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Journal ArticleDOI

Integer flows in network with fuzzy capacity constraints

Stefan Chanas, +1 more
- 01 Mar 1986 - 
- Vol. 16, Iss: 1, pp 17-31
TLDR
A generalization of the maximum flow problem in a network with one-sided (upper) and two sided constraints on arc capacities is presented and allows for violating the capacity constraints in some ranges of tolerance.
Abstract
A generalization of the maximum flow problem in a network with one-sided (upper) and two sided constraints on arc capacities is presented. It allows for violating the capacity constraints in some ranges of tolerance. To solve the generalized problem the apparatus of the fuzzy set theory is used. The efficient algorithms are developed for integer flows.

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Citations
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Journal ArticleDOI

Fuzzy shortest paths

TL;DR: New models based on fuzzy shortest paths are presented and a general algorithm based on dynamic programming is given to solve the new models and the analysis concepts developed are discussed in terms of general fuzzy mathematical programming.
Journal ArticleDOI

Fuzzy genetic algorithm and applications

TL;DR: It is argued that this technique can produce good approximate solutions by applying it to solve a fuzzy optimization problem by introducing fuzzy genetic algorithms to (approximately) solve fuzzy optimization problems.
Journal ArticleDOI

The maximum flow problem of uncertain network

TL;DR: The main purpose of this paper is to solve the maximum flow in an uncertain network by under the framework of uncertainty theory.
Book ChapterDOI

Fuzzy Optimization in Networks

TL;DR: Some selected problems of fuzzy op­ timization in networks are analysed and possible approaches to the shortest path problem in a network with fuzzy arc lengths are discussed.
Book ChapterDOI

Discrete fuzzy optimization

TL;DR: The chapter presents selected problems and algorithms of fuzzy discrete optimization that constitute fuzzy equivalents of the following crisp ones: the maximal and cheapest flow and the shortest route problem in a network, the transportation and assignment problems, the CPM and PERT method.
References
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Book

Flows in networks

TL;DR: Ford and Fulkerson as mentioned in this paper set the foundation for the study of network flow problems and developed powerful computational tools for solving and analyzing network flow models, and also furthered the understanding of linear programming.
Journal ArticleDOI

Flows in Networks.

TL;DR: The techniques presented by Ford and Fulkerson spurred the development of powerful computational tools for solving and analyzing network flow models, and also furthered the understanding of linear programming.
Journal ArticleDOI

Fuzzy programming and linear programming with several objective functions

TL;DR: It is shown that solutions obtained by fuzzy linear programming are always efficient solutions and the consequences of using different ways of combining individual objective functions in order to determine an “optimal” compromise solution are shown.
Journal ArticleDOI

Latent connectives in human decision making

TL;DR: The results of the experiments support the hypothesis that people often use compensatory procedures and suggest a new class of operators which varies with respect to a parameter of compensation.
Journal ArticleDOI

On a general class of fuzzy connectives

TL;DR: A general class of conectives, intersection and union, are presented for fuzzy sets, and the properties of this class are studied in comparison to the ordinary intersection andunion.