Extending tuple spaces with Description Logic provides a coordination model particularly suitable for open and dynamic application domains like pervasive systems, Internet applications and service-based applications. However, description logic per se is not powerful enough to represent vague/fuzzy knowledge so often required by real-world application scenarios. Hence, in this paper we extend the model of Description Logic tuple spaces ( Description Spaces , or DS, in short) with fuzziness in order to support fuzzy semantic coordination.
Why is it so plausible that business organisations in contemporary society use values in their communication? In order to answer this question, a sociological, system theoretical approach is applied which approaches values not pre-empirically as invisible drivers for action but as observable semantics that form organisational behaviour. In terms of empirical material, it will be shown that business organisations resort to a communication of values whenever uncertainty or complexity is very high. Inevitably, value semantics are applied in organisations first when the speakers are uncertain about which stakeholders to whom they have to address (uncertainty) or when different stakeholder groups have to be addressed simultaneously (complexity); second, when the identity of the organisation has to be described; and third, when future strategic options that cannot be expressed by quantitative terms have to be communicated. Values accordingly play a role in organisational practice when certain aspects are indeterminate. Therefore, they are a means for organisations to communicate under fuzzy circumstances. On the basis of these findings, new approaches to value management can now be formulated.
The railway freight transportation planning problem under the mixed uncertain environment of fuzziness and randomness is investigated in this paper, in which the optimal paths, the amount of commodities passing through each path and the frequency of services need to be determined. Based on the chance measure and critical values of the random fuzzy variable, three chance-constrained programming models are constructed for the problem with respect to different criteria. Some equivalents of objectives and constraints are also discussed in order to investigate mathematical properties of the models. To solve the models, a potential path searching algorithm, simulation algorithms and a genetic algorithm are integrated as a hybrid algorithm to solve an optimal solution. Finally, some numerical examples are performed to show the applications of the models and the algorithm.
Image enhancement is usually necessary to support the human visual perception. The results of enhancement, however, do not satisfy the demands of the observers in many situations. To find a solution, one may find a new cognitive framework for image understanding at first that enable us to explain the human subjectivity in a sophisticated way. Image enhancement is the processing of images to increase their usefulness. Methods and objectives vary with the application. There are mainly two methods for image-enhancement: one deals with images in the spatial domain the other one deals with images in the frequency domain. The first method is based on the processing of individual pixels in an image; the second is based on modifying the Fourier transform of an image. A new approach theory of Fuzzy set has been used to deal with image enhancement problems.
► We present a new method for fuzzy rule interpolation for sparse fuzzy rule-based systems based on the ratio of fuzziness of interval type-2 fuzzy sets. ► It calculates the weights of the closest fuzzy rules with respect to the observation to obtain an intermediate consequence fuzzy set. ► It uses the ratio of fuzziness of interval type-2 fuzzy sets to infer the fuzzy interpolated result based on the intermediate consequence fuzzy set. ► The proposed fuzzy rule interpolation method gets more reasonable results. In recent years, some fuzzy rule interpolation methods have been presented for sparse fuzzy rule-based systems based on interval type-2 fuzzy sets. However, the existing methods have the drawbacks that they cannot guarantee the convexity of the fuzzy interpolated result and may generate the same fuzzy interpolated results with respect to different observations. Moreover, they also cannot deal with fuzzy rule interpolation with bell-shaped interval type-2 fuzzy sets. In this paper, we present a new method for fuzzy rule interpolation for sparse fuzzy rule-based systems based on the ratio of fuzziness of interval type-2 fuzzy sets. The proposed method can overcome the drawbacks of the existing methods. First, it calculates the weights of the closest fuzzy rules with respect to the observation to obtain an intermediate consequence fuzzy set. Then, it uses the ratio of fuzziness of interval type-2 fuzzy sets to infer the fuzzy interpolated result based on the intermediate consequence fuzzy set. We also use some examples to compare the fuzzy interpolated results of the proposed method with the results by the existing methods. The experimental results show that the proposed fuzzy rule interpolation method gets more reasonable results than the existing methods.
In logistics system, facility location–allocation problem, which can be used to determine the mode, the structure and the form of the whole logistics system, is a very important decision problem in the logistics network. It involves locating plants and distribution centers, and determining the best strategy for allocation the product from the plants to the distribution centers and from the distribution centers to the customers. Often uncertainty may be associated with demand, supply or various relevant costs. In many cases, randomness and fuzziness simultaneously appear in a system, in order to describe this phenomenon; we introduce the concept of hybrid variable and propose a mixed-integer programming model for random fuzzy facility location–allocation problem. By expected value and chance constraint programming technique, this model is reduced to a deterministic model. Furthermore, a priority-based genetic algorithm is designed for solving the proposed programming model and the efficacy and the efficiency of this method and algorithm are demonstrated by a numerical example. Till now, few has formulated or attacked the FLA problems in the above manner. Furthermore, the techniques illustrated in this paper can easily be applied to other SCN problems. Therefore, these techniques are the appropriate tools to tackle other supply chain network problems in realistic environments.