GPU-TOPSIS: A Complete Vectorized and Parallel Reformulation of the TOPSIS Method for Large-Scale Multi-Criteria Decision Making

Additive manufacturing has attracted attention as a new generation manufacturing method that has found widespread use in many industries in recent years due to its many advantages over traditional manufacturing methods. The materials used in metal additive manufacturing technology have a wide range. Therefore, making the ideal choice among these preferable materials is very important. Multi-criteria decision making (MCDM) techniques are reliable and effective methods in material selection processes and are effectively used in material selection processes. In this study, TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and Additive Ratio Assessment (ARAS) methods were applied to the selection process among different criteria and materials for metal additive manufacturing. It was observed that AlSi12Cu2Fe material ranked first in the TOPSIS method, while H13 material ranked first in the ARAS method. The second place was taken by H13 material in the TOPSIS method and AlSi12Cu2Fe material in the ARAS method. A strong relationship exists between TOPSIS and ARAS methods with a Pearson correlation coefficient of 0.977. It has been concluded that it will be more effective to decide according to the nature of the technological application in the use of the materials that rank first two in TOPSIS and ARAS methods in additive manufacturing.

Purpose - The purpose of this study, Borsa Istanbul 100 (BIST100) of the financial performance of energy companies traded multi-criteria decision making methods, TOPSIS (Technique for order preference by Similarity to ideal solution) method is measured. Methodology - For this purpose, 5 energy companies traded in BIST in 2021 and whose data are regularly accessed have been included in the scope of the study. The annual balance sheet and income statements of the companies were used in the study. The balance sheets and income statements of the companies within the scope of the research for the year 2021 were taken from the official website of the Public Lighting Platform (KAP). During the period when the application part of this study was conducted, the data of the year 2022 could not be used due to the fact that the data of the year 2022 has not yet been published. The obtained data were measured by TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method, which is one of the multi-criteria Decisionmaking methods, and the performance of the companies were analyzed and compared among themselves. Findings - Ipek Natural Energy Resources Research and Production A. One of the companies included in the study as a result of the analyzes performed by the TOPSIS method.Sh. ((IPEK) was found to be the company with the best data in terms of financial performance in the analyzes carried out in accordance with the criteria included in the evaluation. After this company, the second best company in terms of financial performance is Koza Anadolu Metal Madencilik Işletmeleri A.Sh. He appeared as a (COCOON). Conclusion - In the light of the analyzes carried out in the study; IPEKE company ranked first as the best company in terms of financial performance. Ardın KOZAA of IPEKE company ranked second, ASELS company ranked third, AKSEN company ranked fourth and VESTL company ranked last in terms of financial performance among the 5 companies included in the study. Dec. Apart from these data, it can be said that changing the December period to be based on different studies, using different financial ratios, the degree of importance given to criteria, using other multi-criteria decision-making methods (MSCS) such as MOORA other than TOPSIS may give results that may change the performance ranking. Keywords: BIST-100, TOPSIS, financial performance, energy, MCDMM JEL Codes: C61, M41, M49

Streamlining apartment provider evaluation: A spherical fuzzy multi-criteria decision-making model

Initial water rights allocation is a complex decision problem, and it is difficult for traditional methods to determine weights and solve the decision problem. In this study, an improved TOPSIS (Technique for order preference by similarity to ideal solution) method is proposed using vertical distance instead of Euclidean distance to calculate the closeness degree of schemes to the ideal solution, which overcomes the shortage of traditional TOPSIS method. In order to determine the weights of evaluation indexes reasonably, the improved method adopts game theory to harmonize and conjugate the subjective weight and the objective weight. On this basis, a comprehensive weight which eliminates the unilateral result can be obtained, and a model of initial water rights allocation in the watershed is established by the improved TOPSIS method. Finally, the proposed model is applied in the study of initial water rights allocation in Fuhuan River in Hubei Province, which shows rationality and validity when comparing with other models like traditional TOPSIS method, analytic hierarchy process and projection pursuit model.

− ecision support system is a computer-based system that can help decisions to solve certain problems by utilizing certain data and models. Many cases can be used as research in decision support systems, one of which is determining the best Vivo promoter. In this research, a decision making system will be designed using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. The TOPSIS method is a multi-criteria decision making method that uses the principle that the chosen alternative must have the shortest distance to the positive ideal solution and the farthest distance to the negative ideal solution. The steps used in the TOPSIS method are the normalization matrix calculation process, the weighted normalization matrix calculation process, the process of determining positive ideal solutions and negative ideal solutions, the process of calculating the distance of each alternative to the ideal solution, and the process of calculating the preference value of each alternative. The results obtained from this study are in the form of the best vivo Pramotor data in one month. Keywords : Decision Support System, TOPSIS, Best Vivo Pramotor

