This article introduces a comprehensive analysis of 20 leading companies, scrutinized through their financial metrics across various sectors. By deploying multi-criteria decision-making (MCDM) techniques, we aim to offer investors a clear and objective perspective on which companies stand out as the best investment options. Among the MCDM techniques, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is utilized, renowned for its efficiency in handling complex decision-making scenarios which is conducted by two clauses. 1) Implementing TOPSIS with assigning equal weights and same share to every chosen metrics as criteria and 2) employ BWM (Best Worst Method) to calculate these weights base on their significance and relevancy to the prosses of ranking. According to the Result gained from the computation, ranks 1 to 5 belong to the similar companies with both assumptions which are Ford Motor Co, BP plc, Tesla Inc, General Motors Co and Exxon Mobil Corp. The consistency in rankings across two different weighting assumptions highlights the robustness of the criteria used, ensuring stable and reliable outcomes. This enhances the credibility of the findings, making them more trustworthy and citable for those who seek reliable and robust methodologies for informed investment decisions.

In MCDM problems, the decision maker is often ready to adopt the closest solution to the reference values in a choice or ranking problem. The reference values represent the desired results as established subjectively by the decision maker or determined through various scientific tools. In a criterion, the reference value could be the maximum value, the minimum value, or a specific value or range. Also, the acceptances degrees of ranges outside the reference may differ from each other in a criterion. Furthermore, measurements in a criterion may have been obtained with any of the nominal, ordinal, interval, and ratio scales. For the decision problems, including qualitative criteria, the solution cannot be achieved without scaling of criteria with the existing MCDM methods. The purpose of this study is to propose the Nearest Solution to References (REF) Method, a novel reference-based MCDM method, for the solution of decision problems having mixed data structure where references can be determined for criteria.

Multi-criteria decision making (MCDM) methods are systematical science projects to help decision-makers reach accurate decisions. Applying MCDM methods in the military is important because accurate decision making is the deciding factor for success and can reduce expenditure and increase defense capability. The full consistency method (FUCOM), one of the methods in the MCDM group, has many advantages, and its results are reliable. This paper aims to evaluate and select an appropriate fighter aircraft for Vietnam People’s Air Force. Using FUCOM as a decision-making process, we find the final weight values of criteria and apply the additive ratio assessment (ARAS) method to derive the final ranking of alternatives to comply with criteria. Sensitivity analysis is conducted and the result is compared with the weighted product method to substantiate the sturdiness of the proposed method. The results show the Su-35 as the best available solution.

Evaluating research productivity of lecturer is a vital process of universities for teaching improvement and administrative decision making. Lecturers’ research productivity evaluation is a difficult and sensitive problem which has many objective and subjective criteria, complexity and imprecision. Therefore, the evaluation of lecturers’ research productivity can be viewed as a multi-criteria decision making (MCDM) problem in vague environment. This study develops an integrated MCDM approach for evaluating the lecturers’ research productivity. In the proposed approach, a fuzzy analytic hierarchy process (AHP) is applied to determine the weights of criteria. A Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method using interval neutrosophic sets is further adopted for showing the preference order of the lecturers’ research productivity in an educational organization. Finally, the proposed approach is applied to solve the lecturers’ research productivity evaluation problem in the case of University of Economics and Business, Vietnam National University, Hanoi (UEB-VNU).

Traditional project management methods have been used for many years in the implementation and management of projects. With the publication of the agile manifesto in 2001, the interest in agile methods increased and successful results were obtained in the execution and solution of complex projects with agile approaches. Agile approach consists of different structures that can be named as framework or method. Among these agile methods, which are selected and applied according to the suitability and purpose of the project, the most frequently preferred one is SCRUM. In this study, SCRUM, an agile project management technique that has been used frequently in recent years in order to develop a flexible project management process, has been examined, and the effectiveness of the events of the SCRUM technique in minimizing the risks that arise in project management has been evaluated. Determining the effectiveness of these SCRUM events, which each agile project team implements in turn, is very important for the correct and effective allocation of resources. For this purpose, a multi-criteria decision-making model has been proposed to evaluate the effectiveness of 4 SCRUM events within the scope of this study. While the importance weights of the 24 project management risk factors proposed in the solution phase of the created model were determined by the SWARA method, the SCRUM events were evaluated with the WASPAS, COPRAS and EDAS methods and the solution values found by three different methods were compared.

