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The purpose of this paper is to evaluate the hotel selection problem of airlines for their hotel and airline alliance (HAA) to develop potential customers of airlines. This paper will propose a hybrid mathematics evaluation model to help airline to select an optimal hotel with both qualitative and quantitative criteria.

To solve the hotel selection problem of airlines for their HAA, this paper focuses on the implementation of the NGT, Fuzzy TOPSIS and MCGP models in the hotel selection process. Initially, the NGT was used to create HAA decision-making criteria based on the literature review and expert opinions, and it was found that scale and scope possibility, brand value, tourism attraction, operating cost and industrial conditions are the most important criteria. Later, the Fuzzy TOPSIS method was used to obtain the general normalized fuzzy preference and to compute the closeness coefficients of each alternative hotel with respect to each criterion. Third, five tangible constraints were incorporated into the Fuzzy TOPSIS-MCGP model to calculate the optimal hotel with LINGO software.

Airline managers can use the proposed model to form a clear view of how to choose the most suitable hotel to cooperate with to outperform their competitors. Having access to this information allows airline managers to take steps to perform better and improve the performance of the partnership, helping them to gain more confidence in their decision-making capabilities while reducing investment risks.

This is the first paper that has adopted Fuzzy TOPSIS-MCGP to select hotel for their HAA from the airline’s point of view. The major contributions of this study are as follows: an efficient and simple evaluation framework is proposed for handling vagueness and uncertainty in real-world hotel selection problems; the advancement of treating uncertainty in the MCDM process; the fuzzy TOPSIS-MCGP method is extended for such problems, taking into account tangible and intangible criteria; airline managers can now make decisions in choosing to select the best hotel for their HAA that meets the airline's business goals and passenger demands; hotel operators are flexible in selecting their airline partnership, thus creating greater profit for both parties.

饭店和航空公司之间横向联盟的选择模型：NGT, fuzzy TOPSIS和MCGP方法的综合应用

本文的主要目的是在协助驾驶评估酒店选择的问题, 并通过由酒店与航空公司的合作联盟HAA, 开发整合的潜在客户。评估模型, 以帮助航空公司选择同时满足定性和定量标准的最佳酒店。

这些研究集中在规模和范围的可能性, 品牌价值, 旅游吸引上力, 运营成本和产业条件上。第二个步骤是日期近似近似最佳解排序技术（fuzzy TOPSIS）计算每家替代酒店与理想解决方案的接近度系数。规划方法（MCGP）选择最佳酒店, 同时选择方法同时考虑酒店的定性和定量标准, 并且从未在酒店选择文献中被采用过。

为了帮助评估评估合适的酒店和建立合作联盟, 因此本文提出了NGT-Fuzzy TOPSIS-MCGP模型, 以帮助决策的决策者实现替代的酒店。在此模型中, 决策者通过最后, 在名目人群技术（NGT）确定客观的酒店选择规范, 然后他们可以根据模糊近似最佳解排序技术（TOPSIS）确定标准权重, 并计算模糊的TOPSIS-MCGP模型中, 决策制定者可以使用多选择目标规划（MCGP）, 通过设定每个目标的期望水准寻找最佳酒店。

这是第一篇以航空的角度, 同时采用模糊TOPSIS-MCGP方法选择合适的酒店的论文。本文最主要的贡献是: 1. 提出了一种有效而简单的评估框架, 用于处理现实世界中酒店选择问题中的模糊性和不确定性。2. 在处理MCDM过程中不确定性方面的进展；模糊TOPSIS-MCGP方法针对此类问题进行了扩展, 同时考虑了有形和无形的标准。3. 航空公司经理现在可以做出决定, 选择适合其HAA的最佳酒店, 借以满足航空公司的业务目标以及乘客的需要。4. 酒店运营商可以灵活选择航空公司合作伙伴关系, 从而为双方创造更大的利润。

