Technical Sessions – Monday July 11

3 - Mean Shift Outlier Model with MARS and Continuous
Optimizaton
Fatma Yerlikaya Ozkurt, Scientific Computing, Institute of
Applied Mathematics, Industrial Engineering Department,
Middle East Technical University, 06531, Ankara, Turkey,
fatmayerlikaya@gmail.com, Pakize Taylan, Gerhard-Wilhelm
Weber
The outlier detection problems is very important problem in statistics. Because,
outliers observations affects estimation and inference as negatively. There are
several outliers detection methods. One of these methods is given by Mean
Shift Outlier model. We consider Mean Shift Outlier model and construct
a new problem for this model. Then, we approach solving this problem using
continuous optimization techniques and Multivariate Adapted Regression
Spline, becoming an important complementary technology and alternative to
the outliers detection methods.
4 - Prognostic Model Based on the Relationships between
Waste Composition and Structural Size, ArcelorMittal
Steel Plant of Poland SA, in Krakow Case Study
Boguslaw Bieda, Management Department, AGH-University of
Science and Technology, ul. Gramatyka 10, 30-067, Krakow,
Poland, bbieda@wp.pl
The objective of the paper is to develop the industrial waste prognostic model
based on the database time-series waste quantities and compositions over the
period of up to 20 years (data from 1980 to 2006). In this paper the slag and ash
are used for statistical analysis to examine relationships between waste stream
composition and structural size of the steel plant. The framework for the study
is based on the ArcelorMittal Steel Plant Poland SA in Krakow. Data analysis
has been conducted using Statistica software.
� HD-15
Thursday, 15:30-17:00
Meeting Room 208
Linguistic Uncertainty in the Design of
Decision Aid Systems
Stream: Fuzzy Logic
Invited session
Chair: Javier Montero, Faculty of Mathematics, Complutense
University, Plaza de las Ciencias, 3, E-28040, Madrid, Spain,
javier_montero@mat.ucm.es
Chair: Jie Lu, School of Software, University of Technology, Sydney,
PO box 123, Broadway, 2007, Sydney, NSW, Australia,
jie.lu@uts.edu.au
Chair: Begoña Vitoriano, Estadística e Investigación Operativa I, Fac.
Matemáticas, Universidad Complutense de Madrid, Plaza de
Ciencias, 3, Ciudad Universitaria, 28040, Madrid, Spain,
bvitoriano@mat.ucm.es
1 - A Disaster-severity Assessment DSS Comparative
Analysis
Begoña Vitoriano, Estadística e Investigación Operativa I, Fac.
Matemáticas, Universidad Complutense de Madrid, Plaza de
Ciencias, 3, Ciudad Universitaria, 28040, Madrid, Spain,
bvitoriano@mat.ucm.es, J. Tinguaro Rodriguez, Javier Montero
SEDD is a decision support system for the assessment of the severity of natural
disasters based on fuzzy rule-based inference. SEDD provides an interpretable
assessment on the consequences of almost every potential disaster scenario,
through a learning and reasoning process based on historical information about
similar scenarios. Data requirements are small, which enables its adaptation
to the context of NGOs and countries requiring humanitarian aid. An analysis
to compare this DSS with some statistical techniques have been developed,
concluding that SEDD outranks these techniques.
2 - A New Auction for Object with Bimodal Valuation
Rosa Espinola, Statistic and Operation Research, Complutense
University, Avda Puerta de Hierro s/n, 28040, Madrid, Spain,
rosaev@estad.ucm.es, Javier Castro
IFORS 2011 - Melbourne HD-16
Several auctions have been defined for the sale of an object whose valuation
is subjective. However, in the simplest case, the benchmark model, the results
given in Myerson (1981) are the reference for these auctions. Myerson proved
that under different conditions for buyers and auction, any auction obtains the
same expected value for the seller. In this work we will change one of the hypotheses
about the auction and we will obtain that when the valuation of the
object is bimodal, with presence of linguistic uncertainty, the expected return
to the seller is greater than the Myerson auction.
3 - A Dynamical Analysis Method of Opinions in Social
Network for Decision Support
Jun Ma, University of Technology, Sydney, Australia,
junm@it.uts.edu.au, Jie Lu, Guangquan Zhang
This paper presents a gradual-changing opinion analysis (GCOA) method. In
the GCOA method, a gradual aggregation operator is developed. By means of
the gradual aggregation operator, the GCOA method takes both the values and
the orders of the opinions into account; moreover, it implements an implied
weighting procedure which can help decision makers to analyse obtained information
dynamically in real applications. Some real cases are then used to
evaluate the presented GCOA method.
4 - Financial Early Warning System: Adaptive Inferencebased
Fuzzy Neural Network
Vahid Behbood, Information Technology, University of
Technology Sydney, 2007, Sydney, NSW, Australia,
vbehbood@it.uts.edu.au, Jie Lu
This study develops an innovative Bank Failure Prediction (BFP) approach
which effectively integrates a fuzzy inference system with the learning ability
of a neural network to generate knowledge in the form of fuzzy rules. The
proposed approach uses a preprocessing phase in order to deal with the imbalance
problem. This study also develops an adaptive inference-based learning
algorithm as a means to reduce prediction error in the BFP approach. A set
of experiments has been conducted to validate the proposed approach. The results
show that it outperforms three existing financial warning systems: GenSo-
EWS; FCMAC-EWS; and MLP and, two popular fuzzy neural networks: AN-
FIS; DENFIS.
� HD-16
Thursday, 15:30-17:00
Meeting Room 209
OR for Health Contingency Operations
Stream: Health Care Applications
Invited session
Chair: Lawrence Fulton, CIS & QM, Texas State University, 112
Valona Drive, 78108, Cibolo, TX, United States, lf25@txstate.edu
1 - Two-Stage Stochastic Optimization for the Allocation of
Medical Assets in Steady State Combat Operations
Lawrence Fulton, CIS & QM, Texas State University, 112 Valona
Drive, 78108, Cibolo, TX, United States, lf25@txstate.edu, Leon
Lasdon, Reuben McDaniel, Nick Coppola
We present a two-stage stochastic optimization model for optimizing medical
asset emplacement in military stabilization operations. This model, updated
from previous work, evaluates the primary components of the medical system
in current combat operations, including the primary treatment and evacuation
components. Currently, this model is undergoing revision to support an analysis
of future medical requirements in stabilization operations.
2 - A Flexible Approach To Paramedics and Healthcare
Staff Scheduling
Patrick Soriano, Management Sciences, HEC Montreal, 3000,
ch. Côte-Ste-Catherine, H3T 2A7, Montreal, Québec, Canada,
patrick.soriano@hec.ca
Staff scheduling in Canadian hospitals and emergency medical services is a decentralized
management task where a large number of very different schedules
is generally needed to run daily operations. To deal with this situation, we propose
a flexible heuristic algorithm inspired from mathematical decomposition
techniques. Our approach can solve a large variety of healthcare staff scheduling
problems, including the typically more complex paramedic staff scheduling
problems.
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