As archipelagic region, an effort to effectively enhance and accelerate the development of each sub-districts to boost the rapid development of Southeast Maluku district in Indonesia cannot happen as long as the local government fails to identify the real potentials and power in fisheries sector of each sub-districts. Identification of each sub-district fisheries top sub-sector has to be based on the human resources, natural resources, infrastructure, current and potential market, current policy of local and central government. A multi-criteria decision making (MCDM) is one of the powerful tools to provide a better result based on complicated factors involved. This paper proposed an integrated MCDM, to tackle the complicated factors in order to provide the best commodities on each sub-districts. Bottom-up concept was used to have a comprehensive result, by combining Fuzzy logic with Analytical Hierarchy Process (AHP) to measure the factors using fuzzy logic with Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) for determining the top sub-sector in fisheries. For comprehensive assessment of macro factors the study used experts ranging from government, scientists, practitioners to NGOs. On the other hand, for micro factors the survey used field instructor, field officer, fishers and farmers. The results provide a guideline for local and central government to form a better policy regarding the development of each sub-district including farmers, fishers and coastal communities in each sub-district to focus on commodities that benefited their regions’ resources and coastal community’s capabilities. By doing so, we hope to contribute on crafting an integrated and collective path on reaching the goal which is the welfare of coastal region.

Green supplier selection is an important group decision making problem that includes not only economic criteria, but also environment and society criteria incorporating vagueness and imprecision. This paper proposes a new integrated quality function deployment (QFD) and technique for order preference by similarity to ideal solution (TOPSIS) using generalized fuzzy numbers. The proposed approach initially identifies the features that the purchased product should possess in order to satisfy the company’s needs (“WHATs”), and then it seeks to establish the relevant supplier assessment criteria (“HOWs”). Then, the importance of the ‘‘WHATs’’, ‘‘HOWs’’, ‘‘HOWs’’-‘‘WHATs’’ correlation scores, and the impact of each potential green suppliers are determined based on generalized fuzzy numbers. The study applies the TOPSIS approach to rank the green suppliers. A numerical example is used to illustrate the advantages and applicability of the proposed approach.

Conveyors and automated guided vehicles (AGVs) are important mechanical handling equipment used to transport loads from one place to another. Selection of proper material handling equipment is an intricate process. In the present paper two material handling equipment selection problem was solved using various newly developed multi criteria decision making (MCDM) methods. First a conveyor selection problem with six conflicting criteria and four alternatives was solved using four MCDM methods, i.e. combinative distance based assessment (CODAS) method, evaluation based on distance from average solution (EDAS) method, weighted aggregated sum product assessment (WASPAS) method and multi-objective optimization on the basis of ratio analysis (MOORA) method. Second an automated guided vehicles selection problem with six conflicting criteria and eight alternatives was solved using CODAS, EDAS, WASPAS and MOORA methods. Spearman rank correlation coefficient was calculated between the ranks obtained by various methods. The ranks obtained by these methods were even compared with the ranks of other MCDM methods and it was found that the relatively new methods CODAS, EDAS and WASPAS were in good agree-ment with each other.

Supplier selection is one of the most essential activities in purchase management and plays a crucial role in the production phase. Supplier selection as a vital step of supply chain management is a multi-criteria decision-making issue. For any organization, the process of selecting the best supplier holds variable multilayered complications involving quantitative and qualitative criteria. This paper tackles the supplier selection problem in a Turkish Textile Company. The present study carries out a novel grey integrated multi-criteria approach for enhancing the supplier procedure within Textile Company with the help of the grey analytical hierarchy process G-AHP model for weighting the set of criteria, and the grey weighted aggregated sum product assessment WASPAS-G model for prioritizing the suppliers. The study starts with reviewing the previous works of multi-criteria decision-making MCDM methods and the list of existing criteria evaluation in supplier selection. Then, the range of criteria is selected based on the company requirements and the experts’ interview. In the case study, the consistency rate of the models is tested in order to verify the quality of experts’ judgments. The final results affirm that Grey integrated approach could be efficient and far more precise than the existing models for overcoming the supplier selection and evaluation obstacles in the supply chain management.

The current paper deals with incomplete Pairwise Comparisons (‘PWs’) when a large number of alternatives is evaluated. PWs are used to quantify decision maker's preferences, both ordinal and cardinal, in multi-criteria decision-making settings for eliciting the priorities of alternative options or weights of criteria. We use additive PWs with a different scale and show how 2-diagonal samples are used to deduce the implied weights thus prioritizing the alternatives. As a consequence, the number of PWs in incomplete judgment decision matrices is greatly reduced while preserving consistency and quality of the results. Computational results are provided and an example from the literature is applied to demonstrate the effectiveness of this method.