Informs 2015

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INFORMS Philadelphia – 2015
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3 - Modeling Location Diffusion, Resource Allocation and Rebalance
in Car Sharing Industry-a Zipcar Exam
Wei Chen, Assistant Professor, York College of Pennsylvania,
441 Country Club Rd, York, PA, 17403, United States of America,
In this study, we build two novel models to tackle three car-sharing management
questions: 1). Car Station Location Selection; 2), Car Station Size problem; and
3).Car Relocation Numbers. Our models require less inputs and offer a quick
analytic result to answer three operational issues. Particularly, we use Zipcar as an
example to illustrate how our models work, it turns out that our models can
perform well and achieve expectations.
4 - Routing Decision Strategy and Resource Allocation Planning
for a Two-Echelon Rescue Delivery System
Yi Liao, Southwestern University of Finance and Economics,
Liutai Ave 555, School of Business Administration, Chengdu,
China, , Hanpeng Zhang
One of the most important post-catastrophe goals is the effective and efficient
allocation of rescue resources. We consider the combined problem of allocating
rescue resources between two main warehouses and planning their delivery
strategies to several local distribution centers. We suggest three routing strategies-
simple, mixed and dynamic mixedóand analyze the effects that different routing
strategies have on rescue resource allocation and relief performance.
WB72
72-Room 203A, CC
Omni-channel Commerce and Analytics
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Dahai Xing, Research Staff Member, IBM Research,
1101 Route 134 Kitchawan Rd, Yorktown Heights, NY, 10598,
United States of America,
1 - Demand Modeling in the Presence of Unobserved Lost Sales
Shivaram Subramanian, IBM Research, 1101 Kitchawan Road,
Yorktown Heights, NY, 10598, United States of America,
Pavithra Harsha
We present an integrated optimization approach to parameter estimation and
missing data imputation for calibrating discrete choice demand models where one
or more choice alternatives are censored. We jointly determine the prediction
parameters associated with the customer arrival rate, as well as their preferences
in an assortment. We share experimental results for instances arising in a variety
of industrial settings. The results achieved indicate the efficacy of the proposed
methods.
2 - Big Data Solutions for Omni-channel Fulfillment Planning
and Optimization
Ajay Deshpande, Research Staff Member, IBM Research, 1101
Route 134 Kitchawan Rd, Yorktown Heights, NY, 10598, United
States of America, , Yingjie Li, Dahai Xing,
Brian Quanz, Arun Hampapur, Ali Koc, Xuan Liu
Retailers look to leverage their store networks to fulfill omni-channel demand.
Our research efforts focus on developing two Big Data solutions. The Network
Planner provides rapid what-if analysis to find the most effective fulfillment plan.
The Optimizer dynamically optimizes sourcing of online orders while balancing
conflicting business goals.
3 - How Can Manufacturers Help Brick-and-mortar Stores Fight
with “Showrooming”?
Dahai Xing, Research Staff Member, IBM Research,
1101 Route 134 Kitchawan Rd, Yorktown Heights, NY, 10598,
United States of America, , Tieming Liu
We study a supply chain in which an online retailer free-rides a brick-and-mortar
retailer’s sales effort. The free riding effect reduces brick-and-mortar retailer’s
desired effort level, thus hurts the overall supply chain performance. We examine
the selective rebate contract with price match in two scenarios: the online
channel is owned by or independent of the manufacturer. We show that the
contract can allocate the supply chain system profits arbitrarily between the
players.
WB73
73-Room 203B, CC
Bayesian Data Analytics for Quality and
Reliability Assurance
Sponsor: Quality, Statistics and Reliability
Sponsored Session
Chair: Mingyang Li, Assistant Professor, University of South Florida,
4202 East Fowler Avenue, Tampa, United States of America,
1 - Density Estimation with Indirect Data
Park Chiwoo, Assistant Professor, FAMU-FSU College of
Engineering, 2525 Pottsdamer Street, Tallahassee, FL, 32310,
United States of America, , Xin Li
We present a general problem of estimating a probability density of a random
variable when there are only observations of other random variables that are
correlated to the random of interest. The application of the approach to estimating
particle size distribution with dynamic light scattering and/or small X-ray
scattering data will be also presented.
2 - Bayesian Hazard Modeling of Heterogeneous Lifetime Data with
an Unknown Number of Sub-populations
Mingyang Li, Assistant Professor, University of South Florida,
4202 East Fowler Avenue, Tampa, FL, United States of America,
Jian Liu
Lifetime data collected from reliability tests or field operations often exhibit
heterogeneity. To quantify such heterogeneous data with an unknown number of
sub-populations, a Bayesian hazard modeling approach is proposed. It features in
jointly estimating model parameters and determining the number of sub-
populations. Effective and efficient sampling schemes are further developed to
comprehensively address the model estimation difficulty when non-conjugate
priors involve.
3 - A Semi-markov Random Field Gaussian Process Models for
Forecasting in Complex Systems
Zimo Wang, Texas A&M University, 3131 TAMU,
College Station, TX, United States of America,
We introduce a Semi-Markov random field approach to extend Gaussian process
models for multi-step forecasting in complex systems with transient behaviors.
Experimental studies indicate that forecasting approach can predict the onsets of
epilepsy episodes 1 min earlier compared to other contemporary methods tested.
4 - Real-time Monitoring for Advanced Manufacturing Processes
using a Novel Greedy Bayesian Method
Kaveh Bastani, Research Assistant, Virginia Tech University,
106 Durham Hall (MC 0118) 1145 Perry Str, Blacksburg, VA,
United States of America, , Zhenyu Kong
The objective of this work is to realize real-time monitoring of process conditions
in advanced manufacturing processes. To achieve this objective we propose an
approach invoking the concept of sparse representation for multiple sensor
signals, and subsequently, develop a novel greedy Bayesian method (GBM) to
approximate the sparse solution. We validate the effectiveness of the proposed
approach in real-time monitoring of a fused filament fabrication additive
manufacturing process.
WB74
74-Room 204A, CC
Reliability I
Contributed Session
Chair: Shufeng LI, University of Houston, Room NT0403, 4401
Wheeler, Houston, TX, 77004, United States of America,
1 - Maintenance Policies for a Deteriorating System Subject to
Non-self-announcing Failures
Onur Bakir, Associate Professor, Istanbul kemerburgaz University,
Mahmutbey Dilmenler Caddesi, No:26, Bagcilar, Istanbul, 34217,
Turkey,
We evaluate various maintenance policies for systems subject to continuous time
Markovian deterioration which may result in non-selfannouncing failures. The
decision maker inspects the system periodically at the decision epochs, identifies
the current state; and chooses an available action. The objective is to minimize
the expected long-run cost rate. We provide a numerical example to analyze the
effect of various cost parameters on the optimum inspection period and policy.
WB72