Multi-granularity Linguistic Assessment Information Research Articles

The consistent fuzzy suitability assessment of forest land resources with multi-source heterogeneous data.

In view of the suitability assessment of forest land resources, a consistent fuzzy assessment method with heterogeneous information is proposed. Firstly, some formulas for transforming large-scale real data and interval data into fuzzy numbers are provided. To derive the unified representation of multi-granularity linguistic assessment information, a fuzzy quantitative transformation for multi-granularity uncertain linguistic information is proposed. The proofs of the desirable properties and some normalized formulas for the trapezoidal fuzzy numbers are presented simultaneously. Next, the objective weight of each assessment indicator is further determined by calculating the Jaccard-Cosine similarity between the trapezoidal fuzzy numbers. Moreover, the trapezoidal fuzzy numbers corresponding to the comprehensive assessment values of each alternative are obtained. The alternatives are effectively ranked according to the distance from the centroid of the trapezoidal fuzzy number to the origin. Finally, based on the proposed consistent fuzzy assessment method, the suitability assessment of forest land resources is achieved under a multi-source heterogeneous data setting.

Fuzzy TOPSIS-EW Method with Multi-Granularity Linguistic Assessment Information for Emergency Logistics Performance Evaluation

The operation of emergency logistics plays a prominent role in reducing the consequences of disasters. Based on the establishment of a comprehensive evaluation system with the whole period of the disaster cycle that covers emergency preparation, response, and recovery, this paper proposes a fuzzy-symmetrical Technique for Order Preference by Similarity to Ideal Solution-Entropy Weight (TOPSIS-EW) method with multi-granularity linguistic assessment (MGLA) information to evaluate the performance of emergency logistics. Furthermore, the proposed evaluation method is employed to evaluate the performance of emergency logistics in Wenchuan earthquake, five worst-hit regions (i.e., Wenchuan County, Beichuan County, Qingchuan County, Mianzhu City, Shifang City) were ranked as V, III, I, II, IV, respectively. Finally, the effectiveness and reliability of the method are verified by comparison with the other two related methods and a sensitivity analysis. Based on the comprehensive evaluation results, some specific managerial suggestions are proposed to improve the emergency logistics capacity.

An approach to emergency decision making based on decision-theoretic rough set over two universes

Emergency decision making is still an important issue in unconventional emergency management research. Although many studies have been written on this topic, they remain political and qualitative, and it is difficult to make them operational in practice. Therefore, this article proposes a decision-theoretic rough set over two universes as an approach for solving this difficulty. The proposed approach integrates rough set theory over two universes using a Bayesian decision-making technique. In this study, emergency decision making is considered as a multiple-criteria ranking or multiple-criteria selection problem with multi-granularity linguistic assessment information. A Bayesian decision process based on linguistic description with qualitative data over two universes is first presented to construct the decision model and approach, and then the decision-theoretic rough set theory over two universes is taken to induce a set of decision rules that satisfy minimum risk of loss conditions. These rules can easily give the optimal decision results with minimum risk of loss by considering online information, realistic scenarios, and the dynamic characteristic of unconventional emergency events as they develop. Finally, the steps and the basic principle of the proposed method are illustrated by a numerical example with the background of emergency decision making.

Uncertain Linguistic Multi-Attribute Group Decision Making Method with Multi-Granularity Based on Projection and Dominance Degree

In order to solve multiple attribute group decision making problems with multi-granularity uncertain linguistic assessment information, where the information of attribute weights are incomplete, a group decision making method based on projection and dominance degree is proposed. Firstly, the uncertain linguistic evaluation matrices with multi-granularity are transformed to two-tuple decision matrices according to the continuous interval two-tuple ordered weighted harmonic (ITC-OWH) operator. By using projection method, a goal programming model is set up to determine the attribute weights vector of individual decision maker. Then, integrative attribute dominance degree between pairwise comparisons of arbitrary alternatives are computed, a group integrative dominance degree matrix is obtained based on integrative dominance degree matrices and expert weight vector. The priority-time matrix is given to calculate the general priority time of alternatives, consequently, the rank result of alternatives can be determined.

A fuzzy multi-granularity linguistic approach under group decision-making for the evaluation of supply chain flexibility

With global competition rapidly intensifying and shifting to the supply chain level, supply chain flexibility (SCF) is becoming increasingly important. Despite its importance, the literature on SCF is limited. In group decision-making problems, the experts have their different backgrounds, experience, and knowledge. The experts use different linguistic term sets with different cardinalities (multi-granularity linguistic term sets) to express individual assessment information. Therefore, the objective of this paper is to develop a new fuzzy fusion method to deal with the heterogeneous group of experts’ problem with multi-granularity linguistic assessment information, and used to improve the SCF evaluation process. The results of this study are more objective and unbiased for two reasons. First, the results are generated by heterogeneous group decision-making with multi-granularity linguistic assessment information. Second, the fuzzy linguistic approach has more advantage to preserve no loss of information. Additionally, an example using a case study is used to illustrate the availability of the proposed method. We also show that the linguistic assessment of SCF is reasonable.

Multi-attribute group decision-making with multi-granularity linguistic assessment information: An improved approach based on deviation and TOPSIS

With respect to group decision-making problems with multi-granularity linguistic assessment information, a new approach is proposed. Firstly, the computational formulae are given in order to transform and unify the multi-granularity linguistic comparison matrices. Secondly, the method of standard and mean deviation is applied to determine the unknown attribute weights, and the weights of the decision makers will be determined by using the extended TOPSIS (technique for order preference by similarity to an ideal solution) method. Finally, based on the LWAA (linguistic weighted arithmetic averaging) operator, information on the preference provided by each decision maker is aggregated into the comprehensive evaluation value of each alternative, and the most desirable alternative is selected. The proposed approach expands the research in multi-attribute group decision-making with multi-granularity linguistic assessment information by both considering the weights of the attributes and decision makers, and objective weighting for them. A numerical example is given to illustrate the practicability and usefulness of the proposed approach.

A rough set approach to knowledge discovery in analyzing competitive advantages of firms

Competitive advantage analysis (CAA) is still an important issue of strategic management research. Although many studies are developed on this topic, they remain conceptual and descriptive, and it is difficult to make them operational in practice. Therefore, this article proposes an intelligent decision support approach for solving such a difficulty. The proposed approach integrates soft computing, rough set theory, and group decision making technique. In this study, CAA is considered as a multiple criteria sorting problem with multi-granularity linguistic assessment information. An algorithm based on linguistic computing is first presented to construct the decision table of exemplary decisions, and then the extended rough set theory and dominance functions are taken to induce a set of decision rules that satisfy a minimum support threshold. These rules can explicitly describe the relationship between the competitive advantage positions and the key determinant factors of competitive advantage. Finally, a numerical example is used to illustrate the application of the proposed approach.

A method for group decision making with multi-granularity linguistic assessment information

This paper proposes a method to solve the group decision making (GDM) problems with multi-granularity linguistic assessment information. In the method, the multi-granularity linguistic information provided by experts is firstly expressed in the form of fuzzy numbers. In order to make the collective opinion close to each expert’s opinion, a linear goal programming model is constructed to integrate the fuzzy assessment information and to directly compute the collective ranking values of alternatives without the need of information transformation. Then, a fuzzy preference relation on the pairwise comparisons of the collective ranking values of alternatives is constructed using the dominance possibility degree of the comparison between the fuzzy numbers. By applying a non-dominance choice degree to this fuzzy preference relation, the ranking of alternatives is determined and the most desirable alternative(s) is selected. An example is used to illustrate the applicability of the proposed method and its advantages.