A problem of task allocation with fuzzy information and two-stage solution algorithm

The problem of high-performance allocation of tasks between executives, where the competence of each for implementation of tasks is specified in the form of fuzzy relations, is considered. Maximization of the aggregated degree of competence of the entire allocation and maximization of the degree of the overall level of employment of standard executives are the optimization criteria. Aggregation is performed by means of the Hurwicz operator and the Ordered Weighted Average (OWA) operator. A two-stage heuristic algorithm is proposed for the solution of the problem. An analysis of different algorithms and assessment of the results of computational experiments is conducted.     

A new approach for fuzzy information retrieval based on weighted power-mean averaging operators

An information retrieval system can help users to retrieve documents relevant to the users’ queries. In recent years, some researchers used averaging operators (i.e., Infinite–One operators, Waller–Kraft operators, P-Norm operators and GMA operators) to handle “AND” and “OR” operations of users’ fuzzy queries for fuzzy information retrieval, but they still have some drawbacks, e.g., sometimes query results do not coincide with the intuition of the human being. In this paper, we present new averaging operators, called weighted power-mean averaging (WPMA) operators, based on the weighted power mean for dealing with fuzzy information retrieval to overcome the drawbacks of the existing methods. Furthermore, we also extend the proposed WPMA operators into the extended WPMA operators to handle weighted fuzzy queries for fuzzy information retrieval. The proposed WPMA operators are more flexible and more intelligent than the existing averaging operators to handle users’ fuzzy queries for fuzzy information retrieval.     

Exploiting syntactic analysis of queries for information retrieval

Up to now, the results of applying sophisticated NL techniques to information retrieval (IR) have been mostly disappointing. Our research aims at investigating in detail the role of syntactic analysis in IR and at finding answers to the question why it works better for some queries and worse for others. The final goal is a hybrid algorithm that selectively applies syntactic analysis to certain classes of queries while relying on standard statistical techniques otherwise.     

Linguistic group decision making with induced aggregation operators and probabilistic information

A new approach for linguistic group decision making by using probabilistic information and induced aggregation operators is presented. It is based on the induced linguistic probabilistic ordered weighted average (ILPOWA). It is an aggregation operator that uses probabilities and OWA operators in the same formulation considering the degree of importance that each concept has in the formulation. It uses complex attitudinal characters that can be assessed by using order inducing variables. Furthermore, it deals with an uncertain environment where the information cannot be studied in a numerical scale but it is possible to use linguistic variables. Several extensions to this approach are presented by using moving averages and Bonferroni means. The applicability of this approach is also studied with a focus on multi-criteria group decision making by using multi-person aggregation operators in order to deal with the opinion of several experts in the analysis. An illustrative example regarding importation strategies in the administration of a country is developed.     

Importance in knowledge systems

In knowledge systems, pieces of information (evidence, hypotheses, attributes, terms, documents, rules) are usually assumed to carry equal importance and to be independent of each other, although it might not actually be the case. Issues for a logic of weighted queries, with possibility of also weighting documents and logical connectors (in terms of intelligent retrieval, for example) are presented here, using “min” or t-norms, and soft operators involving p-norms. This logic cannot be a conventional one for, when introducing relative importance between concepts, definitions are different for ANDed and ORed weighted queries. A concept of “nought”, a limit case of no-importance queries, and its behaviour with fuzzy sets operations is developed, in particular the notion of an extended membership is introduced. Finally it is shown, with a biomedical example, how to combine importance with soft matching in rule-based systems.     

信息检索与过滤中的信息需求表示方法

信息需求的表示方法是影响信息检索和信息过滤结果的重要因素。介绍了一些基本的信息需求表示方法,并对各种表示方法对信息检索和信息过滤结果产生的影响进行了分析和比较,最后提出了改进这些方法的一些思想。     

Choquet integrals of weighted intuitionistic fuzzy information

The Choquet integral is a very useful way of measuring the expected utility of an uncertain event [G. Choquet, Theory of capacities, Annales de l’institut Fourier 5 (1953) 131-295]. In this paper, we use the Choquet integral to propose some intuitionistic fuzzy aggregation operators. The operators not only consider the importance of the elements or their ordered positions, but also can reflect the correlations among the elements or their ordered positions. It is worth pointing out that most of the existing intuitionistic fuzzy aggregation operators are special cases of our operators. Moreover, we propose the interval-valued intuitionistic fuzzy correlated averaging operator and the interval-valued intuitionistic fuzzy correlated geometric operator to aggregate interval-valued intuitionistic fuzzy information, and apply them to a practical decision-making problem involving the prioritization of information technology improvement projects.     

Cloud decision model for selecting sustainable energy crop based on linguistic intuitionistic information

In recent years, sustainable energy crop has become an important energy development strategy topic in many countries. Selecting the most sustainable energy crop is a significant problem that must be addressed during any biofuel production process. The focus of this study is the development of an innovative multi-criteria decision-making (MCDM) method to handle sustainable energy crop selection problems. Given that various uncertain data are encountered in the evaluation of sustainable energy crops, linguistic intuitionistic fuzzy numbers (LIFNs) are introduced to present the information necessary to the evaluation process. Processing qualitative concepts requires the effective support of reliable tools; then, a cloud model can be used to deal with linguistic intuitionistic information. First, LIFNs are converted and a novel concept of linguistic intuitionistic cloud (LIC) is proposed. The operations, score function and similarity measurement of the LICs are defined. Subsequently, the linguistic intuitionistic cloud density-prioritised weighted Heronian mean operator is developed, which served as the basis for the construction of an applicable MCDM model for sustainable energy crop selection. Finally, an illustrative example is provided to demonstrate the proposed method, and its feasibility and validity are further verified by comparing it with other existing methods.