Comparative analysis between TOPSIS and PSI methods of materials selection to achieve a desirable combination of strength and workability in Al/SiC composite

To scientifically and reasonably evaluate and pre-warn the congestion degree of subway transfer hub, and effectively know the risk of subway passengers before the congestion time coming. We analyzed the passenger flow characteristics of various service facilities in the hub. The congested area of the subway passenger flow interchange hub is divided into queuing area and distribution area. The queuing area congestion evaluation model selects M/M/C and M/G/C based on queuing theory. The queuing model and the congestion evaluation model of the distribution area select the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method. Queue length and waiting time are selected as the evaluation indicators of congestion in the queuing area, and passenger flow, passenger flow density and walking speed are selected as the evaluation indicators of congestion in the distribution area. And then, K-means cluster analysis method is used to analyze the sample data, and based on the selected evaluation indicators and the evaluation model establishes the queuing model of the queuing area and the TOPSIS model of the collection and distribution area. The standard value of the congestion level of various service facilities and the congestion level value of each service facility obtained from the evaluation are used as input to comprehensively evaluate the overall congestion degree of the subway interchange hub. Finally we take the Xi’an Road subway interchange hub in Dalian as empirical research, the data needed for congestion evaluation was obtained through field observations and questionnaires, and the congestion degree of the queue area and the distribution area at different times of the workday was evaluated, and the congestion of each service facility was evaluated. The grade value is used as input, and the TOPSIS method is used to evaluate the degree of congestion in the subway interchange hub, which is consistent with the results of passenger congestion in the questionnaire, which verifies the feasibility of the evaluation model and method.

Tourism is the mainstay of the economy of the region of Bali and is an important sector in supporting the level of community welfare. The world of tourism is essentially an important symbol for Bali. Of the many tourists who come to Bali, of course not all tourists know all information about Bali, such as in terms of tourist locations, and tourist attractions closest to other tourist attractions. Therefore, the authors aim to create a recommendation system that can provide planning for the selection of tourist attractions according to the closest distance and the user's budget.
 This system is designed using the TOPSIS (Technique for Order Preference by Similarity To Ideal Solution) method and the Greedy Algorithm. The TOPSIS method is a SPK (Decision Making System) that will be used for the selection of tourist attractions that will help tourists to arrange vacation planning before going on a tour. While the Greedy Algorithm is used to find the closest or shortest distance between a tourist site and other tourist attractions, this algorithm will be able to determine which path will be taken first or called the local optimal path, so that all the paths are taken at the end of the trip and create a travel route shortest or called the global optimum so that it can also be the expected solution. From this it can be determined the value of the shortest travel route that starts from the location of the user's residence to the tourist attractions and to other nearby tourist attractions. The data used is data obtained from DISPARDA, namely tourist data.
 Research related to the selection of tourism object decisions based on the type of tourism, prices and facilities, and the results are able to provide recommendations for tourist attractions that meet these criteria. Tourism Selection Using Techniques For Order Preference By Similarity To Ideal Solution (Topsis) With Object Localization Visualization [4], Development of Decision Support System for Hotel Determination in Buleleng District Using Analytic Hierarchy Process (AHP) Method and Technique for Others Reference By Similarity To Ideal Solution (TOPSIS) [3], a Decision Support System for Determining Tourist Locations using the Top-sis Method [6]

Taguchi method, as a cost-effective method for off-line quality control, especially the quadratic loss function shows loss of quality directly. However, most previous Taguchi method applications often deal with single-response problem, but few of them pay attention to multi-response problem. In fact, there are always more than one response exist in product or process design, and TOPSIS (Technique for order preference by similarity to ideal solution) method is a popular multiple attribute decision making (MADM) methodology. In conventional TOPSIS method, responses are usually transformed into loss of quality, and then normalized by the largest quality loss. However, for nominal-the-best (NTB) problem, it's not a reasonable methodology. The proposed method is based on TOPSIS and makes an improvement to adapt to NTB response. The improved TOPSIS method is illustrated by an example from literature, and the result indicates its reasonableness and effectiveness.

TOPSIS (Technique for order preference by similarity to ideal solution) method, as a multiple attribute decision making (MADM) methodology, is often used for solving problems having multivariable quality characteristics. In conventional TOPSIS method, responses are usually transformed into loss of quality, and then normalized by the largest quality loss. However, the conventional method lacks of robustness for it only takes the mean value into consideration and ignores the variance of response. Based on the conventional TOPSIS methodology, the proposed method makes an improvement for the purpose of robust design by taking the mean value and variance of response into consideration simultaneously. The improved TOPSIS method is illustrated by an example from literature, and the result indicates its reasonableness and effectiveness.

