Paper Info
Title
A (Soft) Robustness for Possibilistic Optimization Problems
Abstract
This paper discusses a linear programming problem and a general combinatorial optimization problem with uncertain parameters, whose unknown distributions are modeled by fuzzy intervals. The fuzzy intervals have possibilistic interpretation. Some criteria for choosing robust solutions to the problems under consideration, resulting from the use of possibilistic decision theory, are proposed. It is shown that the fuzzy problems constructed are computationally tractable if their deterministic counterparts are polynomially solvable. The algorithms for finding the robust solutions are provided. Some computational experiments are performed.
Year
DOI
Venue
2019
10.1109/FUZZ-IEEE.2019.8858834
2019 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE)
Keywords
Field
DocType
possibilistic decision theory,fuzzy problems,robust solutions,possibilistic optimization problems,linear programming problem,general combinatorial optimization problem,uncertain parameters,unknown distributions,fuzzy intervals,possibilistic interpretation
Mathematical optimization,Combinatorial optimization problem,Computer science,Fuzzy logic,Possibility theory,Robustness (computer science),Artificial intelligence,Decision theory,Linear programming,Optimization problem,Machine learning
Conference
ISSN
ISBN
Citations 
1544-5615
978-1-5386-1729-8
1
PageRank 
References 
Authors
0.36
9
2
Name
Order
Citations
PageRank
Adam Kasperski135233.64
Paweł Zieliński222728.62