2016 INFORMS Annual Meeting Program

MD34

INFORMS Nashville – 2016

3 - Facility Recovery Plan Under Resource Constraints Gang Li, Bentley University, Management Department, 175 Forest Street, Waltham, MA, 02452-4705, United States, gli@bentley.edu, Xiangtong Qi We address a facility recovery plan problem after disruption. The disruption has damaged some of the existing facilities while the resources used to repair these facilities are limited. This plan entails supply adjustment among customers and resource allocation among damaged facilities in order to minimize the total supply and shortage cost during the recovery period. In this talk, we present the problem formulation and discuss efficient algorithms to solve. 4 - Energy Conscious Robot Scheduling In Robotic Cell Manufacturing Vahid Eghbal Akhlaghi, Research Assistant, Middle East Technical University, Cankaya, METU, Yurt. 2, room 112-1, Ankara, 06800, Turkey, vahid.akhlaghi@metu.edu.tr, Hakan Gultekin, Sinan Gurel Robotic cells usually operate under time pressure to minimize time related objectives such as cycle time. Besides, robots consume significant amount of energy determined by the speeds and distances of their moves. We study the tradeoff between cycle time and energy consumption of a robot in a two machine flexible robotic cell. There are alternative cyclic schedules for such a cell and each cycle involves a number of different robot moves. Energy consumption of a robot is formulated as a convex function of its speed. Given a cycle time, we find optimal speeds for different robot moves in robotic cell cycles. We determine the best cyclic schedule and optimal robot speeds that minimize energy consumption. MD33 203B-MCC Simulation II Contributed Session Chair: Geonsik Yu, Yonsei University, Seoul, Korea, Republic of, geonsik.yu@gmail.com 1 - Siting A Geological Disposal Facility – A Discrete Event Simulation Approach. Matthew Gilbert, Mr, University of Warwick, Statistics, University of Warwick, Coventry, CV4 7ES, United Kingdom, m.g.gilbert@warwick.ac.uk, Simon French, Jim Q Smith Disposal of nuclear waste has become an increasing concern for the UK government over the past few decades. We present a discrete event simulation modelling changes in public opinion for their previous failed geological disposal siting attempt in Cumbria between 2009 and 2013. Using this we explore potential bias that could have been introduced at the end of the process. 2 - Estimating Cumulative Mean Behavior Using Standardized Time Series Dashi Singham, Naval Postgraduate School, 1411 Cunningham Road, Operations Research Department, Monterey, CA, 93943, United States, dsingham@nps.edu, Michael Atkinson We present an alternative to confidence intervals that evaluates the probability a sequentially updated sample mean stays within a given distance from the true mean of a process after a given initial sample size. This measure of reliability relies on properties of standardized time series, which we explore in relation to Brownian bridges. 3 - Managing The Delays Caused By Slow Groups In Golf MoonSoo Choi, Columbia University, 500 West 120th Street, S.W. We use simulation to study various models of slow groups and their significant impact on the pace of play in golf. We also compare different remedies to reduce the delays caused by slow groups. The golf course model is a series of eighteen queues in which multiple groups can play simultaneously with random stage playing times and precedence constraints. 4 - Simulation Metamodeling In The Presence Of Model Inadequacy Xiaowei Zhang, Hong Kong University of Science and Technology- HKUST, Room 5559B, Academic Building, HKUST, Clear Water Bay, Hong Kong, NA, China, xiaoweiz@ust.hk, Lu Zou A simulation model is often used as a proxy for the real system of interest in a decision-making process. However, no simulation model is totally representative of the reality. The impact of the model inadequacy on the prediction of system performance should be carefully assessed. We propose a new metamodeling approach to simultaneously characterize both the simulation model and its model inadequacy. Our approach utilizes both simulation outputs and real data to predict system performance, and accounts for four types of uncertainty that arise from unknown performance measure of the simulation model, simulation errors, unknown model inadequacy, and observation errors of the real system. Mudd Building, New York, NY, 10027, United States, moonsoo.choi@columbia.edu, Qi Fu, Ward Whitt

5 - When The Diversified Organization Prevails: A Simulation Approach Geonsik Yu, Yonsei University, Seoul, Korea, Republic of, geonsik.yu@gmail.com This research investigates through simulation the mechanism under which diversity improves the performance of a company. In experiments, we focus on the settings where the organization consisting of dissimilar members and try to explain the reported reality-instances where diversity is ineffective. Analyzing the characteristics of those settings, we identify that under what conditions the organizational diversity is more or less effective. The simulation model newly formulated in this study extends the existing methods that use discrete vectors and genetic algorithm and is able to include market needs and product paradigm shifts. 204-MCC Joint Session HAS/MSOM: Healthcare Operations Sponsored: Manufacturing & Service Oper Mgmt, Healthcare Operations Sponsored Session Chair: Mahboubeh Madadi, Louisiana Tech University, 123, Ruston, LA, 71272, United States, madadi@latech.edu 1 - Team Primary Care Practice Scheduling Problem Ekin Koker, University of Massachusetts-Amherst, Amherst, MA, United States, ekoker@umass.edu, Hyun Jung Oh, Hari Balasubramanian, Ana Muriel We consider the team primary care practice scheduling problem where each patient is seen by one of many available nurses before seeing her provider. In other words, the nurse step is flexible while the provider step is dedicated. Both steps have uncertain durations and these durations further depend on the type of patient — some patients require a longer service duration than others. The patients can also crossover in schedule, so the order of patients seen by the nurse might not be the same as the order in which the provider sees patients. We develop a two-stage stochastic integer programming model to solve this challenging scheduling problem and present computational results. 2 - Optimal Decision Making In The Processing Of Human Breast Milk Ruichen Sun, University of Pittsburgh, Pittsburgh, PA, United States, rus19@pitt.edu, Lisa M Maillart, Andrew J Schaefer Donated breast milk - collected, processed and dispensed via milk banks - is the standard of care for premature and unhealthy infants whose mothers cannot provide adequate supply. We formulate a multi-criteria mixed-integer program to optimize the daily decisions involved in the pooling of milk from different donors to meet macronutrient requirements across different product types, and the batching of pooled milk for efficient pasteurization. Our numerical results demonstrate significant improvements compared to historical decisions at our partner milk bank. 3 - A Stochastic Programming Approach For Surgery Scheduling Under Limited Availability Of Turnover Teams Serhat Gul, TED University, serhat.gul@tedu.edu.tr The number and availability of turnover teams may significantly affect the performance of a surgery schedule. We propose a two-stage stochastic integer programming formulation for setting the patient appointment times under limited availability of turnover teams. We consider the duration of surgical operation and turnover to be random variables. The objective is to minimize the competing criteria of patient waiting time and operating room idle time. We present a heuristic to solve the problem. We conduct numerical experiments using data from a large hospital. MD34

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