Parametric Non-linear programming approach for N-policy queues with infinite capacity

This paper proposes a procedure to construct the membership function of N-policy queue with infinite capacity.  By using mathematical programming we construct the membership function of the system performance measure in which arrival rate and service rate are fuzzy numbers.  Based on a-cut approach and Zadeh’s extension principle, the fuzzy queues are converted into a family of crisp queues.  Suitable real world example is exemplified to analyze N-policy fuzzy queues.  Extending this model to fuzzy environment it would have further more wider applications

Fuzzy linear programs with optimal tolerance levels

It is usually supposed that tolerance levels are determined by the decision maker a priori in a fuzzy linear program (FLP). In this paper we shall suppose that the decision maker does not care about the particular values of tolerance levels, but he wishes to minimize their weighted sum. This is a new statement of FLP, because here the tolerance levels are also treated as variables

Multiple fuzzy reasoning approach to fuzzy mathematical programming problems

We suggest solving fuzzy mathematical programming problems via the use of multiple fuzzy reasoning techniques. We show that our approach gives Buckley’s solution [1] to possibilistic mathematical programs when the inequality relations are understood in possibilistic sense

Decision Making in the Medical Domain: Comparing the Effectiveness of GP-Generated Fuzzy Intelligent Structures

ABSTRACT: In this work, we examine the effectiveness of two intelligent models in medical domains. Namely, we apply grammar-guided genetic programming to produce fuzzy intelligent structures, such as fuzzy rule-based systems and fuzzy Petri nets, in medical data mining tasks. First, we use two context-free grammars to describe fuzzy rule-based systems and fuzzy Petri nets with genetic programming. Then, we apply cellular encoding in order to express the fuzzy Petri nets with arbitrary size and topology. The models are examined thoroughly in four real-world medical data sets. Results are presented in detail and the competitive advantages and drawbacks of the selected methodologies are discussed, in respect to the nature of each application domain. Conclusions are drawn on the effectiveness and efficiency of the presented approach

A Fuzzy Logic Programming Environment for Managing Similarity and Truth Degrees

FASILL (acronym of "Fuzzy Aggregators and Similarity Into a Logic Language") is a fuzzy logic programming language with implicit/explicit truth degree annotations, a great variety of connectives and unification by similarity. FASILL integrates and extends features coming from MALP (Multi-Adjoint Logic Programming, a fuzzy logic language with explicitly annotated rules) and Bousi~Prolog (which uses a weak unification algorithm and is well suited for flexible query answering). Hence, it properly manages similarity and truth degrees in a single framework combining the expressive benefits of both languages. This paper presents the main features and implementations details of FASILL. Along the paper we describe its syntax and operational semantics and we give clues of the implementation of the lattice module and the similarity module, two of the main building blocks of the new programming environment which enriches the FLOPER system developed in our research group.Comment: In Proceedings PROLE 2014, arXiv:1501.0169

An Interactive Fuzzy Satisficing Method for Fuzzy Random Multiobjective 0-1 Programming Problems through Probability Maximization Using Possibility

In this paper, we focus on multiobjective 0-1 programming problems under the situation where stochastic uncertainty and vagueness exist at the same time. We formulate them as fuzzy random multiobjective 0-1 programming problems where coefficients of objective functions are fuzzy random variables. For the formulated problem, we propose an interactive fuzzy satisficing method through probability maximization using of possibility