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)

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

A neutrosophic enhanced best–worst method for considering decision-makers’ confidence in the best and worst criteria

The best–worst method (BWM) is a multiple criteria decision-making (MCDM) method for evaluating ≤a set of alternatives based on a set of decision criteria where two vectors of pairwise comparisons are used to calculate the importance weight of decision criteria. The BWM is an efficient and mathematically sound method used to solve a wide range of MCDM problems by reducing the number of pairwise comparisons and identifying the inconsistencies derived from the comparison process. In spite of its simplicity and efficiency, the BWM does not consider the decision-makers’ (DMs’) confidence in their pairwise comparisons. We propose a neutrosophic enhancement to the original BWM by introducing two new parameters as the DMs’ confidence in the best-to-others preferences and the DMs’ confidence in the others-to-worst preferences. We present two real-world cases to illustrate the applicability of the proposed neutrosophic enhanced BWM (NE-BWM) by considering confidence rating levels of the DMs

Energy-saving building program evaluation with an integrated method under linguistic environment

In the context of sustainable development, building energy conservation has become the development trend of the construction industry. The selection of energy-saving building program, as a multi-criteria decision-making (MCDM) problem, has a direct influence on the actual energy-saving effect. In this paper, an integrated MCDM method combining the extended best worst method (BWM) and Weighted Aggregated Sum Product Assessment (WASPAS) method is proposed to solve the energy-saving building program selection problem under the linguistic Pythagorean fuzzy environment. The Linguistic Pythagorean fuzzy sets (LPFSs) are used to model the uncertain evaluation information of experts. The extended BWM is developed to determine the weights of criteria, while the extended WASPAS method is proposed to determine the ranking of alternatives. To validate the applicability and reliability of the proposed method, this paper presents a numerical example of the selection problem for energy-saving building programs. Some managerial insights are also given for practitioners to use the proposed method

Decision analysis in the UK energy supply chain risk management: tools development and application.

The aims of this thesis are developing decision-making tools for risk identification, risk causal relationships analysis, risk prioritisation, and long-term risk mitigation strategy recommendations in the UK energy supply chain. The thesis is comprised of four study phases in eight chapters. In phase I, a framework is introduced including 12 risk dimensions, and 5 classification perspectives. Then, in phase II, the Neutrosophic Revised Decision-Making Trial and Evaluation Laboratory (NR-DEMATEL) method has been utilised in order to analyse the 12 identified risk dimensions based on the causal interrelationships between them. Additionally, a novel Hesitant Expert Selection Model (HESM) to systematically assist researchers with the expert selection process is proposed. In phase III, two extensions of the original Best-Worst Method (BWM) are proposed in order to contribute to the theoretical development and application of the BWM in energy supply chain risk prioritisation. The Neutrosophic Enhanced BWM (NE-BWM) and hybrid Spanning Trees Enumeration and BWM (STE-BWM) are introduced to enhance the efficiency of the original BWM in dealing with uncertainty in experts’ subjective judgements. In phase IV, a novel stratified decision-making model is introduced. It is based on Concept of Stratification (CST), game theory and Shared Socio-economic Pathway (SSP) to deal with long-term risk mitigation planning for the most critical identified risks. The model has been applied in the region of Highland and Argyll in Scotland based on the primary data obtained from experts to prioritise flooding risk mitigation strategies which were recommended by the Scottish Environment Protection Agency (SEPA). The stratified decision-making model is aimed at taking into account both UK socio-economic situations and flooding risk impacts for the long-term decision making