2016 INFORMS Annual Meeting Program

300 / 561

INFORMS Nashville – 2016
300
Attraction Recommendation For Tour Operators Under
Stochastic Demand
Chao Huang, Southeast University, Nanjing, Jiangsu Province,
China, , Weihao Hu
The main stakeholders of attraction recommendations include tourists who refer
to the recommendation to make traveling decisions and the tour operator who
operates the recommendation. Existing attraction recommendation methods
focus on recommending most relevant attractions to the tourists, yet overlook the
benefit attraction recommendation could bring to the tour operator under
stochastic demand. We provide a focused study on cost-based attraction under
stochastic demand from the perspective of tour operators. We analyze the costs of
attraction recommendation of tour operators and the impact of stochastic tourist
demand, and formulate the cost-based attraction recommendation problem. We
then propose a two-stage stochastic optimization model that involves joint chance
constraint to optimize the attraction recommendation solution that focuses solely
on tourist preferences, and solve the optimization model with Sample Average
Approximation method. To verify the effectiveness of the proposed cost-based
attraction recommendation method, comprehensive experimental studies are
conducted with simulated instances as well as real-world case.
Dynamic Pricing Of A Bottleneck With Heterogeneous User
Preferences And Value Of Time Using Tradable Credits
Mahyar Amirgholy, Postdoctoral Research Associate, Cornell
University, 220 Hollister dr, Ithaca, NY, 14850, United States,
, H. Oliver Gao, Eric J. Gonzales
We propose an optimal credit-based pricing scheme for a single bottleneck with
time-dependent demand and heterogeneous user preferences. Implementing such
a strategy using a revenue neutral credit-based pricing scheme allows the value of
the credits to be determined by the interaction between the users in the
equilibrium condition of the market. The proposed strategy allocates the money
raised from the “credit buyers” to subsidize the commutes of the “credit sellers” in
order to incentivize the commuters to form a uniform distribution of arrival times
over the peak period. As a result, designing a revenue neutral pricing strategy can
raise public support by improving the social welfare of the users.
Optimal Design Of The Large-scale Transit Systems In Urban
Regions Using The Macroscopic Fundamental Diagram
Lan Liu, Cornell University, Ithaca, NY, 14853, United States,
, Mahyar Amirgholy, Mehrdad Shahabi,
H. Oliver Gao
In this research, we propose a continuum approximation model for optimizing
the network structure (line spacing and stop spacing) and the operating
characteristics (headway and fare) of the transit system by minimizing a linear
combination of (1) the generalized cost that users experience in their trips, (2) the
operating cost of the transit system for the agency, and (3) the external cost of the
emission in region. The optimal design of the transit system can be derived by
minimizing the total cost of the transportation system in three different network
allocation scenarios: (i) mixed network (Bus), (ii) dedicated lanes (Bus Rapid
Transit), and (iii) parallel networks (Metro).
Tuesday Poster Competition
Exhibit Hall
Tuesday Poster Competition
Competition Poster Session
Dynamic Pricing And Demand Side Management In
Smart Communities
Vignesh Subramanian, University of South Florida, 5006,
Bordeaux Village pl, Apt 201, Tampa, FL, 33617, United States,
Tapas K. Das
Dynamic pricing will actively engage the electricity consumers, having an
advanced metering infrastructure (AMI), in centralized demand side management
(CDSM), a key to price stability and network reliability. We propose a quadratic
binary programming model for a centralized controller to schedule the consumer
load. The numerical result demonstrates how CDSM can lower the price peaks,
reduce the reserve capacity of the generator and minimize the consumer’s hourly
tariff.
Multi-stage Stochastic Optimization For Considering Investment
Risk In Conflict Prone Countries – A Case Study Of South Sudan
Neha Satish Patankar, PhD Student, NC State University,
2366 Champion court, Raleigh, NC, 27606, United States,
Open source framework for energy system modeling - referred to as Tools for
Energy Model Optimization and Analysis, Temoa - is employed to explore possible
energy planning strategies for South Sudan. Stochastic optimization is utilized to
explicitly consider the risk of conflict and the resultant damage to generators and
transmission lines within the system. Because data related to both conflict
probabilities and damage are subjected to deep uncertainty, we rely on sensitivity
analysis to generate key insights. Results show that while large, centralized plants
benefit from economies of scale, distributed solar photovoltaic are more resilient
to conflict.
