A multi-criterion group decision-making method based on regret theory under 2-tuple linguistic environment

PurposeThe purpose of this paper is to develop a multi-criterion group decision-making (MCGDM) method by combining the regret theory and the Choquet integral under 2-tuple linguistic environment and apply the proposed method to deal with the supplier selection problem.Design/methodology/approachWhen making a decision, the decision-maker is more willing to choose the alternative(s) which is preferred by the experts so as to avoid the regret. At the same time, the correlative relationships among the criterion set can be sufficiently described by the fuzzy measures, later the evaluations of a group of criteria can be aggregated by means of the Choquet integral. Hence, the authors cope with the MCGDM problems by combining the regret theory and the Choquet integral, where the fuzzy measures of criteria are partly known or completely unknown and the evaluations are expressed by 2-tuples. The vertical and the horizontal regret-rejoice functions are defined at first. Then, a model aiming to determine the missing fuzzy measures is constructed. Based on which, an MCGDM method is proposed. The proposed method is applied to tackle a practical decision-making problem to verify its feasibility and the effectiveness.FindingsThe vertical and the horizontal regret-rejoice functions are defined. The relationships of the fuzzy measures are expressed by the sets. A model is built for determining the fuzzy measures. Based on which, an MCGDM method is proposed. The results show that the proposed method can solve the MCGDM problems within the context of 2-tuple, where the decision-maker avoids the regret and the criteria are correlative.Originality/valueThe paper proposes an MCGDM method by combining the regret theory and the Choquet integral, which is suitable for dealing with a variety of decision-making problems.

An Automatic Assessment Method for Spoken English Based on Multimodal Feature Fusion

This paper provides a comprehensive explanation of the theoretical foundations of multimodal discourse analysis theory as applied to speaking instructional design. The specific application of multimodal theory in the teaching of elementary English speaking classrooms is explored through the teaching design of elementary English speaking classrooms, the teaching implementation of multimodal teaching design is carried out, and the effect of the teaching practice of elementary English speaking guided by multimodal discourse analysis theory is comprehensively evaluated through classroom observation method, questionnaire survey method, and interview method, combined with the teaching evaluation and teaching implementation effect, which is the multimodal teaching design. The paper also summarizes the findings and shortcomings of the study. Through the teaching design and implementation, the advantages of multimodal teaching are obvious; it can combine with modern advanced teaching techniques to create more realistic communicative situations in the classroom, gather and present various modal resources and information, and ensure rich and diverse language input; students can receive various sensory stimuli in the classroom, deepen their memory and experience of language, increase the interest of classroom teaching, and improve students’ participation. It also increases the interest of the classroom and enhances students’ participation and motivation. Based on multimodal theory, the author designed a multimodal teaching framework for a semester-long speaking course in the speaking classroom for reference. The fuzzy measures were constructed based on subsets of language segments containing 10 phonemes belonging to the same HDP set. Finally, linguistic scores are given by the Surgeon integral model based on the plausibility of the system and the fuzzy measures. The experimental results based on Sphinx-4 show that the evaluation model yields plausible and stable evaluation results for the 3 test sets at an average correct recognition rate of 84.7% of phonemes.

Application of Choquet Integral-Fuzzy Measures for Aggregating Customers’ Satisfaction

Choquet integral is a type of aggregation operator that is commonly used to aggregate the interrelated information. Nowadays, this operator has been successfully embedded with fuzzy measures in solving various evaluation problems. Inspired from this new development, this paper aims to introduce a combined Choquet integral-fuzzy measures (CI-FM) operator that uses the Shapley value standard and interaction index to deal with the interactions between elements of information. The proposed operator takes into account not only the importance of elements or their ordered positions but also the interaction among criteria during the evaluation process. A case of customers’ satisfaction over two fast restaurants in Malaysia is presented to illustrate the application of the proposed aggregation operator. Based on three customers’ satisfaction criteria, restaurant 1 and restaurant 2 received CI-FM scores of 0.711011 and 0.704945, respectively. Interestingly, the criterion “services” constantly received the highest rating across both restaurants. In addition, the proposed aggregation operator successfully identified which restaurant is superior in the eyes of customers. Finally, this study offers some research ideas for the future.

Regional integrated energy system schemes selection based on risk expectation and Mahalanobis-Taguchi system

Regional integrated energy system (RIES) provides a platform for coupling utilization of multi-energy and makes various energy demand from client possible. The suitable RIES composition scheme will upgrade energy structure and improve integrated energy utilization efficiency. Based on a RIES construction project in Jiangsu province, this paper proposes a new multi criteria decision-making (MCDM) method for the selection of RIES schemes. Because that subjective evaluation on RIES schemes benefit under criteria has uncertainty and hesitancy, intuitionistic trapezoidal fuzzy number (ITFN) which has the better capability to model ill-known quantities is presented. In consideration of risk attitude and interdependency of criteria, a new decision model with risk coefficients, Mahalanobis-Taguchi system and Choquet integral is proposed. Firstly, the decision matrices given by experts are normalized, and then are transformed to minimum expectation matrices according to different risk coefficients. Secondly, the weights of criteria from different experts are calculated by Mahalanobis-Taguchi system. Mobius transformation coefficients based on interaction degree are to calculate 2-order additive fuzzy measures, and then the comprehensive weights of criteria are obtained by fuzzy measures and Choquet integral. Thirdly, based on group decision consensus requirement, the weights of experts are obtained by the maximum entropy and grey correlation. Fourthly, the minimum expectation matrices are aggregated by the intuitionistic trapezoidal fuzzy Bonferroni mean operator. Thus, the ranking result according to the comparison rules using the minimum expectation and the maximum expectation is obtained. Finally, an illustrative example is taken in the present study to make the proposed method comprehensible.