Ranking Sustainable Projects through an Innovative Hybrid DEMATEL-VIKOR Decision-Making Approach Using Z-Number

According to the heavy reliance of economic growth on environmental and social matters, sustainable development has turned into one of the main strategies of the organizations associated with selecting the project basket. Moreover, market conditions, rapid global changes in several aspects, uncertainty in intellectual judgments of the experts, and many other factors have increased uncertainty in problems of this kind. Accordingly, the main objective of this paper is to develop a mixed decision-making approach (DEMATEL-VIKOR) with the aim of ranking and evaluating suggested projects considering sustainability indices in a “mass production and infrastructural” company under uncertainty, while also taking reliability (Z-number information) into account. Given the existing uncertainty in the opinions of experts and previous data, fuzzy approach and Z-number information have been employed for weighting the filtered sustainability indices that are aligned with the objectives of the considered company (Z-DEMATEL), as well as ranking candidate projects (Z-VIKOR), which contribute to the main innovation of the present research. In addition, for scoring in the Z-number state, a novel definition of linguistic variable (fuzzy linguistic variable considering probabilities) has been put forward. The suggested approach was solved by an expert in two numerical examples in three certain, fuzzy, and Z-number scenarios, the results of which show that different results are obtained for different scenarios. Apart from that, given that Z-number takes the probability of reliance on the opinions of experts into consideration, the more the approach moves from being certain towards the fuzzy state and the Z-number state, the more the results approach the real world, hence yielding more reliable results. Therefore, results obtained from the proposed Z-DEMATEL-VIKOR approach are far more reliable. Moreover, significance weights in the Z-number scenario are 39%, 26%, and 36% in the economic, social, and environmental dimensions, respectively, which is indicative of the importance of social and environmental aspects apart from the economic dimension.

Interval probability hesitant fuzzy linguistic analytic hierarchy process and its application in talent selection

Hesitant fuzzy linguistic term set (HFLTS) can handle the qualitative and hesitant information in multiple attribute decision making (MADM) problems which are widely used in various fields. However, the experts’ evaluation of information is not completely reliable in the situation where their own knowledge background is insufficient. In order to deal with deviations due to incomplete reliability of the evaluation, this paper first proposes the interval probability hesitant fuzzy linguistic variable (IPHFLV), which takes the HFLTS as the evaluation part and adds a novel element-reliability of evaluation, thus can describe the different credibility of information evaluation due to the familiarity of experts with schemes and the differences in knowledge cognition. The operation rules and comparison methods are also illustrated. Particularly, under the inspiration of probability theory, we propose the possibility degree of the IPHFLVs. Then we propose IPHFL-AHP based on the AHP and interval probability hesitant fuzzy linguistic variable. Especially, the general geometric consistency index (G-GCI) based on the unbiased estimator of the variance is presented to measure the consistency and the iterative algorithm is constructed to improve the consistency. We use the possibility degree to calculate the priority vector to acquire the total ranking and introduce the process of IPHFL-AHP. Finally, case study of talent selection is given to illustrate the effectiveness and feasibility of the proposed method.

Fuzzy Integrated Ontology Model of Dynamic Learner Profiling

In the context of lifelong learning, learner profile has emerged as a feasible model that support and promote the provision of lifelong learning opportunities. Learner profile describes the attributes and outcomes of education in a learning system. It includes information on learner’s gender, skills, education, interest, learning preferences, learning style, etc. This paper proposes an approach to construct a fuzzy based semantic learner profile in the promising technology of semantic web by using the concept of ontology and use it for the reasoning of learner preferences. The approach starts with the collection of learner’s static and dynamic data. The dynamic data of learner particularly learner interest and learning style are extracted by weblog analysis and using algorithms such as semantic based representation using WordNet and modified decision tree classifier with strong rules based on Felder-Silverman learning style model. The retrieved data is then used to construct learner profile using ontology in which automatic learner profile updating is obtained using ontology based semantic similarity algorithm. Finally to achieve semantic retrieval from learner profile ontology, fuzzy concepts such as fuzzy linguistic variable and fuzzy IF THEN rules are applied. Fuzzy linguistic variable facilitate semantic retrieval and more specific classification from learner profile ontology and fuzzy IF THEN rules predict the learning preference of new students based on the forward chaining reasoning process implemented in the existing ontology model. The final representation of semantic fuzzy ontology based learner profile improves the performance of tasks such as classification, semantic retrieval and prediction of learning preference to the new learners. The case study is conducted for the real-time learners involved in studying the courses registered in Moodle Learning Management System. The experiments were performed with NetBeans IDE, Jena framework and Protégé 4.2 beta editor. The experiments confirm that the proposed learner profile is a good representation of the learner's preferences.

