Special Issue "Applications of Fuzzy Optimization and Fuzzy Decision Making"
Deadline for manuscript submissions: 30 September 2020.
Interests: Fuzzy Decision Making; Software Engineering; Requirements Engineering; Systems Analysis and Design
We invite you to submit your latest applied research in the field of fuzzy optimization and decision making to the Special Issue entitled “Applications of Fuzzy Optimization and Fuzzy Decision Making”. The aim of the Special Issue is to expand the applicability of fuzzy optimization and decision making for solving various types of problems in the areas of economics, business, engineering, management, operations research, etc. Any experimental research or empirical study of theoretical developments in fuzzy optimization and decision making is highly welcome. Additionally, research papers presenting solution methods and/or studying their computational complexity, and proposing new algorithms to solve fuzzy optimization and decision making problems, in an effective and efficient manner, are also welcome. We are looking forward to receive innovative approaches that apply, in practical settings, state-of-the art mathematical/algorithmic techniques from fuzzy technology, computational intelligence and soft-computing methodologies, with the aim to offer robust solutions for complex optimization and decision making problems characterized by non-probabilistic uncertainty, vagueness, ambiguity, and hesitation. Such type of papers will address the suitability, validity, and advantages of using fuzzy technologies and the enhancement of them using intelligent methods to treat real-life problems from various disciplines.
Submissions may present, but are not limited to, applications of the following methods:
fuzzy multi-criteria method;
soft computing methods;
intuitionistic fuzzy sets;
fuzzy data mining;
hybrid fuzzy optimization;
evolutionary and swarm intelligence methods;
bio-inspired intelligent computational methods;
fuzzy cognitive maps;
big data optimization;
fuzzy linear programming;
fuzzy nonlinear programming;
discrete fuzzy optimization;
continuous fuzzy optimization;
fuzzy integer programming;
fuzzy dynamic programming;
fuzzy multi-objective programming;
possibilistic linear programming;
fuzzy optimization control;
fuzzy set operation;
fuzzy sensitivity analysis;
fuzzy dual theory;
interval-valued probability measures.
Application areas include, but are not limited to, the following:
operations and production management;
smart cities engineering;
big data analytics;
Prof. Dr. Vassilis C. Gerogiannis
Manuscript Submission Information
Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.
Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Mathematics is an international peer-reviewed open access monthly journal published by MDPI.
Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.
The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.
Title: Extending Fuzzy Cognitive Graphs With Tensor-Based Distance Metrics
Authors: Georgios Drakopoulos 1,* , Andreas Kanavos 2 , Phivos Mylonas 1 and Panagiotis Pintelas 3
Affiliation: 1 Humanistic and Social Informatics Lab, Department of Informatics, Ionian University; [email protected]; [email protected] 2 Computer Engineering and Informatics Department, University of Patras; [email protected] 3 Department of Mathematics, University of Patras; [email protected]
Abstract: Cognitive graphs are generalized and high level representations of the key topological attributes of real or abstract spatial environments which are progressively built by a sequence of noisy observations. Higher order relationships are represented in lower dimensions as patterns with similar structure so that salient features are preserved during projection. The latter can be partially attributed to the form of the distance metrics in the original space. Currently cognitive graphs play a crucial role in psychology as it is believed that this is how humans construct inner representations of both actual space and, perhaps more interestingly, complex fields comprising of interconnected notions such as languages with dedicated neurons located at hippocampus. In deep learning cognitive graphs are effective tools for simultaneous dimensionality reduction and visualization with applications to edge prediction, ontology alignment, and transfer learning. Fuzzy edge cognitive graphs have been proposed as an algorithmic way for representing inside the original space locations of incomplete knowledge or errors caused by either noisy or insufficient number of observations. In this graph class a probability denoting how likely is an edge to belong to the graph is mapped to that particular edge. This allows the definition of edge costs based, perhaps non-linearly, on these probabilities. In this article the potential of employing tensor-based distance metrics in order to assess distances dependent on multiple topological features in a locally higher order way is explored. This is consistent both with substantial empirical evidence on how human associative memory works and with the requirement for smooth filling when local information is insufficient. As a concrete example, the Kaggle Myers-Briggs personality type dataset is used with norm- and tensor-based distance metrics. The coherency of the resulting cognitive graphs, compared against the dataset ground truth, indicate that the latter approach achieves higher accuracy and F1 scores.
Title: Supportiveness of Low-Carbon Energy Policy Making via a Comparative Analysis of Fuzzy Multi-Criteria Decision Making Methodologies
Authors: Konstantinos Kokkinos 1 and Vayos Karayannis 2,*
Affiliation: 1 Energy Systems Department, University of Thessaly, Larissa, Greece; [email protected] 2 Chemical Engineering Department, University of Western Macedonia, Kozani, Greece; [email protected]
Abstract: The deployment and management of low-carbon energy (LCE) technologies and installations represents an imperative to face climate change. LCE planning is an interminable process affected by a multitude of anthropogenic, economic, environmental and health factors. A major challenge for policy makers is to select the future energy strategy that maximizes sustainability. Thus, policy formulation and evaluation needs to be addressed in an analytical manner including multidisciplinary knowledge emanating from diverse social stakeholders. In this work, a comparative analysis of the LCE planning is provided, evaluating different multi-criteria decision making methodologies: Initially, by applying SWOT (strengths, weaknesses, opportunities and threats) analysis, the available energy strategy alternatives are prioritized. A variety of stakeholders is surveyed for that reason. To deal with the ambiguity occurred in their judgements, Fuzzy Goal Programming (F-GP) is used for the translation into fuzzy numbers. Then Stochastic Fuzzy Analytic Hierarchical Process (SF-AHP) and Fuzzy Technique for Order Performance by Similarity to Ideal Solution (F-TOPSIS) are applied to evaluate a repertoire of energy alternatives including solar, biofuel, hydro and wind power. The methodologies are estimated based on the same set of tangible and intangible criteria for the case study of Thessaly region, Greece.
Title: Fuzzy Cognitive Maps Optimization for Decision Making and Prediction
Authors: Katarzyna Poczeta 1, Elpiniki Papageorgiou 2,* and Vassilis C. Gerogiannis 3,*
Affiliation: 1 Department of Information Systems, Kielce University of Technology, Kielce, Poland; [email protected] 2 Faculty of Technology, University of Thessaly, Geopolis, Larisa, Greece; [email protected] 3 Department of Digital Systems, University of Thessaly, Geopolis, Larisa, Greece; [email protected]
Abstract: The problem of representing and analyzing complexity of models constructed by data is a difficult one emerging the need for new, more effective techniques, even there are many methodologies proposed to cope with it recently. The main idea of this paper is to systematically create a nested structure based on a Fuzzy Cognitive Map (FCM) in which each element/concept at a higher map level is decomposed into another FCM that provides more detailed and more precise representation of complex time series data. The resulted nested structure is optimized through evolutionary and hybrid learning algorithms. Through applying a dynamic optimization process, the whole nested structure based on FCMs is restructured in order to derive important relationships between map concepts at every nesting level as well as to determine the weights of these relationships on the basis of the available time series. This allows discovering and describing hidden relationships between important map concepts. The paper recommends to apply the proposed nested approach for the forecasting of consumption in energy buildings, as well as for decision making tasks in selected complex problems from the energy domain devoted to energy efficiency.