Paper Info
Title
Possibilistic Minmax Regret Sequencing Problems With Fuzzy Parameters
Abstract
In this paper, a class of sequencing problems with uncertain parameters is discussed. The uncertainty is modeled by the usage of fuzzy intervals, whose membership functions are regarded as possibility distributions for the values of unknown parameters. It is shown how to use possibility theory to find robust solutions under fuzzy parameters; this paper presents a general framework, together with applications, to some classical sequencing problems. First, the interval sequencing problems with the minmax regret criterion are discussed. The state of the art in this area is recalled. Next, the fuzzy sequencing problems, in which the classical intervals are replaced with fuzzy ones, are investigated. A possibilistic interpretation of such problems, solution concepts, and algorithms for the computation of a solution are described. In particular, it is shown that every fuzzy problem can be efficiently solved if a polynomial algorithm for the corresponding interval problem with the minmax regret criterion is known. Some methods to deal with NP-hard problems are also proposed, and the efficiency of these methods is explored.
Year
DOI
Venue
2011
10.1109/TFUZZ.2011.2159982
IEEE T. Fuzzy Systems
Keywords
Field
DocType
fuzzy parameter,fuzzy interval,interval sequencing problem,classical interval,sequencing problem,classical sequencing problem,sequencing problems,fuzzy parameters,fuzzy problem,possibilistic minmax regret,fuzzy sequencing problem,minmax regret criterion,np-hard problem,sequencing,optimization,scheduling,job shop scheduling,membership function,polynomials,fuzzy set theory,computational complexity,np hard problem,computer model,computational modeling,solution concept,uncertainty,possibility theory
Mathematical optimization,Fuzzy classification,Regret,Fuzzy logic,Fuzzy set,Possibility theory,Artificial intelligence,Type-2 fuzzy sets and systems,Fuzzy number,Mathematics,Machine learning,Computational complexity theory
Journal
Volume
Issue
ISSN
19
6
1063-6706
Citations 
PageRank 
References 
2
0.37
26
Authors
2
Name
Order
Citations
PageRank
Adam Kasperski135233.64
Paweł Zieliński222728.62