2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - Product Planning With Sensory Customer Requirements:
A Plain Yogurt Case
Leman Esra Dolgun, Anadolu University, Iki Eylÿl Kampusu,
Endustri Muhendisligi Bolumu, Eskisehir, Turkey,
Gulser Koksal, Elcin Kartal Koc
Customers use vague terms to describe their requirements and perceptions of
sensory products such as yogurt, which makes prioritizing improvements and
setting targets for them difficult. In this study, we propose an improved use of
Quality Function Deployment and Kansei Engineering to overcome such
difficulties, and demonstrate it in a plain yogurt case. Customer needs and
perceptions are assessed by special surveys based on semantic scales. Survey data
are analyzed using hypothesis tests and building logistic regression models.
Targets are suggested by optimizing these models.
4 - A Reformulation-based Interior-point Path-following Method For
Determining Proper Equilibria
Chuangyin Dang, Professor, City University of Hong Kong, Dept of
Systems Eng & Eng Mgmt, 83 Tat Chee Avenue, Kowloon, Hong
Kong, , Yin Chen
This paper extends an equivalent reformulation of Nash equilibrium to a
specifically defined perturbed game that deforms continuously with an extra
variable. As a result of this extension, we develop an interior-point path-following
method for determining a proper equilibrium. The method numerically follows a
smooth path that starts from any given point in the Euclidean space and ends at a
proper equilibrium. Numerical results further confirm the effectiveness of the
method.
TB15
104E-MCC
Industry Job Search Panel
Invited: INFORMS Career Center
Invited Session
Moderator: Adam McElhinney, Uptake Technologies, Chicago, IL,
United States,
1 - Industry Job Search Panel
Adam McElhinney, Uptake Technologies, Chicago, IL,
United States,
Ever wonder how to make the transition from student to OR, Analytics or Data
Science professional? Or maybe you are an experienced professional looking to
make a career switch. Join the Industry Job Search Panel to hear tips from the
pros. Specifically: 1. Strategies to ensure your skills are aligned with the job
market 2. Tips and tricks for landing and succeeding at the interview 3. How to
interview the interviewer
2 - Panelist
Aly Megahed, IBM Research - Almaden, 150 Palm Valley Blvd APT
2066, San Jose, CA, 95123, United States,
3 - Panelist
Beverly Wright, Cox Communications Inc, 125 Centennial Drive,
Peachtree City, GA, 30269, United States,
4 - Panelist
Warren Hearnes, Cardlytics, Lilburn, GA, 30047, United States,
TB16
105A-MCC
Information in Optimization
Sponsored: Optimization, Optimization Under Uncertainty
Sponsored Session
Chair: Eugene Perevalov, Lehigh University, 1, Bethlehem, PA, 18015,
United States,
1 - Outpatient Clinic Scheduling With Heterogeneous Patient
Preference And Resource Uncertainty
Deepak Agrawal, Pennsylvania State University, University Park,
PA, United States, , Guodong Pang,
Priyantha Devapriya, Soundar Kumara
Reasons of No-shows and how to stop them, has been focus of the research since
more than a decade. No-show adds up to the increases healthcare waste.
Therefore motivated by this we aim to develop advanced scheduling models
which can reduce No-shows by scheduling appointments at patients’ preferred
day and time with their preferred physicians. Researchers have acknowledged
that each patient may have different priority. Our model considers patient
segments with different priorities to design an efficient yet profitable scheduling
system.
2 - Mutual Information Minimization For Evaluating The Causal
Impact Of Home Care Services On Patient Discharge Disposition
Alexander Nikolaev, University at Buffalo, ,
Yuan Zhou, Sabrina Casucci, Lei Sun, Li Lin
Recent developments in observational causal inference employ subset selection
algorithms that balance the covariate distributions in the compared groups of
treated and untreated units under study. We present a subset selection approach
that works by minimizing the mutual information (MI) between the covariates
and the treatment variable. This becomes possible thanks to the derived
optimality conditions that tackle the non-linearity of a sample-based MI function.
The resulting algorithm runs in polynomial (close to linear) time, allowing for
treatment effect estimation with large datasets. We proceed to draw causal
insights from hospital readmission data of the UB HOMEBASE project.
3 - Information Acquisition Process: The Quantitative Aspect
Eugene Perevalov, Lehigh University,
The classical Information Theory was able to facilitate significant advances in
information transmission by finding a way of properly describing the quantity of
the form of information existence - the various symbols. On the other hand, to
make similar advances in the process of information acquisition, one would need
to properly understand and describe the quantities associated with the
information itself - its content. We take a step in that direction.
TB17
105B-MCC
Stochastic Sequential Assignment and Planning
Sponsored: Optimization, Optimization Under Uncertainty
Sponsored Session
Chair: Olga Raskina, Juno Therapeutics, Seattle, WA, United States,
1 - Stochastic Production Scheduling For Personalized Cancer
Therapy Manufacturing
Olga Raskina, Juno Therapeutics,
Jon Gunther
Juno Therapeutics is a clinical-stage company developing novel cellular
immunotherapies. To manufacture our therapeutic T cell product candidates, we
harvest blood cells from a cancer patient, separate or enrich for the appropriate T
cells, activate the cell, insert the gene sequence for the CAR or TCR construct into
the cell’s DNA, and grow these modified T cells to the desired dose level. The
modified T cells can then be infused into the patient or frozen and stored for later
infusion. We are investing substantially in a process that we believe is
commercially scalable for both CARs and TCRs.
2 - Dynamic Path Assignment For Robotic Bees Under
Collision Control
Felisa Vázquez-Abad, Hunter College,
We study the problem of dynamic routing of robotic bees towards the hive. Due
to uncertainty in position measurements, the stochastic problem cannot ensure
collision-free paths. We study the effects that the algorithm parameters have in
reducing the computational complexity and expected number of collisions. We
explore via experimentation a parallel algorithm for cloud computing as well as
crowd sourcing for estimation. In this manner we seek a faster completion time
reducing collisions and computational complexity.
3 - Multidimensional Birth And Death Models For Biochemical
Modeling With A View To Personalized Medicine
Alexey Nikolaev, CUNY, Hunter College and Graduate Center, New
York, NY, 11232, United States, ,
Felisa J Vázquez-Abad, Jon Gunther
Chemical reactions can be modeled either using ordinary differential equations,
or continuous time Markov chains. The ODE describes the dynamics of the
concentration of proteins. The multidimensional Birth and Death process
describes the dynamics of the number of particles of proteins. We study an
activator/repressor model that regulates circadian cycles in cells. We show that
the ODE is an exact expectation for the B&D process, under some regimes. For
other regimes we apply a novel technique of Taboo kernels. This methodology has
potential to predict malfunctions and to identify treatment.
TB17