Two-stage Methodology For Multiobjective Robust Decision
Making With Application In Water-energy Planning
Daniel Jornada, Texas A&M University, 2734 San Felipe Dr,
College Station, TX, 77845, United States, ,
V.Jorge Leon
The large number of compromise solutions to choose from a multiobjective
program poses significant challenge for decision making. We formalize a two-
stage optimization methodology to narrow the alternatives under consideration
by introducing secondary robustness criteria to hedge against implementation
uncertainties. A water-energy planning problem illustrates the significance of the
methodology.
Modeling And Maximizing Power For Wind Turbine Arrays
Lucas Buccafusca, University of Illinois Urbana-Champaign,
Urbana-Champaign, IL, United States,
This talk considers a specific application domain, that of wind turbine arrays, and
explores the use of partitioning and control design to optimize energy extraction.
Large wind turbine arrays, or wind farms, can be viewed as coupled networks,
which present many problems when applying traditional optimization techniques.
In our work, we apply heuristic techniques, exploiting the inherent symmetry
found in wind turbine arrays, to obtain simplified models for large arrays. Using
these simplified models we first consider a dynamic programming-like approach
to maximize power extraction under the condition of uniform wind.
Using A Private Marketplace To Build A Hybrid Workforce
For IT Service Delivery
Monica Johar, Associate Professor, University of North Carolina,
9201 University City Blvd, Friday 352 C, Charlotte, NC, 28223,
United States, Su Dong, Ram Kumar
The emergence of on-demand service marketplaces is a relatively new
phenomenon. Technology is facilitating innovative work arrangements using an
on-demand workforce. As the range of services available on such marketplaces
increases, organizations could explore innovative uses of on-demand workers.
Organizations can explore work arrangements that benefit from using a hybrid
workforce that consists of full-time and on-demand workers. This paper addresses
this interesting new work paradigm by presenting a mathematical programming
model of service delivery that leverages in-house workers and on-demand
marketplaces for service delivery.
A New Class Of Measures For Independence Test With Its
Application In Big Data
Qingcong Yuan, PhD Candidate, University of Kentucky, 305 MDS
Building, 725 Rose Street, Lexington, KY 40506, Lexington, KY,
40506, United States, Xiangrong Yin
We introduce a new class of measures for testing independence between two
random vectors, using characteristic functions. By choosing a particular weight
function in the class, we study a new index for measuring independence and its
property. Sample versions and their asymptotic properties using different
estimations are developed. We demonstrate the advantage of our methods via
simulations and real data. In particular, we illustrate the effective use of our
methods in big data analysis.
A Time-series System To Predict Glucose Concentrations Based
On Continuous Glucose Monitoring
Lei LI, Beihang University, Beijing, 100191, China,
, Yimeng Shi, Jun Yang, Xiaolei Xie
The estimated prevalence of diabetes in Chinese adults in 2013 was 11.6%, which
for the first time surpassed the U.S. In recent years, Continuous Glucose
Monitoring (CGM) systems are developed to record the patient’s daily blood
glucose level. Such systems provide us the real- time glucose level. Recently,
researchers implemented AR or ARMA models on a small pool of CGM data to
predict future glucose level. In this study, we developed a method by using
adaptive Autoregressive Integrated Moving Average (ARIMA) model on a larger
data- set rather than models with fixed-order, which is more practical and
accurate as the order of the whole data is unknown before.
Optimizing Screening Policies Inside A Food Production Facility
Nicole T Lane, PhD Candidate, North Carolina A&T State
University, 3511 Carrington Street, Greensboro, NC, 27407,
United States, Lauren Berrings Davis
New legislation requires food production facilities to have a food safety plan
including mitigation strategies to increase security. This research identifies an
optimal set of implementable strategies. The two-stage stochastic model presented
incorporates the need for production minimums and food safety constraints. The
results of a numerical study show that for relatively low costs, the
implementation of these policies ensures that products leaving the facility are safe
for consumption.
POSTER SESSION