the expected total return for both cases. We give conditions under which a list policy is optimal. 2 - Optimal Control Of General Dynamic Matching System Mohammadreza Nazari, PhD Student, Lehigh University, Lehigh University, Murray H Goodman Campus, Bethlehem, PA, 18015, United States, mon314@lehigh.edu, Aleksandr Stolyar Consider a system with random arrivals of items of multiple types. There is a finite number of possible matchings, each being a subset of item types. Each matching has associated fixed reward, and matched items leave the system. We propose a matching algorithm and prove its asymptotic optimality in the sense of maximizing the long-term average reward, while keeping the item queues stable. This algorithm applies an extended version of the greedy primal-dual (GPD) algorithm to a virtual system, which allows negative item queues. 3 - Acuity-based Nurse-staffing Strategies For Inpatient Settings Using A Stochastic Modeling Approach Parisa Eimanzadeh, PhD Student, Wichita State University, 6000 E Mainsgate Street, Apt 108, Wichita, KS, 67220, United States, pxeimanzadeh@wichita.edu, Ehsan Salari Minimum nurse-to-patient ratios have been traditionally used to guide staffing decisions in inpatient units. However, the severity of nursing care may vary across inpatients, rendering those ratios ineffective. We develop a stochastic modeling framework to quantify the impact of different staff levels on the performance of inpatient units while accounting for heterogeneity in patient acuity and staff nursing skill levels.

4 - Condition-based Maintenance For Queues With Degrading Servers

Iqra Ejaz, Texas A&M University, College Station, TX, United States, iejaz@tamu.edu, Michelle M. Alvarado, Nagi Gebraeel, Natarajan Gautam, Mark Alan Lawley We derive an analytical model for condition monitoring of a single server queue with Markovian degradation, Poisson arrivals, and general service and repair times. Stability conditions and performance measures (e.g., average queue length, average degradation.) are derived through steady state analysis. An optimal repair decision model is presented that minimizes an objective function with four costs: repair, catastrophic failure, quality and holding. We develop and verify a simulation model, perform a sensitivity analysis, and show insights learned from relaxing underlying assumptions. 204-MCC Public Policy and Healthcare Operations Sponsored: Manufacturing & Service Oper Mgmt, Healthcare Operations Sponsored Session Chair: Susan F Lu, Purdue University, West Lafayette, IN, United States, lu428@purdue.edu Co-Chair: Lauren Xiaoyuan Lu, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, lauren_lu@unc.edu 1 - Do Mandatory Overtime Laws Improve Quality? Staffing Decisions And Operational Flexibility Of Nursing Homes Lauren Xiaoyuan Lu, University of North Carolina at Chapel Hill, Chapel Hill, NC, United States, lauren_lu@unc.edu, Susan F Lu During the 2000s, over a dozen U.S. states passed laws that prohibit health care employers from mandating overtime for nurses. Using a nationwide panel dataset from 2004 to 2012, we find that these mandatory overtime laws reduced the service quality of nursing homes, as measured by an increase in deficiency citations. This outcome can be explained by two undesirable changes in the staffing hours of registered nurses: decreased hours of permanent nurses and increased hours of contract nurses per resident day. 2 - Predicting Nurse Turnover And Its Impact On Staffing Decisions Eric Webb, Indiana University, Bloomington, IN, United States, ermwebb@indiana.edu, Kurt Bretthauer Nurse turnover remains a significant problem in skilled nursing facilities across the United States. High turnover leads to two important questions: (1) Hiring decisions - What applicant attributes should be valued when hiring nurses, in order to hire nurses that are effective at their jobs and likely to stay for a long duration? (2) Staffing decisions - How should nurse workload be managed in order to prevent burnout and decrease turnover? Based on a large dataset from skilled nursing facilities in the United States, we first use a survival model to predict nurse turnover. For this talk we then focus on staffing and incorporate these empirical results into analytical models for nurse staffing decisions. 3 - Hospital Readmissions Reduction Program: An Economic And Operational Analysis Dennis Zhang, Washington University in St. Louis, St. Louis, MO, 90024, United States, zjj1990228@gmail.com The Hospital Readmissions Reduction Program (HRRP) requires the Centers for Medicare and Medicaid Services to penalize hospitals with excess readmissions. We take an economic and operational (patient flow) perspective to analyze the effectiveness of this policy in encouraging hospitals to reduce readmissions. We develop a game-theoretic model to show that the competition among hospitals can be counterproductive: it increases the number of nonincentivized hospitals. We calibrate our model with a data set of more than 3,000 hospitals and draw several policy recommendations to improve this policy’s outcome. TC34

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