饭店, 航空公司, 名目人群技术（NGT）, 最佳解排序技术（TOPSIS）, 多选择目标规划（MCGP）, 横向联盟

El modelo de selección Para alianzas horizontales entre hoteles y aerolíneas (Haa): una aplicación integrada de los métodos NGT, fuzzy TOPSIS y MCGP

El objetivo principal de este documento es evaluar el problema de selección de hoteles de las aerolíneas para su HAA (hotel airline alliance) a fin de desarrollar clientes potenciales para las aerolíneas. Este documento propondrá un modelo híbrido de evaluación matemática para ayudar a la aerolínea a seleccionar un hotel óptimo con criterios cualitativos y cuantitativos.

Para resolver el problema de selección de hoteles de las aerolíneas para su HAA, este documento se centra en la implementación de los modelos NGT, Fuzzy TOPSIS y MCGP en el proceso de selección de hoteles. Inicialmente, el NGT se utilizó para crear criterios de toma de decisiones de HAA basados en la revisión de la literatura y las opiniones de expertos, y se descubrió que la escala y la posibilidad de elección, el valor de la marca, la atracción turística, los costes operativos y las condiciones industriales son los criterios más importantes. Posteriormente, se utilizó el método Fuzzy TOPSIS para obtener la preferencia fuzzy general y normalizada y calcular los coeficientes de cercanía de cada hotel alternativo con respecto a cada criterio. En tercer lugar, se incorporaron cinco restricciones tangibles al modelo Fuzzy TOPSIS-MCGP para calcular el hotel óptimo con el software LINGO.

Los gerentes de aerolíneas pueden usar el modelo propuesto para tener una visión clara de cómo elegir el hotel más adecuado para colaborar con el fin de superar a sus competidores. Tener acceso a esta información permite a los gerentes de las aerolíneas tomar medidas para gestionar mejor y mejorar el resultado de la alianza, lo que les ayuda a ganar más confianza en su capacidad de toma de decisiones y al mismo tiempo reducir los riesgos de inversión.

Este es el primer documento que adopta el modelo Fuzzy TOPSIS-MCGP para seleccionar un hotel para su HAA desde el punto de vista de la aerolínea. Las principales contribuciones de este estudio son las siguientes: 1. Se propone un marco de evaluación eficiente y simple para manejar la imprecisión y la incertidumbre en los problemas de selección de hoteles del mundo real. 2. El avance del tratamiento de la incertidumbre en el proceso MCDM; extiende el método fuzzy TOPSIS-MCGP a tales problemas, teniendo en cuenta criterios tangibles e intangibles. 3. Los gerentes de aerolíneas ahora pueden tomar decisiones al elegir el mejor hotel para su HAA que cumpla con los objetivos comerciales de la aerolínea y las demandas de los pasajeros. 4. Los operadores de hoteles son flexibles en la selección de su asociación de aerolíneas, creando así mayores ganancias para ambas partes.

Hotel, Aerolínea, Técnica de grupo nominal (NGT), Técnica Para el orden de preferencia por similitud a solución real (TOPSIS), Programación de objetivos de opción múltiple (MCGP), Alianza horizontal

Trabajo de investigación

The objective of this paper is to present the employment of the new hierarchical fuzzy technique for order preference by similarity to ideal solution (TOPSIS) approach to evaluate the most suitable business process outsourcing (BPO) decision.

The paper explains the importance of selection criteria for evaluation of BPO. It then describes briefly the fuzzy hierarchical TOPSIS methodology. There then follows a discussion of the application of the fuzzy hierarchical TOPSIS with some sensitivity analysis to the BPO evaluation problem. Finally, some concluding remarks and perspectives are offered.

