18,339 research outputs found
A reusable iterative optimization software library to solve combinatorial problems with approximate reasoning
Real world combinatorial optimization problems such as scheduling are
typically too complex to solve with exact methods. Additionally, the problems
often have to observe vaguely specified constraints of different importance,
the available data may be uncertain, and compromises between antagonistic
criteria may be necessary. We present a combination of approximate reasoning
based constraints and iterative optimization based heuristics that help to
model and solve such problems in a framework of C++ software libraries called
StarFLIP++. While initially developed to schedule continuous caster units in
steel plants, we present in this paper results from reusing the library
components in a shift scheduling system for the workforce of an industrial
production plant.Comment: 33 pages, 9 figures; for a project overview see
http://www.dbai.tuwien.ac.at/proj/StarFLIP
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An intelligent system for risk classification of stock investment projects
The proposed paper demonstrates that a hybrid fuzzy neural network can serve as a risk classifier of stock investment projects. The training algorithm for the regular part of the network is based on bidirectional incremental evolution proving more efficient than direct evolution. The approach is compared with other crisp and soft investment appraisal and trading techniques, while building a multimodel domain representation for an intelligent decision support system. Thus the advantages of each model are utilised while looking at the investment problem from different perspectives. The empirical results are based on UK companies traded on the London Stock Exchange
Assessment of Sustainable Development
The objective of this paper is to introduce fuzzy set theory and develop fuzzy mathematical models to assess sustainable development based on context-dependent economic, ecological, and societal sustainability indicators. Membership functions are at the core of fuzzy models, and define the degree to which indicators contribute to development. Although a decision-making process regarding sustainable development is subjective, fuzzy set theory links human expectations about development, expressed in linguistic propositions, to numerical data, expressed in measurements of sustainability indicators. In the future, practical implementation of such models will be based on elicitation of expert knowledge to construct a membership function. The fuzzy models developed in this paper provide a novel approach to support decisions regarding sustainable development.agriculture;assessment;fuzzy set theory;sustainable development
Soft computing techniques applied to finance
Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad
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