Convex fuzzy mapping with differentiability and its application in fuzzy optimization

The concepts of differentiability, convexity, generalized convexity and minimization of a fuzzy mapping are known in the literature. The purpose of this present paper is to extend and generalize these concepts to fuzzy mappings of several variables using Buckley–Feuring approach for fuzzy differentiation and derive Karush–Kuhn–Tucker condition for the constrained fuzzy minimization problem.     

Possibilistic linear regression analysis for fuzzy data

Fuzzy data given by expert knowledge can be regarded as a possibility distribution by which possibilistic linear systems are defined. Recently, it has become important to deal with fuzzy data in connection with expert knowledge. Three formulations of possibilistic linear regression analysis are proposed here to deal with fuzzy data. Since our formulations can be reduced to linear programming problems, the merit of our formulations is to be able to obtain easily fuzzy parameters in possibilistic linear models and to add other constraint conditions which might be obtained from expert knowledge of fuzzy parameters. This approach can be regarded as a fuzzy interval analysis in a fuzzy environment.     

一致性互反Fuzzy判断矩阵的传递性

研究矩阵元素为Fuzzy数的互反判断矩阵的传递性质.首先得到了判断两个Fuzzy数近似相等的等价条件,并得到了揭示Fuzzy数的核之间关系的一个充要条件.在此基础上,进一步证明了一致性互反Fuzzy判断矩阵具有传递性的两个结论.这两个结论说明:在层次分析法中,用一致性互反Fuzzy判断矩阵表示一组方案在同一目标下的两两重要性比较是符合理性决策的思维特征的.     

Fuzzy level control of a tank with optimum valve movement

This paper presents a fuzzy-based controller chip to control the level of a tank. A motor valve controls the tank level. The significant aim of this paper is to present a controller for minimizing the movement of the motor valve, at the same time, setting the level of the tank at defined points. The advantages of this type of control are to reduce both motor and input valve amortization and to increase their useful lifetime. On the other hand, simple and inexpensive elements are used in order to measure tank level and output flow. This is another advantage of the method. The controller is designed by fuzzy techniques in CMOS technology. The die size of the chip is .     

A multi-objective wholesaler–retailers inventory-distribution model with controllable lead-time based on probabilistic fuzzy set and triangular fuzzy number

This paper develops a single wholesaler and multi retailers mixture inventory distribution model for a single item involving controllable lead-time with backorder and lost sales. The retailers purchase their items from the wholesaler in lots at some intervals throughout the year to meet the customers’ demand. Not to loose the demands, the retailers offer a price discount to the customers on the stock-out items. Here, it is assumed that the lead-time demands of retailers are uncertain in both stochastic and fuzzy sense, i.e., these are simultaneously random and imprecise. To implement this behavior of the lead-time demands, at first, these demands are assumed to be random, say following a normal distribution. With these random demands, the expected total cost for each retailer is obtained. Now, the mean lead-time demands (which are crisp ones) of the retailers are fuzzified. This fuzzy nature of the lead-time demands implies that the annual average demands of the retailers must be fuzzy numbers, suppose these are triangular fuzzy numbers. Using signed distance technique for defuzzification, the estimate of total costs for each retailer is derived. Therefore, the problem is reduced to optimize the crisp annual costs of wholesaler and retailers separately. The multi-objective model is solved using Global Criteria method. Numerical illustrations have been made with the help of an example taking two retailers into consideration. Mathematical analyses have been made for global pareto-optimal solutions of the multi-objective optimization problem. Sensitivity analyses have been made on backorder ratio and pareto-optimal solutions for wholesaler and different retailers are compared graphically.     

The optimization problem over a distributive lattice

In this paper we give a necessary and sufficient condition for existence of minimal solution(s) of the linear system A * X ≥ b where A, b are fixed matrices and X is an unknown matrix over a lattice. Next, an algorithm which finds these minimal solutions over a distributive lattice is given. Finally, we find an optimal solution for the optimization problem min {Z = C * X | A * X ≥ b} where C is the given matrix of coefficients of objective function Z. This research was completed while the author was a visitor of the Center for Informatics and Applied Optimization, University of Ballarat, Ballarat, Australia.     

