Fuzzy routing

Arnold, Wolfgang; Hellendoorn, Hans; Seising, Rudolf; Thomas, Christoph; Weitzel, Andreas

doi:10.1016/0165-0114(95)00357-6

Cited by 19 publications

(8 citation statements)

References 16 publications

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“…What is more, this optimization not only allows us to reduce the complexity of the fuzzy shortest path problem to that of the crisp single-criterion labeling algorithms without sacrificing too much in the way of expressivity, but may also serve to give us more insight into, and hence attribute more credit to, heuristic approaches like the one in Ref. 2 by embedding them in a sound theoretical framework.…”

Section: Resultsmentioning

confidence: 97%

“…This label is added to the empty queue PQ and is immediately removed from it by takeNext~PQ!. Label Step 1: Nodes v 2 and v 3 are children of s; suppose that, for example, v 2 is considered first: Step 2: @~20,20,10,10!,~s,1!# v 2 1 is selected and becomes permanent. Step 3: @~58,60,13,15!,~v 2 ,1!# v 3 1 is selected and becomes permanent.…”

Section: A Numerical Examplementioning

confidence: 99%

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Shortest paths in fuzzy weighted graphs

Cornelis

Kesel

Kerre

2004

Int. J. Intell. Syst.

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The task of finding shortest paths in weighted graphs is one of the archetypical problems encountered in the domain of combinatorial optimization and has been studied intensively over the past five decades. More recently, fuzzy weighted graphs, along with generalizations of algorithms for finding optimal paths within them, have emerged as an adequate modeling tool for prohibitively complex and/or inherently imprecise systems. We review and formalize these algorithms, paying special attention to the ranking methods used for path comparison. We show which criteria must be met for algorithm correctness and present an efficient method, based on defuzzification of fuzzy weights, for finding optimal paths.

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Section: Resultsmentioning

confidence: 97%

Section: A Numerical Examplementioning

confidence: 99%

Shortest paths in fuzzy weighted graphs

Cornelis

Kesel

Kerre

2004

Int. J. Intell. Syst.

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“…Fileti et al 2007), and its applications are widely spreading. Since fuzzy logic is an excellent heuristic method to deal with the complexity problem in communication and computer networks (Arnold et al 1997), some researchers have used fuzzy logic in these areas. For instance, priority control of ATM networks with fuzzy logic approach has investigated by Catania et al (1999) to control the traffic flows.…”

Section: Fuzzy Controlmentioning

confidence: 99%

A fuzzy based threshold policy for a single server retrial queue with vacations

Khodadadi

Jolai

2010

Cent Eur J Oper Res

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In this paper we deal with a single server retrial queue with vacations. The server serves the customers until the system becomes empty, then it takes a vacation. The system consists of two types of costs. The blocking cost is considered whenever a customer is blocked either because of the server is busy or off. There is also a cost each time the server is turned on. The problem is to find an effective policy for turning on the dormant server. We propose a Fuzzy Based Threshold Policy (FBTP) to control the server, substitute for conventional threshold policies. The FBTP is based on four input parameters, an inference stage and it is tuned up using a stochastic List Based Threshold Accepting (LBTA) algorithm. Simulation models are developed to validate the fuzzy controller. Numerical experiments are provided to show that the proposed method is superior to crisp threshold policies.

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“…Erst Ende der achtziger Jahre entdeckte die Industrie, vornehmlich die japanische, diese mathematische Theorie für sich. Heute gibt es eine Vielzahl von Anwendungen, die sich die Vorteile der Fuzzy-Logik zunutze machen [5,2,3].…”

Section: Fuzzy-mengen Fuzzy-logik Und Fuzzy-systemeunclassified