Written on May 28, 2018.
Special Session 01: Soft computing methods in quantitative management and decision making processes
Ioan Dzitac, Aurel Vlaicu University of Arad & Agora University of Oradea, Romania, firstname.lastname@example.org
Florin Gheorghe Filip,Romanian Academy, Romania, email@example.com
Misu-Jan Manolescu, Agora University of Oradea, Romania, firstname.lastname@example.org
Simona Dzitac,University of Oradea, Romania, email@example.com
In accordance with Zadeh’s definition, Soft Computing (SC) consist of computational techniques in computer science, machine learning and some engineering disciplines, which study, model, and analyze very complex reality: those for which more traditional methods have unusable or inefficiently.
SC uses soft techniques, contrasting it with classical artificial intelligence, Hard Computing (HC) techniques), and includes: Fuzzy Logic, Neural Computing, Evolutionary Computation, Machine Learning, and Probabilistic Reasoning.
HC is bound by a Computer Science (CS) concept called NP-Complete, which means that there is a direct connection between the size of a problem and the amount of resources needed to solve that called "grand challenge problem". SC aids to surmount NP-complete problems by using inexact methods to give useful but inexact answers to intractable problems.
SC became a formal CS area of study in the early 1990’s. Earlier computational approaches could model and precisely analyze only relatively simple systems. More complex systems arising in biology, medicine, the humanities, management sciences, and similar fields often remained intractable to HC. It should be pointed out that simplicity and complexity of systems are relative, and many conventional mathematical models have been both challenging and very productive.
SC techniques resemble biological processes more closely than traditional techniques, which are largely based on formal logical systems, such as Boolean logic, or rely heavily on computer-aided numerical analysis (as in finite element analysis).
SC techniques are intended to complement HC techniques. Unlike HC schemes, which strive for exactness and full truth, soft computing techniques exploit the given tolerance of imprecision, partial truth, and uncertainty for a particular problem. The inductive reasoning plays a larger role in SC than in HC. SC and HC can be used together in certain fusion techniques.
Soft Computing 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.
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.
Decision making in fuzzy environments 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 (1970) 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.
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.