Discrete optimization with fuzzy constraints

The primary benefit of fuzzy systems theory is to approximate system behavior where analytic functions or numerical relations do not exist. In this paper, heuristic fuzzy rules were used with the intention of improving the performance of optimization models, introducing experiential rules acquired from experts and utilizing recommendations. The aim of this paper was to define soft constraints using an adaptive network-based fuzzy inference system (ANFIS). This newly-developed soft constraint was applied to discrete optimization for obtaining optimal solutions. Even though the computational model is based on advanced computational technologies including fuzzy logic, neural networks and discrete optimization, it can be used to solve real-world problems of great interest for design engineers. The proposed computational model was used to find the minimum weight solutions for simply-supported laterally-restrained beams

Fuzzy Techniques for Decision Making

This book contains the successful invited submissions to a Special Issue of Symmetry in the subject area of “Fuzzy Techniques for Decision Making”. We invited contributions addressing novel techniques and tools for decision making (e.g., group or multi-criteria decision making) with notions that overcome the problem of finding the membership degree of each element as in Zadeh's original model. We could garner interesting articles on a variety of setups as well as applications. As a result, this book includes some novel techniques and tools for decision making, such as instrumental tools for analysis (correlation coefficients, similarity measures, aggregation operators) in various settings and novel methodological contributions (discrete optimization with fuzzy constraints, COMET, or fuzzy bi-matrix games) and applications (to project delivery systems, maintenance performance in industry, group emergencies, pedestrians flows, valuation of assets, water pollution control, or aquaculture enterprise sustainability). The published submissions cover models like fuzzy soft sets, hesitant fuzzy sets, (fuzzy) soft rough sets, neutrosophic sets, as well as other hybrid models