2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
45
SB08
103A-MCC
Health Care, Modeling II
Contributed Session
Chair: Shenghai Zhou, Shanghai Jiao Tong University, 1954 Hua Shan
Road, Shanghai, 200030, China,
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, 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,
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,
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, , 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,
1 - Biomass Supply Contract Pricing And Environmental Policy
Analysis: An Agent-based Modeling Approach
Shiyang Huang, Iowa State University, Ames, IA, United States,
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,
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, ,
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,
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.
SB10
103C-MCC
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,
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,
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, ,
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