ITQM2017, 8-10 December, 2017, Delhi, India
Special Session 01: Soft computing methods in quantitative management and decision making processes
Organizers and Chairs:
1. Florin Gheorghe Filip, Romanian Academy, Romania& Doctor Honoris Causa of Agora University;
2. Ioan Dzitac, Rector of Agora University of Oradea & Professor at Aurel Vlaicu University of Arad, Romania;
3. Simona Dzitac, Assoc. Professor at University of Oradea, Romania & Researcher at R&D Agora Center.
In
according with Zadeh’s definition, Soft Computing (SC) is based on
Fuzzy Logic, Neural Networks, Support Vector Machines, Evolutionary
Computation, Machine Learning and Probabilistic Reasoning. SC can deal
with ambiguous or noisy data and is tolerant of imprecision,
uncertainty, partial truth, and approximation. In effect, the role model
for SC is the human mind. Artificial Intelligence and Computational
Intelligence based on SC provide the background for the development of
smart management systems and decisions in case of ill-posed problems.In many real-world situations, the problems of decision making are
subjected to some constraints, objectives and consequences that are not
accurately known. After Bellman and Zadeh introduced for the first time
fuzzy sets within multiple-criteria decision-making (MCDM), many
researchers have been preoccupied by decision making in fuzzy
environments. The fusion between MCDM and fuzzy set theory has led to a
new decision theory, known today as fuzzy multi-criteria decision making
(FMCDM), where we have decision-maker models that can deal with
incomplete and uncertain knowledge and information. The most important
thing is that, when we want to assess, judge or decide we usually use a
natural language in which the words do not have a clear, definite
meaning. As a result, we need fuzzy numbers to express linguistic
variables, to describe the subjective judgement of a decision maker in a
quantitative manner. Fuzzy numbers (FN) most often used are triangular
FN, trapezoidal FN and Gaussian FN. We highlight that the concept of
linguistic variable introduced by Lotfi A. Zadeh in 1975 allows
computation with words instead of numbers and thus linguistic terms
defined by fuzzy sets are intensely used in problems of decision theory
for modelling uncertain information. After Atanassov introduced the
concept of intuitionistic fuzzy sets, where each element is
characterized by a membership function, as in fuzzy sets, as well as by a
non-membership function, the interest in the study of the problems of
decision making theory with the help of intuitionistic fuzzy sets has
increased.The goal of this special session is to bring together researchers
interested in applications of soft computing algorithms and procedures
in quantitative management and decision making, in order to exchange
ideas on problems, solutions, and to work together in a friendly
environment.
Topics of interest include, but are not limited to, the following:
- Ant colony optimization algorithms;
- Artificial intelligence methods for web mining;
- Bayesian networks and decision graphs; Computational intelligence methods for data mining;
- Decision support systems for quantitative management;
- Decision making with missing and/or uncertain data;
- Fuzzy multi-criteria decision making;
- Fuzzy and neuro-fuzzy modelling and simulation;
- Fuzzy numbers applications to decision making;
- Fuzzy-sets-based models in operation research;
- Knowledge Discovery in Databases;
- Machine learning for intelligent support of quantitative management;
- Neural networks in decision making tools;
- Smarter decisions;
- Support Vector Machine in SC applications;
Special Sessions: http://www.itqm-meeting.org/2017/workshop.htm#ss1 |
