Hospitality brand management by a score-based q-rung ortho pair fuzzy V.I.K.O.R. method integrated with the best worst method

Hospitality brand management is a primary concern in the hotel industry and the evaluation of brands can be considered as a decision-making problem with multiple criteria. The evaluation information of brands may be uncertain sometimes. The q-rung orthopair fuzzy set (q-R.O.F.S.), which represents the preference degree of a person from the positive and negative aspects, has turned out to be an efficient tool in depicting uncertainty and vagueness in the decision-making process. This article dedicates to presenting an integrated multiple criteria decision-making method with q-R.O.F.S.. Firstly, a score function of the q-R.O.F.S. is proposed to solve the deficiencies of two existing score functions. Then, a weight-determining method based on the additive consistency of the preference relation is developed. A decision-making method integrating the score function, the best worst method and the VIsekriterijumska optimizacija I KOmpromisno Resenje (V.I.K.O.R.) which means multiple criteria compromise optimisation in English) method is further proposed. Finally, a case study regarding the hospitality brand management is provided to show the applicability and validity of the proposed method

Hospitality brand management by a score-based q-rung orthopair fuzzy V.I.K.O.R. method integrated with the best worst method

Hospitality brand management is a primary concern in the hotel industry and the evaluation of brands can be considered as a decision- making problem with multiple criteria. The evaluation information of brands may be uncertain sometimes. The q-rung orthopair fuzzy set (q-R.O.F.S.), which represents the preference degree of a person from the positive and negative aspects, has turned out to be an efficient tool in depicting uncertainty and vagueness in the decision-making process. This article dedicates to presenting an integrated multiple criteria decision-making method with q-R.O.F.S.. Firstly, a score function of the q-R.O.F.S. is proposed to solve the deficiencies of two existing score functions. Then, a weight-determining method based on the additive consistency of the preference relation is developed. A decision-making method integrating the score function, the best worst method and the VIsekriterijumska optimizacija I KOmpromisno Resenje (V.I.K.O.R.) which means multiple criteria compromise optimisation in English) method is further proposed. Finally, a case study regarding the hospitality brand management is provided to show the applicability and validity of the proposed method.The work was supported by the National Natural Science Foundation of China (71771156, 71971145), the Scholarship from China Scholarship Council (No. 201906240161) and the Deanship of Scientific Research (DSR) at King Abdulaziz University, Jeddah (No. RG-10-611- 39, No. RG-7-135-38)

Integrating BWM and ARAS under hesitant linguistic environment for digital supply chain finance supplier section

In the era of intelligence and informatization, digital supply chain finance (DSCF) has become one of the important trends in the development of supply chain finance. With the gradual increase of DSCF suppliers and various requirements of small and medium-sized enterprises for suppliers in providing financing services, selecting the most suitable DSCF supplier is of great significance for most small and medium-sized enterprises to expand reproduction and improve competitiveness. To address such a decision-making problem, this paper proposes a new multi-expert multiple criteria decision-making model by integrating the Best Worst Method (BWM) and Additive Ratio ASsessment (ARAS) method under the hesitant fuzzy linguistic environment, in which the hesitant fuzzy linguistic BWM method is applied to determine the weights of criteria while the hesitant fuzzy linguistic ARAS method is proposed to rank the candidate suppliers. A case study is given to demonstrate the procedure of the proposed method for the selection of optimal DSCF suppliers, which shows the feasibility of the proposed method. Finally, sensitivity analysis and comparative analyses are provided to testify the applicability and superiority of the proposed method