聚炔、累积多烯与全碳环分子的模糊对称性

近年来我们关于分子模糊对称性的工作多属于模糊点对称性的研究, 关于模糊空间对称性探讨较少. 聚炔作为线状一维模糊周期分子, 我们曾对其进行了初步分析. 虽然对于聚炔分子骨架的分析比较全面, 但由于繁冗的计算使我们对分子轨道(MO)模糊对称性的分析只限于少数典型分子. 本文将对不同的聚炔分子MO模糊对称性特征进行较为系统的分析. 结果表明包含不同碳原子数目的分子轨道模糊对称性参数值之间有一定相关性. 此外我们还对一些相关体系分子的MO进行分析, 累积多烯分子虽然并非线型分子, 但其π-MO相关的碳原子处于线性位置, 可依模糊一维周期的G11体系处理. 按Born-Karman近似, 即n个单元的一维周期对称群与Cn点群同构, 本文还分析了相关的全碳环分子的MO的对称性和模糊对称性. 努力寻求与一维周期性相关的模糊对称性规律性特征.     

A fuzzy stochastic model for telecommunications bandwidth brokers under probabilistic QoS measures

In this paper, we model and solve profit maximization problem of a telecommunications Bandwidth Broker (BB) under uncertain market and network infrastructure conditions. The BB may lease network capacity from a set of Backbone Providers (BPs) or from other BBs in order to gain profit by leasing already purchased capacity to end-users. BB’s problem becomes harder to deal with when bandwidth requests of end-users, profit and cost margins are not known in advance. The novelty of the proposed work is the development of a mechanism via combining fuzzy and stochastic programming methodologies for solving complex BP selection and bandwidth demand allocation problem in communication networks, based on the fact that information needed for making these decisions is not available prior to leasing capacity. In addition, suggested model aims to maximize BB’s decision maker’s satisfaction ratio rather than just profit. As a solution strategy, the resulting fuzzy stochastic programming model is transformed into deterministic crisp equivalent form and then solved to optimality. Finally, the numerical experiments show that on the average, proposed approach provides 14.30% more profit and 69.50% more satisfaction ratio compared to deterministic approaches in which randomness and vagueness in the market and infrastructure are ignored.     

A transportation problem with fuzzy-stochastic cost

In this paper, we consider some transportation problems (TPs) with different types of fuzzy-stochastic unit transportation costs and budget constraints. These fuzzy stochastic costs are reduced to corresponding crisp ones in two different ways. For the first method, using the definition of α$\alpha$-cut of the fuzzy numbers, expectation is taken separately on both lower and upper α$\alpha$-cuts and then mean expectation is calculated with the help of signed distance. In the second procedure, we realize fuzzy random events (ξ?r)$(\xi ?r)$ and (ξ?r)$(\xi ?r)$ for the fuzzy random variable (ξ)$(\xi )$. Using credibility measure of these events, mean chances for the above fuzzy random events are calculated and then expectation is taken to get the crisp expressions. The reduced deterministic problems of the fuzzy stochastic TP are solved using a real coded genetic algorithm with Roulette wheel selection, arithmetic crossover and random mutation. Few numerical examples are demonstrated to find the optimal solutions of the proposed models.     

Interval optimization based line sampling method for fuzzy and random reliability analysis

For structural system with fuzzy variables as well as random variables, a novel algorithm for obtaining membership function of fuzzy reliability is presented on interval optimization based Line Sampling (LS) method. In the presented algorithm, the value domain of the fuzzy variables under the given membership level is firstly obtained according to their membership functions. Then, in the value domain of the fuzzy variables, bounds of reliability of the structure are obtained by the nesting analysis of the interval optimization, which is performed by modern heuristic methods, and reliability analysis, which is achieved by the LS method in the reduced space of the random variables. In this way the uncertainties of the input variables are propagated to the safety measurement of the structure, and the membership function of the fuzzy reliability is obtained. The presented algorithm not only inherits the advantage of the direct Monte Carlo method in propagating and distinguishing the fuzzy and random uncertainties, but also can improve the computational efficiency tremendously in case of acceptable precision. Several examples are used to illustrate the advantages of the presented algorithm.