There are always more than one quality responses or characteristics in product or process design and engineers often must choose optimum parameter combination for these responses simultaneously. Taguchi method, as a cost-effective method for off-line quality control, especially the quadratic loss function shows loss of quality directly, has been widely employed by quality engineering to deal with single-response problem, but few of them pay attention to multi-response problem. TOPSIS (Technique for order preference by similarity to ideal solution) method is a popular used multiple attribute decision making (MADM) methodology. In conventional TOPSIS method, responses are usually transformed into loss of quality, and then normalized by the largest quality loss. However, for nominal-the- best (NTB) problem, it's not a reasonable methodology. Based on conventional TOPSIS, the proposed method makes an improvement to adapt to NTB type response, it also takes the standard deviation of responses into consideration simultaneously for the purpose of robustness. The improved TOPSIS method is illustrated by an example from literatures, and the result indicates its reasonableness and effectiveness.

Among several computing models in Health Insurance, the selection of appropriate health insurance is necessary for gaining higher beneficial profit during critical situations. Sometimes the random selection of Health Insurance leads to bad results. In this paper, we proposed the Multiple Criteria Decision Making (MCDM) theory to make a decision about the best form of health insurance. This MCDM is one of the best ideal solutions to overcome bad results. We implemented TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) method for choosing the best Health Insurance. In the TOPSIS method, we have defined the Fuzzy TOPSIS hesitant method for a decision-making scenario for multi-criteria. The advantages of hesitant fuzzy TOPSIS approaches are flexibility, consistency, understandability, strong analytical efficiency and the ability to calculate the relative strength in a simple mathematical form for each alternative. In this, we used linguistic and Intuitionistic decision-makers. Here multiple objectives like 1.beneficial and non-beneficial 2.fuzzy weightage for attributes are used for group decision making. In the Health Insurance scenario we have multi attributes like 1.individual plan 2.family plan 3.entry age 4.premium 5.claim 6.sum assured that are applied to all Health Insurance and by using the above methods.

The choice of appropriate nano additives for tribological purposes has many factors and requirements to be met. These include: lower friction; better wear properties; formation of protective tribofilms; good dispersibility in lubricant; and good thermal oxidation stability. In this paper, an MCDM (Multi-Criteria Decision Making) framework is presented for evaluating/ranking different types of nano additives used in the tribological field. Specifically, MoS₂, WS₂, MoO₃, TiO₂, Al₂O₃, SiO₂, and glycerol were evaluated. To select the best nano additive, a comparison was made with each other based on how close they came to the "ideal" solution. This comparison was done using the TOPSIS (Technique for Order Preference by Similarity to Ideal Solution). A decision matrix was prepared from published experimental data. Then, after normalizing each criterion and assigning weights to each one, the results were ranked. Finally, to validate the rankings obtained from the TOPSIS analysis, a second decision-making technique called SAW (Simple Additive Weighting) was applied. The rankings from both decision-making techniques were consistent and confirmed the effectiveness of the multi-criteria decision-making approach. From the TOPSIS analysis, it could be concluded that MoS₂ achieved the greatest value for closeness to the "ideal" solution which indicates that MoS₂ exhibited the most favorable behavior when considering all criteria. Additionally, it appears that MoO₃ and WS₂ will serve as possible candidates for use in future studies or as substitutes in certain applications where high temperatures and/or heavy loads exist.

A state-of the-art survey of TOPSIS applications

Worldwide, earthquakes and related disasters have persistently had severe negative impacts on human livelihoods and have caused widespread socioeconomic and environmental damage. The severity of these disasters has prompted recognition of the need for comprehensive and effective disaster and emergency management (DEM) efforts, which are required to plan, respond to and develop risk mitigation strategies. In this regard, recently developed methods, known as multi-criteria decision analysis (MCDA), have been widely used in DEM domains by emergency managers to greatly improve the quality of the decision-making process, making it more participatory, explicit, rational and efficient. In this study, MCDA techniques of the Analytical Hierarchical Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), integrated with GIS, were used to produce earthquake hazard and risk maps for earthquake disaster monitoring and analysis for a case study region of Kucukcekmece in Istanbul, Turkey. The five main criteria that have the strongest influence on the impact of earthquakes on the study region were determined: topography, distance to epicentre, soil classification, liquefaction and fault/focal mechanism. AHP was used to determine the weights of these parameters, which were also used as input into the TOPSIS method and GIS (ESRI ArcGIS) for simulating these outputs to produce earthquake hazard maps. The resulting earthquake hazard maps created by both the AHP and TOPSIS models were compared, showing high correlation and compatibility. To estimate the elements at risk, population and building data were used with the AHP and TOPSIS hazard maps for potential loss assessment; thus, we demonstrated the potential of integrating GIS with AHP and TOPSIS in generating hazard maps for effective earthquake disaster and risk management.