2016 INFORMS Annual Meeting Program

WA33

INFORMS Nashville – 2016

WA33 203B-MCC Production and Scheduling I Contributed Session

2 - Proactive Patient Service: An Operational And Economic Analysis Kraig Delana, London Business School, kdelana@london.edu, Nicos Savva, Tolga Tezcan We present an operational and economic analysis of the value derived from healthcare providers using knowledge of upcoming patient care needs to proactively initiate service. On the operational side, we develop a novel queueing model to show that proactive service leads to significant reduction in delays. On the economic side, we show that if proactive service generates an inconvenience cost, strategic patients are less likely to choose to adopt than socially optimal due to economic frictions. This has important implications for providers working to implement proactive service in practice. 3 - Online Surgical Case Scheduling With Repeating Blocks Shashank Goyal, University of Minnesota, Minneapolis, MN, United States, goyal030@umn.edu, Diwakar Gupta Hospitals often assign surgeons a fixed number of blocks of operating-room time on a periodic basis. The value and the duration of a surgery request are revealed upon its arrival. The surgeon must place it in one of the blocks or decline it without knowing the future requests. This decision must respect capacity constraints and cannot be modified at a later time because the patients have to make arrangements for travel and post-operative care. The aim is to maximize the total value of accepted requests. We model the problem as the online multiple knapsack problem and propose algorithms that have provable worst-case performance. We also present bounds on the best performance that any algorithm can achieve. 4 - Data-driven Patient Scheduling In Emergency Departments: A Hybrid Robust-stochastic Approach Meilin Zhang, National University of Singapore, meilin.zhang@u.nus.edu Emergency care necessitates adequate and timely treatment, which has unfortunately been compromised by crowding in many emergency departments (EDs). To address this issue, we study patient scheduling in EDs so that mandatory targets imposed on each patient’s door-to-provider time and length of stay can be collectively met with the largest probability. Exploiting patient flow data from the ED, we propose a hybrid robust-stochastic approach to formulating the patient scheduling problem, which allows for practical features such as a time-varying patient arrival process, general consultation time distributions, and multiple heterogeneous physicians. WA35 205A-MCC Topics in Service Operations Sponsored: Manufacturing & Service Oper Mgmt, Service Operations Sponsored Session Chair: Yong-Pin Zhou, University of Washington, Seattle, Michael G. Foster School of Business, Seattle, WA, 98195, United States, yongpin@uw.edu 1 - Search Among Queues Under Quality Differentiation Luyi Yang, University of Chicago, Chicago, IL, United States, luyi.yang@chicagobooth.edu, Laurens G Debo, Varun Gupta To understand implications of policy initiatives to cut elective surgical wait times, we build an equilibrium search model where customers choose over a large collection of vertically differentiated, congested service providers. We find that policies that reduce either the search cost or customer demand may increase the average waiting time in the system as customers substitute toward high-quality service providers. Moreover, the improved quality customers obtain may not compensate the prolonged wait, degrading the overall search reward while yielding no returns in customer welfare. 2 - Observational Learning In Environment With Multiple Options This paper studies, both theoretically and empirically, the choice strategy (behavior) of agents in a system with multiple options for which agents observe the aggregate choices of previous agents. With information asymmetry regarding the quality of the different options, the choices of better informed agents turn sales into informative signals, allowing uninformed agents to learn about the options’ quality. Contrary to the traditional observational learning literature with binary choice, our theoretical analysis shows that options with less sales usually have higher chance of being high quality. But the experiment data reveals that uninformed subjects tend to choose options with most sales. Chen Jin, Northwestern University, chenjin2011@u.northwestern.edu

Chair: Atieh Madani, University at Buffalo, The State University of New York, 11221 Nickel Way, Amherst, NY, 14228, United States, atiehmad@buffalo.edu 1 - Transient Analysis Of Geometric Serial Lines With Perishable Intermediate Products Feng Ju, Assistant Professor, Arizona State University, 699 S. Mill Avenue, #553, Tempe, AZ, 85287, United States, fengju@asu.edu, Ningxuan Kang, Li Zheng Perishable intermediate products are commonly seen in practical manufacturing systems, where the residence time of intermediate products in the buffer is limited. Parts have to be scrapped if their maximum allowable time is exceeded. In order to reduce the scrap ratio and optimize the production operation in a timely manner, the transient behavior of the system needs to be investigated to evaluate and predict the system performance in real time. In this paper, we develop an analytical model to analyze the transient behaviors for such systems. Compared with simulation, it is shown that the proposed method could estimate the system’s transient performance with high accuracy. 2 - Integrated Production Planning And Distribution Problems Utku Koc, MEF University, Huzur Mah. Ayazaga Cad No:4, Maslak-Sariyer-Istanbul, Istanbul, 34396, Turkey, utku.koc@mef.edu.tr In this study, we consider multiple integrated production and distribution problems. Given a set of orders, a subset will be selected to be included in the production plan. Each order has a profit, size and deadline for distribution. Among the two types of vehicles available for distribution, the first type is unlimited in supply but costly. The availability of the second type of vehicle, which is less costly, varies by time. A number of problem classes are defined depending on the distribution characteristics of the system. The difficulty of each class is determined. Moreover, the value of integration is assessed depending on the distribution characteristics. 3 - Incorporation Of Breaks In A Staffing Model For A Service Center With Flexible Servers Atieh Madani, University at Buffalo, The State University of New York, 11221 Nickel Way, Amherst, NY, 14228, United States, atiehmad@buffalo.edu The shift scheduling problem (SSP) is a problem of assigning tasks to the resources for each shift with the aim of minimizing costs and fulfilling the demand. In our study we consider breaks, different levels of skill (for staff), and multiple tasks for resources in the shift scheduling problem. In our work we use the shift scheduling model that is developed by Batta, Berman, and Wang as the base model. A column generation heuristic is developed and used to solve this problem. The performance of this model and heuristic method is good. The average gap between the integer program result and the lower bound (for different size of problems) is 2.47%. 204-MCC Operational and Economic Models in Healthcare Sponsored: Manufacturing & Service Oper Mgmt, Healthcare Operations Sponsored Session Chair: Huiyin Ouyang, University of North Carolina, Hanes Hall CB# 3260, Chapel Hill, NC, 27599, United States, ouyang5@live.unc.edu Co-Chair: Serhan Ziya, University of North Carolina, 356 Hanes Hall CB# 3260, Chapel Hill, NC, 27599, United States, ziya@unc.edu 1 - Managing Returning Customers In An Appointment Based Service System Yichuan Ding, University of British Columbia, daniel.ding@sauder.ubc.ca We study how to manage returning customers in a slotted queue with the goal of maximizing service volume. Applications of this model include outpatient or dental appointment scheduling. We consider a simple strategy that a service provider may use to reduce balking — book a potential returning customer right before she leaves the clinic. We focus on a threshold-type policy and prove that the throughput rate is a quasi-concave function of the threshold under the retrials see time averages (RTA) assumption. We also analyze possible impact of the panel size and panel mix on the choice of this threshold. WA34

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