2016 INFORMS Annual Meeting Program

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INFORMS Nashville – 2016
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3 - The Impact Of Manufactures Comptition To Pay Incentive Funds
On Supply Chain
Neda Khanjari, Assistant Professor, Rutgers University,
227 Penn St, BSB 260, Camden, PA, 08102, United States,
In many industries manufacturers pay incentive funds to retailer hired sales
agents to boost the demand for the manufacturers’ products. In this paper, we
study a supply chain in which two manufactures are competing to get the
attention of the retailer hired sales agent to boost their own product.
4 - Partial Outsourcing And Linked Learning Processes
Burcu Tan, Tulane University, A. B. Freeman School Of Business,
7 Mcalister Dr., New Orleans, LA, 70118, United States,
, Edward Anderson, Geoffrey Parker,
Xiaoyue Jiang
Firms are increasingly outsourcing software and technology development as well
as other knowledge work. Standard economic models predict that firms should
outsource either all or none of a particular activity; however, recent evidence
contradicts that. We develop a dynamic optimization model to provide a rational
explanation for partial outsourcing. Our explanation hinges upon the linked
learning processes at the subsystem development level and the systems
integration level.
5 - A Hybrid Stochastic Fuzzy Approach For Inventory Control In
Multi Item Job Shop Processes With Unsteady Lead Time
Alcides Santander, Universidad del Norte, Barranquilla, Colombia,
Kevin Melendez, Nathalia Hernandez,
Diego Guillen, Marco E. Sanjuan
Uncertainties inherent in customer demands and variable lead times make
difficult for supply chains to achieve either just-in-time inventory replenishment
or economic optimal process. This results in a low service level among the
different echelons of the supply chain. A hybrid stochastic-fuzzy approach for
inventory control is proposed in order to guarantee production rate despite
fluctuations in demand and process lead time to minimize the work in process.
Demand forecast and probabilistic depiction of lead times, along with the process
variables, are the inputs of a fuzzy inference system designed to determine
whether it is profitable to generate a purchase order in a given period of time.
MD79
Legends G- Omni
Health Care, Modeling VIII
Contributed Session
1 - Multi-period Appointment Scheduling In Outpatient
Procedure Centers
Utku Tarik Bilgic, Middle East Technical University, Department of
Industrial Engineering, Middle East Technical University, Ankara,
06800, Turkey, , Sakine Batun
Single-period appointment scheduling for a given sequence of outpatient
procedures is a well-studied problem. We consider the problem determining the
sequence of and the appointment times for a given set of surgeries to be operated
over a multi-period planning horizon in the presence of patient no-shows and
uncertainty in surgery durations. We formulate and solve the problem as a two-
stage stochastic program to estimate the value of capturing uncertainty.
2 - Robust Design Of A Stroke Hospital Network
Amir Ardestani Jaafari, McGill University,
1001 Rue Sherbrooke West, Montreal, QC, H3A 1G5, Canada,
, Beste Kucukyazici
With advances in the diagnosis and treatment of acute stroke, timely medical
intention is increasingly critical; however, simply transporting the patients to the
closest stroke hospital may cause congestions in some hospitals, while the stroke
beds are underutilized in other hospitals. In this research, we study the trade-off
between minimizing transportation time to the hospital and minimizing the
congestion experiences by the patient at the hospital using robust optimization.
3 - Method To Assign Specialties To Timetable Slots In Surgery Units
To Smooth Postoperative Inward Bed Demand
Flavio S Fogliatto, Prof., Federal Univ of Rio Grande do Sul, Av.
Osvaldo Aranha, 99/5o andar, Porto Alegre, RS, 90040020, Brazil,
Marcos Gerchman, Jeruza Neyeloff,
Michel J Anzanello, Beatriz D Schaan
Peaks in patients’ demand for inward hospitalization usually lead to disruptions in
the provision of healthcare, having negative effects on patient and staff
satisfaction. A main source of inward bed demand is the surgical theater. This
paper proposes a method to determine the best assignment of specialties to
timetable slots in surgical theaters, such that the variance of inward bed demand
is minimized. For that, we use integer programming heuristics. Our propositions
are tested in the surgical unit of a large public University hospital located in the
south of Brazil. We were able to reduce the inward bed demand variability by
90%, smoothing the flow of post-surgical patients to hospital wards.
MD94
5th Avenue Lobby-MCC
Technology Tutorial: Gurobi/AMPL
Technology Tutorial
1 - Gurobi Optimization
Dr. Greg Glockner, Vice President of Engineering, Bellevue, WA,
Advanced Python Modeling with Gurobi Are you looking for an environment
that combines the expressiveness of a modeling language with the power and
flexibility of a programming language? The Gurobi Python interface allows you to
build concise and efficient optimization a model using high-level modeling
constructs. This tutorial will provide an overview of these capabilities, including
an introduction to new modeling features that significantly enhance the
expressiveness of our environment.
2 - AMPL In The Cloud: Using Online Services To Develop And
Deploy Optimization Applications Through Algebraic Modeling
Robert Fourer, AMPL Optimization Inc., 2521 Asbury Ave,
Evanston, IL, 60201, United States,
Optimization modeling systems first appeared online almost 20 years ago, not
long after web browsers came into widespread use. This presentation describes
the evolution of optimization alternatives in what has come to be known as cloud
computing, with emphasis on the role of the AMPL modeling language in making
models easy to develop and deploy. We start with the pioneering free NEOS
Server, and then compare more recent commercial offerings such as Gurobi
Instant Cloud; the benefits of these solver services are readily leveraged through
their use with the AMPL modeling tools. We conclude by introducing QuanDec,
which creates web-based collaborative applications from an AMPL models. Robert
Fourer, an authority on the design and implementation of computer software to
support large-scale optimization, studied at M.I.T. and Stanford and was a
professor of Industrial Engineering and Management Sciences for over 30 years.
He is a founder and is currently President of AMPL Optimization Inc. and is co-
author of a popular book on modeling in the AMPL language.
Tuesday, 8:00AM - 9:30AM
TA01
101A-MCC
Machine Learning II
Sponsored: Data Mining
Sponsored Session
Chair: Hamidreza Ahady Dolatsara, MD, United States,
1 - Expectation Maximization For Finite Mixture Of Linear Regression
Models With Group Structure Data
Haidar Almohri, PhD Candidate, Wayne State University,
2833 Catalpa Circle, Ann Arbor, MI, 48108, United States,
, Ratna Babu Chinnam, Arash Ali Amini
One of the limitations with the available methods for fitting Finite Mixture of
Linear Regression (FMR) models is that they do not explicitly account for group
structure within the datasets during modeling. It is sometimes desirable to force
the algorithm to allocate groups/blocks of observations to individual models,
instead of individual observations. We propose an Expectation Maximization
(EM) approach to fit FMR to data with group structure.
2 - A Comparison Method For Association Rule Mining Algorithms
Gulser Koksal, Prof. Dr., Middle East Technical University,
Industrial Engineering Department, Ankara, 06800, Turkey,
Sanam Azadiamin
Association rule mining algorithms are helpful in extracting useful information
from large amount of data. In literature, these algorithms are compared using
data sets for which interesting association rules are unknown. A novel
comparison method is developed to perform the comparison using designed data
sets containing interesting rules generated by logistic regression. Several
comparison measures are defined. Statistical analyses and multi-criteria decision
making approaches are applied to find the best algorithm considering the selected
measures.
MD79