Informs 2015

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INFORMS Philadelphia – 2015
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4 - Assessing the Impact of Product and Service Quality on
Consumer Returns: A Data Analytics Study
Necati Ertekin,Texas A&M University, Mays Business School,
College Station TX 77840, United States of America,
, Gregory Heim, Michale Ketzenberg
We contribute to the understanding of consumer return behavior by examining
the association between in-store customer experience during a purchase and a
subsequent return. We demonstrate that retail efforts such as increasing salesper-
son competence and improving store environment that are so long believed to
prevent returns may indeed induce returns.
TA76
76-Room 204C, CC
Advances in Simulation-based Optimization I
Sponsor: Simulation
Sponsored Session
Chair: Jie Xu, George Mason University, 4400 University Dr., MS 4A6,
Engr Bldg, Rm 2100, Fairfax, VA, 22030, United States of America,
1 - Estimating the Probability of Convexity of a Function Observed
with Noise
Nanjing Jian, PhD Student, Operations Research and Information
Engineering, 288 Rhodes Hall, Cornell University, Ithaca, NY,
14850, United States of America,
Shane Henderson
Given estimates of the values of a function observed with noise from simulation
on a finite set of points, we wish to sequentially estimate the probability that the
function is convex. By updating a Bayesian posterior on the function values, we
iteratively estimate the posterior probability of convexity by solving certain linear
programs in a Monte Carlo simulation. We discuss a variety of variance reduction
methods for the estimation and the linear programs associated with each.
2 - Adaptive Sampling Trust Region Optimization
Sara Shashaani, Associate Professor, Department of Statistics,
Purdue University, 250 N University Street, West Lafayette, IN,
47907, United States of America, ,
Raghu Pasupathy
We develop derivative free algorithms for optimization contexts where the
objective function is observable only through a stochastic simulation. The
algorithms we develop follow the trust-region framework where a local model is
constructed, used, and updated as the iterates evolve through the search space.
We incorporate adaptive sampling to keep the variance and the squared bias of
the local model in lock step, in a bid to ensure optimal convergence rates.
3 - Parallel Empirical Stochastic Branch & Bound
Sajjad Taghiye, George Mason University, 4400 University Dr.,
MS 4A6, Engr Bldg, Rm 2100, Fairfax, VA, 22030,
United States of America, , Jie Xu
To efficiently solve problems with time-consuming high-fidelity simulations, we
develop a new parallel algorithm known as parallel empirical stochastic branch &
bound (PESBB) to exploit the power of high performance computing. We will
discuss synchronous and asynchronous versions of PESBB and present initial
numerical results to demonstrate the scalability of PESBB.
4 - Finding the Best using Multivariate Brownian Motion
Seong-hee Kim, Professor, Georgia Institute of Technology,
755 Ferst Dr NW, Atlanta, GA, 30332, United States of America,
Ton Dieker, Seunghan Lee
We present a new fully sequential procedure based on multivariate Brownian
motion when variances are known but unequal. The procedure uses an ellipsoid
as a continuation region, and a system with the worst sample mean is eliminated
whenever the procedure’s statistic exits the ellipsoid. The size of the ellipsoid
changes as the number of survivors decreases. Experimental results are provided
for both equal and unequal variances.
TA77
77-Room 300, CC
Green Supply Chain Management
Contributed Session
Chair: Vinay Gonela, Assistant Professor Of Management, Southwest
Minnesota State University, CH 214, 1501 State Street, Marshall, MN,
56258, United States of America,
1 - The Impact of Contracts on Environmental Innovation in a
Supply Chain
Seyoun Jung, PhD Student, KAIST (Korea Advanced Institute of
Science and Technology), 85 Hoegiro, Dongdaemun-gu, Seoul,
Korea, Republic of, , Bosung Kim,
Kun Soo Park
We examine the impact of contracts between a supplier and a manufacturer on
the supplier’s environmental innovation. We calculate and compare the
equilibrium outcomes under three types of contract such as wholesale-price,
revenue-sharing, and quality-dependent contracts.
2 - Producer-dominated Green Supply Chain Collaboration under
Trade-in Programs
Chih-Tien Chiu, Doctoral Student, National Taiwan University,
No.1,Sec. 4, Roosevelt Rd., Taipei, 10617, Taiwan - ROC,
Mu-chen Chen, Jiuh-biing Sheu
This paper aims to address new-product/used-product pricing in a green logistics.
We adopt the dynamic programming approach integrated with the logit model to
formulate the n-period trade-in pricing-logistics problem, where the logit model is
utilized for trade-in service channels choice. Data collected via stated preference
experiments are used for the parameter estimation of the logit model, followed by
conducting quantitative analyses to provide important findings and managerial
insights.
3 - Metrics for Sustainable Operations: Current State and
Path to Improvement
Remi Charpin, Clemson University, 100 Sirrine Hall, Clemson,
United States of America, Aleda Roth
From an operations and supply chain management lens, we examine
sustainability metrics currently being reported by firms. We propose that certain
metrics are ‘attractors,’ as they are apt to lead the business towards sustainability,
whereas others are deemed to be ‘detractors’ that are likely to be used for
‘greenwashing.’
4 - Stochastic Optimization of Sustainable Industrial Symbiosis
Based Hybrid Generation Bioethanol Supply Chain
Vinay Gonela, Assistant Professor Of Management, Southwest
Minnesota State University, CH 214, 1501 State Street, Marshall,
MN, 56258, United States of America, ,
Atif Osmani, Jun Zhang, Joseph Szmerekovsky
This paper focuses on designing a new industrial symbiosis based hybrid
generation bioethanol supply chain (ISHGBSC). A SMILP model is proposed to
design the optimal ISHGBSC under different sustainability standards. The result
provides guidelines for policy makers to determine the appropriate standard to
use under different sustainable concerns. In addition, it provides investors a
guideline to invest in different technologies under different sustainability
standards.
TA78
78-Room 301, CC
Big Data and Energy
Contributed Session
Chair: Feng Gao, SGRI North America, 5451 Great America Parkway,
Santa Clara, CA, 95054, United States of America,
1 - Resilient Power System State Estimation using
Compressive Sensing
Hanif Livani, Assistant Professor, University of Nevada Reno,
Electrical & Computer Engineering, MC 0111, 1185 Perry St, /
Room 302, Reno, NV, 89557, United States of America,
Phasor Measurement Units (PMU) have become widely used for power system
monitoring and control. However, they are not installed on all the buses in a
network. Therefore, PMU-only state estimation encounters problems arising from
a limited number of installed PMUs and probable data losses as the results of
congestion or disconnection in this study, we propose power
system state estimation using Compressive Sensing (CS) algorithm which is
resilient to loss of data.
TA78