The paper analyses the linear programming problem with fuzzy coefficients in the objective function. The set of nondominated (ND) solutions with respect to an assumed fuzzy preference relation, according to Orlovsky&#...
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The paper analyses the linear programming problem with fuzzy coefficients in the objective function. The set of nondominated (ND) solutions with respect to an assumed fuzzy preference relation, according to Orlovsky's concept, is supposed to be the solution of the problem. Special attention is paid to unfuzzy nondominated (UND) solutions (the solutions which are nondominated to the degree one). The main results of the paper are sufficient conditions on a fuzzy preference relation allowing to reduce the problem of determining UND solutions to that of determining the optimal solutions of a classical linear programming problem. These solutions can thus be determined by means of classical linear programming methods. (C) 2000 Elsevier Science B.V. All rights reserved.
作者：
Mikhailov, LUMIST
Dept Computat Manchester M60 1QD Lancs England
The main objective of this paper is to present a new fuzzy approach to partnership selection in the formation of virtual enterprises. The phases of the virtual enterprise life cycle are briefly described and it is sho...
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The main objective of this paper is to present a new fuzzy approach to partnership selection in the formation of virtual enterprises. The phases of the virtual enterprise life cycle are briefly described and it is shown that the partnership selection is a key factor in the formation of such complex organisations. It is justified that the partnership selection process should be formulated as a multiple criteria decision-making problem under uncertainty. A new fuzzy programming method is proposed for assessment of uncertain weights of partnership selection criteria and uncertain scores of alternative partners, in the basic framework of the Analytic Hierarchy Process. The proposed fuzzy prioritisation method uses interval pairwise comparison judgements rather than exact numerical values of the comparison ratios and transforms the initial prioritisation problem into a linear program. The method can derive priorities from inconsistent interval comparison matrices, thus eliminating the drawbacks of the existing interval prioritisation methods. Moreover, the method generalises the known prioritisation methods, since it can be used for deriving priorities from exact, interval or mixed comparison matrices, regardless of their consistency. A numerical example, illustrating the application of this method to partnership selection process is given. (C) 2002 Elsevier Science Ltd. All rights reserved.
This paper will present a novel concept of expected values of fuzzy variables, which is essentially a type of Choquet integral and coincides with that of random variables. In order to calculate the expected value of g...
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This paper will present a novel concept of expected values of fuzzy variables, which is essentially a type of Choquet integral and coincides with that of random variables. In order to calculate the expected value of general fuzzy variable, a fuzzy simulation technique is also designed. Finally, we construct a spectrum, of fuzzy expected value models,,and integrate fuzzy simulation, neural network, and genetic algorithms to produce a hybrid intelligent algorithm for solving general fuzzy expected value models.
作者：
Liu, BDTsinghua Univ
Dept Math Sci Uncertain Syst Lab Beijing 100084 Peoples R China
This paper presents the concepts of uncertain environment, event, chance function and principle of uncertainty for fuzzy random decision systems, thus offering a theoretical framework of fuzzy random dependent-chance ...
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This paper presents the concepts of uncertain environment, event, chance function and principle of uncertainty for fuzzy random decision systems, thus offering a theoretical framework of fuzzy random dependent-chance programming. A hybrid intelligent algorithm is applied to solving fuzzy random dependent-chance programming models. Some numerical examples are also provided to illustrate the effectiveness of hybrid intelligent algorithm.
作者：
Liu, BDTsinghua Univ
Dept Math Sci Uncertain Syst Lab Beijing 100084 Peoples R China
By fuzzy random programming, we mean the optimization theory dealing with fuzzy random decision problems. This paper presents a new concept of chance of fuzzy random events and then constructs a general framework of f...
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By fuzzy random programming, we mean the optimization theory dealing with fuzzy random decision problems. This paper presents a new concept of chance of fuzzy random events and then constructs a general framework of fuzzy random chance-constrained programming (CCP). We also design a spectrum of fuzzy random simulations for computing uncertain functions arising in the area of fuzzy random programming. To speed up the process of handling uncertain functions, we train a neural network to approximate uncertain functions based on the training data generated by fuzzy random simulation. Finally, we integrate fuzzy random simulation, neural network, and genetic algorithm to produce a more powerful and effective hybrid intelligent algorithm for solving fuzzy random programming models and illustrate its effectiveness by some numerical examples.