Use of the hierarchical fuzzy TOPSIS methodology offers a number of benefits. It is a more systematic method than the other fuzzy multi‐criteria decision‐making (FMCDM) methods and it is more capable of capturing a human's appraisal of ambiguity when complex multi‐criteria decision‐making problems are considered. The hierarchical fuzzy TOPSIS is superior to the other FMCDM methods, such as fuzzy analytic hierarchy process (FAHP) and classical fuzzy TOPSIS methods, since the hierarchical structure without making pairwise comparisons among criteria, sub‐criteria, and alternatives is considered. Hierarchical fuzzy TOPSIS is an excellent tool to handle qualitative assessments about BPO evaluation problems, and its calculations are faster than FAHP. Also, hierarchical fuzzy TOPSIS makes it possible to take into account the hierarchical structure in the evaluation model. However, there are drawbacks. The classical fuzzy TOPSIS is a highly complex methodology and requires more numerical calculations in assessing the ranking order of the alternatives than the hierarchical fuzzy TOPSIS methodology and hence it increases the effort, thus limiting its applicability to real world problems.

The proposed model will be very useful to managers in the manufacturing sector, as this method makes decision making easier, systematic, efficient and effective.

The purpose of this paper is to develop a model for sustainable manufacturing by adopting a combined approach using AHP, fuzzy TOPSIS and fuzzy EDAS methods. The proposed model aims to identify and prioritize the sustainable factors and technical requirements that help in improving the sustainability of manufacturing processes.

The proposed approach integrates both AHP, Fuzzy EDAS and Fuzzy TOPSIS. AHP method is used to generate the weights of the sustainable factors. Fuzzy EDAS and Fuzzy TOPSIS are applied to rank and determine the application priority of a set of improvement approaches. The ranks carried out from each MCDM approach is assessed by computing the spearman's correlation coefficient.

The results reveal the proposed model is efficient in sustainable factors and the technical requirements prioritizing. In addition, the results carried out from this study indicate the high efficiency of AHP, Fuzzy EDAS and Fuzzy TOPSIS in decision making. Besides, the results indicate that the model provides a useable methodology for managers' staff to select the desirable sustainable factors and technical requirements for sustainable manufacturing.

The main limitation of this paper is that the proposed approach investigates an average number of factors and technical requirements.

This paper investigates an integrated MCDM approach for sustainable factors and technical requirements prioritization. In addition, the presented work pointed out that AHP, Fuzzy EDAS and Fuzzy TOPSIS approach can manipulate several conflict attributes in a sustainable manufacturing context.

Safety data sheets are documents developed by chemical manufacturers to identify and label hazardous materials. The occupational safety and health administration regulations state that employers must make safety data sheets available for employees. When firms use hundreds of chemicals, tracking their safety data sheets becomes difficult. Safety Data Sheet Management Systems are developed to track safety data sheets. This paper aims to propose a multi-attribute decision-making framework for selecting a Safety Data Sheet Management System.

A total of 12 attributes are proposed based on a real-life project conducted at a firm in New York and the software selection models existed in the literature. Fuzzy technique for order of preference by similarity to ideal solution is used to assess the proposed attributes and alternatives. A case study and sensitivity analysis are conducted to show the robustness of the proposed model. Fuzzy analytical hierarchy process is used for validation.

Safety Data Sheet Management System is important for firms to track and manage safety data sheets. The proposed framework is practical and easy to implement.

The proposed decision model is useful for firms to select a proper Safety Data Sheet Management System. The system developers can use the model to update their systems.

This paper develops a new multi-attribute decision-making model for selecting a Safety Data Sheet Management System. To the best of the authors’ knowledge, no previous study has developed such a model.

The objective of the study is to develop a methodology to strategically rank store locations using criteria such as population, store site characteristics, economic considerations, competition and so on to select the most optimal retail convenience store location.

A case of National Capital Region, India, for a 24-h convenience store was considered for the study and the major criteria that affect the performance of a convenience store are identified, such as population characteristics, economic criteria, competition, consumer accessibility, store size, total cost, site attractiveness and security. Fuzzy AHP is utilized to find the weightage for each criteria and a combination of fuzzy TOPSIS and grey relational analysis (GRA) is applied to rank the alternative using these criteria weight. Further, results obtained are compared with results from fuzzy TOPSIS and fuzzy VIKOR methods. Sensitivity analysis is also performed for ensuring the robustness of the framework.

