2016 INFORMS Annual Meeting Program

SB10

INFORMS Nashville – 2016

SB08 103A-MCC Health Care, Modeling II Contributed Session Chair: Shenghai Zhou, Shanghai Jiao Tong University, 1954 Hua Shan Road, Shanghai, 200030, China, zshsjtu2014@sjtu.edu.cn 1 - Planning And Scheduling Of Operating Rooms And Personnel Under Uncertainty Dominic Johannes Breuer, PhD Candidate, Northeastern University, 360 Huntington Ave, Boston, MA, 02115, United States, dbreuer@coe.neu.edu, Nadia Lahrichi, James C Benneyan De-centralized decision-making in complex operating room (OR) environments leads to sub-optimal resource allocations. In this study, we consider operating room planning and patient sequencing as well as clinician scheduling to minimize the number of open rooms, overtime, and patient wait while maximizing shift preferences, urgent case accommodation, and OR utilization. Uncertainties such as case duration, surgeon lateness, and staff availability by specialty are incorporated in realistic-sized scenarios through robust optimization. 2 - Second-order Conic Robust Optimization With Radiation Therapy Treatment Planning Of Breast Cancer Zengbo Zhang, Beijing Institute of Technology, 5 South Zhongguancun Street, Beijing, 10081, China, zhangzengbo_1999@163.com We incorporate robust optimization into CVaR to formulate a loss distribution under uncertainty. We demonstrate an application of our model to the radiation therapy treatment planning problem of breast cancer. In this therapy process, the dose distribution dependented on each state is uncertain. Our framework generalize and develop this type of uncertainty and that the uncertainty set is ellipsoidal, then the formulation can be re-written as second-order conic programs. Monte Carlo simulation example are presented to illustrate the proposed approach. Our results increased dosimetric performance for former treatment planning methods and improved cardiac sparing. 3 - Patient Assignment And Operation Room Scheduling Under Uncertainty Of Patient Cancellation And Operation Duration Bowen Pang, Tsinghua univ., Beijing, China, pzkaixin@foxmail.com, Xiaolei Xie, Li Luo, Yongjia Song Considering the multistage decisions faced by hospital practitioners under the uncertainties of operation duration and patient no-show in multiple operation rooms, we develop a Stochastic Integer Programming (SIP) model, in which all the objectives from different stakeholders are unified into costs. Bender’s Decomposition is applied to enhance the performance for solving the SIP. A case study of West China Hospital, SCU is presented. 4 - Using A Slotted Queuing Model To Predict Collaborative Emergency Center Operational Performance Peter Vanberkel, Dahousie University, PO Box 1000, Halifax, NS, B3H 4R2, Canada, peter.vanberkel@dal.ca, Alix Carter, Ben Wedge Nova Scotia has developed a novel way to manage Emergency Department (ED) patients in small communities. Staffed by a paramedic and a RN, and overseen by physician via telephone, Collaborative Emergency Centres (CECs) and able to manage the few patients who seek emergency care overnight in a cost effective manner. This work models the performance of CECs using a slotted queuing model in a number of different communities. Using the model, it is found that a CEC’s success is related to the proportion of demand for primary care appointments compared with the supply of primary care appointments. SB09 103B-MCC Renewable Energy Policies Sponsored: Energy, Natural Res & the Environment I Environment & Sustainability Sponsored Session Chair: Sandra D. Eksioglu, Clemson University, Clemson, SC, United States, seksiog@clemson.edu 1 - Biomass Supply Contract Pricing And Environmental Policy Analysis: An Agent-based Modeling Approach Shiyang Huang, Iowa State University, Ames, IA, United States, shuang@iastate.edu, Guiping Hu This paper proposes an agent-based simulation model to study the biomass supply contract pricing and policy making in biofuel industry. Farmers’ decision making is assumed to be profit driven and the biofuel producer’s pricing decision is represented with a linear equation with an objective to maximize profits. A case based on Iowa has been developed to analyze the interactions between

stakeholders. The impact of government environmental regulations on farmers’ decision making and biomass supply has also been analyzed, and managerial insights have been derived. 2 - On The Effectiveness Of Tax Incentives To Support Biomass Co-firing Hadi Karimi, Clemson University, hkarimi@clemson.edu, Sandra D. Eksioglu We present models which capture the efficiency of renewable energy policies (such as, the production tax credit (PTC)) on biomass co-firing in coal-fired power plants. The efficiency measure assumed here is the sum of utilities (profits) obtained when power plants adopt biomass co-firing. The utilitarian approach identifies a PTC which maximizes this summation. We use the utilitarian solution as a basis for comparison with other PTC schemes, such as, flat tax rate and capacity based rate. 3 - A Game Theoretic Model Of Biomass Co-firing Policies Sandra Eksioglu, Clemson University, seksiog@clemson.edu, Amin Khademi We propose a bilevel optimization model for the optimal design of a production tax credit that optimizes renewable electricity production via biomass co-firing in coal-fired power plants. The policy maker identifies a tax credit scheme which minimizes the total tax credit necessary to meet GHG emission reduction standards at power plants. Power plants decide on biomass utilization in order to maximize their profits. We propose a solution algorithm and evaluate its performance on a case study. 4 - Evaluation Of A Wind Farm Project Metin Cakanyildirim, The University of Texas at Dallas, metin@utdallas.edu We discuss the evaluation of profit, revenues and costs of a wind farm. The revenue requires both wind energy generated and the sales price per unit of this energy. Generated energy is based on the wind speed and so is random. The price can also be random. Appropriate random variables for wind speed are introduced and their moments are evaluated. Costs are more predictable but government tax incentives can drastically affect profitability. Optimal Surveillance and Control of Bio-Invasions Sponsored: Energy, Natural Res & the Environment I Environment & Sustainability Sponsored Session Chair: Esra Buyuktahtakin, Wichita State University, 1845 N Fairmount, Wichita, KS, Wichita, KS, 67260, United States, esra.b@wichita.edu 1 - Cooperative Management Of Invasive Species: A Dynamic Nash Bargaining Approach Robert G Haight, USDA Forest Service Northern Research Station, St. Paul, MN, 55108, United States, rhaight@fs.fed.us Kelly Cobourn, Gregory Amacher We use a Nash bargaining framework to examine scope for bargaining in invasive species problems where spread depends on the employment of costly controls. Municipalities bargain over a transfer payment that slows spread but requires an infested municipality to forgo nonmarket benefits from the host species. We find that when the uninfested municipality has a relative bargaining power advantage, bargaining may attain the first-best solution. However, in many cases a short- term bargaining agreement is unlikely to succeed, which suggests a role for higher levels of government to facilitate long-term agreements even when the details are left to municipalities to negotiate. 2 - Stochastic Programming Approaches To Surveillance And Control Planning For Emerald Ash Borer Infestations In Cities Eyyub Yunus Kibis, Wichita State University, eykibis@wichita.edu, Esra Buyuktahtakin, Robert Haight In this study, our objective is to maximize the net benefits of the ash trees on a landscape by applying surveillance to the ash population, followed by treatment or removal of trees based on the emerald ash borer (EAB) infestation level. Specifically, we propose a new multistage stochastic programming model which allows us to consider all possible scenarios for surveillance, treatment, and removal decisions over a planning horizon to control the invasion. Due to the model complexity, we use a decomposition technique to reach to optimal solutions for various initial scenarios. Results provide insights into surveillance and control policies, and provide an optimal strategy to reduce EAB infestation. SB10 103C-MCC

45