In this paper, we propose interactive fuzzy programming for multi-level 0-1 programming problems through genetic algorithms. Our method is supposed to apply to hierarchical decision problems in which decision-making a...
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In this paper, we propose interactive fuzzy programming for multi-level 0-1 programming problems through genetic algorithms. Our method is supposed to apply to hierarchical decision problems in which decision-making at each level is sequential from upper to lower level and decision makers are essentially cooperative. After determining the fuzzy goals of the decision makers at all levels, a satisfactory solution is derived efficiently by updating the satisfactory degrees of the decision makers at the upper level with considerations of overall satisfactory balance among all levels. An illustrative numerical example for three-level 0-1 programming problems is provided to demonstrate the feasibility of the proposed method. (C) 1999 Elsevier Science B.V. All rights reserved.
Yang ct al., in their paper "fuzzy programming with nonlinear membership functions...", published in fuzzy Sets and Systems 41 (1991), declared that their model can solve a fuzzy program with an S-shaped mem...
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Yang ct al., in their paper "fuzzy programming with nonlinear membership functions...", published in fuzzy Sets and Systems 41 (1991), declared that their model can solve a fuzzy program with an S-shaped membership function by adding only one 0-1 variable. This paper indicates that their declaration is correct only for a specific type of S-shape membership functions. We propose another model to treat the fuzzy programs which cannot be solved effectively by Yang et al. (C) 1999 Elsevier Science B.V. All rights reserved.
An introduction to mathematical programming based methods was given in the first tutorial of this three-part series (October 2000 PEJ, p.245). This second part covers major modern heuristic optimisation techniques and...
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An introduction to mathematical programming based methods was given in the first tutorial of this three-part series (October 2000 PEJ, p.245). This second part covers major modern heuristic optimisation techniques and their integration and comparison with other methods. The third and last tutorial will consider full-scale power system application examples.
Multi-criteria or multi-objective decision-making is becoming increasingly popular as a decision support tool for natural resource management. Stakeholders as well as the planners can be involved in the decision makin...
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Multi-criteria or multi-objective decision-making is becoming increasingly popular as a decision support tool for natural resource management. Stakeholders as well as the planners can be involved in the decision making process, using this approach. This article deals with the use of multi-criteria (multi-objective) technique in solving some complex problems related to water resource management. Five objectives were considered in the study. The benefit of combining these objective functions with the decision support tool is that the management of land and water resources can be made more effectively. Based on this concept, a methodology was developed through this study, for the water managers and decision-makers, to obtain a compromising solution in terms of area allocated under different crops and the magnitude of farming system variables in a canal command area. This study was under taken in the Mahanadi Delta of India. Multi-objective techniques such as Sequential Linear fuzzy programming and Goal programming were used for their simplicity in computation and flexibility in application. Using fuzzy programming technique, the objective function values under benefit maximization, production maximization, investment minimization, labour maximization and labour minimization were found to be 44.26 M INR, 8795 tonnes, 42.00 M INR and 548 150 man-days, respectively. These results were found to be quite compromising in nature. Goal programming technique was also used to arrive at a consensus in allocation of the resources. It was used to decide the best out of the eight alternative priorities. Results indicated that only five alternative goals (Goal1, Goal2, Goal3, Goal6 and Goal8) had distinct allocations while the other three alternatives (Goal4, Goal5 and Goal7) had allocations similar to either of the above five alternatives irrespective of their priority levels. Cropping intensity was found to be the maximum (238%) for two of the goals (Goal6 and Goal7). Though the re
We present in this paper a finitary approach to the concept of fuzzy computability. On this basis we define the class FR of fin-recursive W-functions. We prove that FR is a strict subset of the class of W-functions wi...
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We present in this paper a finitary approach to the concept of fuzzy computability. On this basis we define the class FR of fin-recursive W-functions. We prove that FR is a strict subset of the class of W-functions with recursive graph, as defined by Gerla. We show that it is possible to generate the class FR by means of programs written in the language XL. (C) 2001 Elsevier Science B.V. All rights reserved.
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