It is observed that outcomes do not change under various settling coefficient values, demonstrating that the methodology is very robust. The developed framework will be quite useful to diverse retailers looking to expand and generate substantial profits.

A large sample size of number of locations encourages generalization of results. Strategic ranking of the selected locations is carried out on a few selected criteria. The study was limited by the designated geographical area.

The study contributes to the few available articles on convenience store selection using combination of fuzzy AHP, fuzzy TOPSIS and GRA for a developing country.

This study aims to propose a method known as the fuzzy technique for order preference by similarity to ideal solution (fuzzy TOPSIS) for complex project selection in organizations. To fulfill study objectives, the factors responsible for making a project complex are collected through literature review, which is then analyzed by fuzzy TOPSIS, based on three decision-makers’ opinions.

The selection of complex projects is a multi-criteria decision-making (MCDM) process for global organizations. Traditional procedures for selecting complex projects are not adequate due to the limitations of linguistic assessment. To crossover such limitation, this study proposes the fuzzy MCDM method to select complex projects in organizations.

A large-scale engine manufacturing company, engaged in the energy business, is studied to validate the suitability of the fuzzy TOPSIS method and rank eight projects of the case company based on project complexity. Out of these eight projects, the closeness coefficient of the most complex project is found to be 0.817 and that of the least complex project is found to be 0.274. Finally, study outcomes are concluded in the conclusion section, along with study limitations and future works.

The outcomes from this research may not be generalized sufficiently due to the subjectivity of the interviewers. The study outcomes support project managers to optimize their project selection processes, especially to select complex projects. The presented methodology can be used extensively used by the project planners/managers to find the driving factors related to project complexity.

The presented study deliberately explained how complex projects in an organization could be select efficiently. This selection methodology supports top management to maintain their proposed projects with optimum resource allocations and maximum productivity.

The recent global market trend is seemed enforcing existing manufacturing organizations (as well as service sectors) to improve existing supply chain systems or to take up/adapt advanced manufacturing strategies for being competitive. The concept of the agile supply chain (ASC) has become increasingly important as a means of achieving a competitive edge in highly turbulent business environments. An ASC is a dynamic alliance of member enterprises, the formation of which is likely to introduce velocity, responsiveness, and flexibility into the manufacturing system. In ASC management, supplier/partner selection is a key strategic concern. Apart from traditional supplier/partner selection criteria; different agility-related criteria/attributes need to be taken under consideration while selecting an appropriate supplier in an ASC. The paper aims to discuss these issues.

Therefore, evaluation and selection of potential supplier in an ASC have become an important multi-criteria decision making problem. Most of the evaluation criteria being subjective in nature; traditional decision-making approaches (mostly dealing with objective data) fail to solve this problem. However, fuzzy set theory appears an important mean to tackle with vague and imprecise data given by the experts. In this work, application potential of the fuzzy multi-level multi-criteria decision making (FMLMCDM) approach proposed by Chu and Velásquez (2009) and Chu and Varma (2012) has been examined and compared to that of Fuzzy-techniques for order preference by similarity to ideal solution (TOPSIS) and Fuzzy-MOORA in the context of supplier selection in ASC.

It has been observed that similar ranking order appears in FMLMCDM as well as Fuzzy-TOPSIS. In Fuzzy-MOORA, the best alternative appears same as in case of FMLMCDM as well as Fuzzy-TOPSIS; but for other alternatives ranking order differs. A comparative analysis has also been made in view of working principles of FMLMCDM, Fuzzy-TOPSIS as well as Fuzzy-MOORA.

Application feasibility of FMLMCDM approach has been verified in comparison with Fuzzy-TOPSIS and Fuzzy-MOORA in the context of agile supplier selection.