DESCRIPTION AND IDENTIFICATION OF TEMPORAL REGULARITIES FOR FUZZY TIME SERIES WITH APPLICATION OF HYBRID OLS-PATTERNS

ObjectivesThe aim of the research is to develop the principle of storing data templates to take their temporal natureinto account, making it possible to reduce decision-making times.In order to describe and identify temporal patterns in fuzzy time series behaviour in real time, the task was set to develop a hybrid data structure that allows for a consideration of sequences of fuzzy values formed from clear observable data as well as a determination of the length of these sequences and possible uneven time intervals between the observations.MethodsThe article discussesan approach to formalising the description of temporal cause-effect relationships between events occurring at the object location as well as that of its environment, based on a set of singly-connected lists of triplets. Each triplet contains a fuzzy linguistic variable, the duration of its observation and the permitted interval of observation of insignificant data.ResultsAn algorithm for detecting knowledge base patterns in real time was developed, taking into account the possibility of a time shift in observing long sequences of identical values of the observed value. The possibility of partial data overlapping corresponding to triplets of different patterns is taken into account. The proposed hybrid pattern makes it possible to accelerate the detection of temporal regularities in the data.ConclusionScientific results are presented by the developed structure for storing information on temporal regularities in data, based on a singly linked linear list, as well as an algorithm for finding regularities in observational data using a set of OLS-patterns. The advantage of this structure and algorithm in comparison with the known ways of storing and analysing temporal data is a reduction in the amount of memory necessary for storing templates in the knowledge base, as well as the possibility of applying OLS patterns for decisionmaking purposes.

Fuzzy Knowledge Representation Based on Fuzzy Linguistic Variable Ontology and SWRL on the Semantic Web

Ontology is adopted as a standard for knowledge representation on the Semantic Web, and Ontology Web Language (OWL) is used to add structure and meaning to web applications. In order to share and resue the fuzzy knowledge on the Semantic Web, we propose the fuzzy linguistic variables ontology (FLVO), which utilizes ontology to represent formally the fuzzy linguistic variables and defines the semantic relationships between fuzzy concepts. Then fuzzy rules are described in Semantic Web Rule Language (SWRL) on the basis of FLVO model. Taking a sample case for students’ performance in physics for example, the fuzzy rule management system is built by using the tool protégé and SWRLTab, which shows that this research enables distributed fuzzy applications on the Semantic Web.

Risk Assessment Model with 2-Tuple Temporal Linguistic Variable

The paper aims to develop a risk assessment model with the fuzzy temporal information. The traditional model assess risk with the risk matrix method. And the method rank the risks without regard to the with the temporal information to assess the risk. The model integrates the method of 2-tuple linguistic and temporal linguistic variable. The improved concept defines the transition symbols operator as the projection of temporal term on the fuzzy linguistic variable. The new model may deal with the temporal information in the fuzzy linguistic judgements. The emprical research give a example by applying the new method. The result of example show that the new model can provide the worthwhile temporal information in the assessment result. temporal element temporal element temporal element temporal element

Knowledge Modeling of Product Data Based on Fuzzy Ontology

Ontology is the basis of knowledge modeling on the Semantic Web, and fuzzy ontology is an extension of domain ontology for solving the uncertainty problems. Ontology-base knowledge modeling of product data management (PDM) is meaningful for product design and trade etc. In order to handle fuzzy phenomenon and uncertainty of product knowledge, this paper proposes a series of fuzzy ontology models that consists of fuzzy domain ontology and fuzzy linguistic variable ontologies. Then, a fuzzy ontology framework is presented, including three parts: concepts, properties of concepts and values of properties. The application, which uses fuzzy ontology to model product knowledge, shows that these models can overcome the localization of other fuzzy ontology models, and this research facilitates the fuzzy knowledge sharing and reuse for PDM on the Semantic Web.