Supplier evaluation and selection is an essential (multi-criteria decision-making) MCDM process that considers qualitative and quantitative factors. This research work attempts to use a MCDM technique based on merging fuzzy Technique for Order Preference by Similarity to Ideal Solution (F-TOPSIS) and Quality Function Deployment (QFD) ideas. The study attempts to find the supplier's attributes (HOWs) to accomplish its goals after determining the product's characteristics to suit the company's needs (WHATs).

The proposed research methodology comprises the following four steps: Step 1: Determine the product purchase requirements (“WHATs”) and those pertinent to supplier evaluation (“HOWs”). In Step 2, the relative importance of the “WHAT-HOW” correlation scores is determined and also the resulting weights of “HOWs”. In Step 3, linguistic evaluations of possible suppliers in comparison to subjective criteria are given to the decision-makers. Step 4 combines the QFD and F-TOPSIS techniques to select suppliers.

A fuzzy MCDM method based on fusing and integrating fuzzy information and QFD is presented to solve the drawbacks of conventional decision-making strategies used in supplier selection. Using the F-TOPSIS method, fuzzy positive ideal solution (FPIS) and fuzzy negative ideal solution (FNIS), the relative closeness coefficient values for all alternatives are computed. The suppliers are ranked by relating the closeness of coefficient values. This method permits the combination of ambiguous and subjective data expressed as fuzzy-defined integers or linguistic variables.

QFD and TOPSIS, two widely used approaches, are combined in this article to rank and evaluate suppliers based on the traits that the suppliers choose to prioritize. This study demonstrates that the method employed could address multiple-criteria decision-making scenarios in a computationally efficient manner. The effectiveness and applicability of the method are illustrated using an example.

The purpose of this paper is threefold: to classify wildcards into three particular types sharing similar characteristics; use the Fuzzy TOPSIS as a new method in foresight to turn qualitative ideas into quantitative ones; and apply a combination of Fuzzy TOPSIS and a panel of experts to prioritize weak signals.

In this paper, the authors classify wildcards into three particular types which share similar character: natural wildcards, artificial wildcards (Degree 1) and artificial wildcards (Degree 2). Wildcards point to unexpected and surprising events including important results that can form watershed in the development of a specific trend. In addition, the authors present a Fuzzy TOPSIS model which can be used in various cases to prioritize a number of weak signals and put them in order, so that the most important ones are likely to yield the wildcard in the future

The authors presented a classification of wildcards with the same characteristics being natural wildcards, artificial wildcards (Degree 1) and artificial wildcards (Degree 2). The authors also prioritized the weak signals to deal with the most important ones and take appropriate action in advance so as to minimize possible damages and maximize the benefits of potential wildcards in an uncertain environment.

In this paper, the authors report on the prioritizing of weak signals by applying Fuzzy TOPSIS and classify wildcards. This is significant because, by identifying the most important weak signals, appropriate actions can be taken in the future if necessary. The paper should be of interest to readers in the area of participatory foresight.

The purpose of the paper is to develop a model for the selection of knowledge management system (KMS), in which the assessment criteria are defined and the TOPSIS method with multiple distances in fuzzy environment is proposed.

First, the paper establishes the evaluation criteria from functional, performance and economic aspects. Second, a new TOPSIS method is proposed to deal with the linguistic evaluation information. In the proposed method, in order to eliminate the bias of TOPSIS with single distance, six kinds of distances that are commonly used in TOPSIS including Hamming distance, Euclidean distance, D_p,q distance, Hausdorff distance, L₂ distance and vertex distance are extended in fuzzy environment and employed in the TOPSIS to generate six independent pre-rankings. Afterwards these pre-rankings are combined by Condorcet method to generate the final joint ranking.

Since the final ranking is the collective result, the bias in each single pre-ranking is eliminated and the selection is more objective and accurate. The example shows the proposed model is practical.

The linguistic preferences are given in the single granularity linguistic information.

The proposed model can be applied as a tool for decision makers in the evaluation and selection of KMS.

The paper gives an overall evaluation of KMS and proposes the new TOPSIS method with multiple distances in fuzzy environment.