The aim of this paper is to apply the technique for order preference by similarity to ideal solution (TOPSIS) as a multi-criteria decision making tool to form the all-time best World XI Test cricket team while taking into consideration over 2600 cricketers participated in Test matches for more than 100 years of cricket history. From the voluminous database containing the performance of numerous Test cricketers, separate lists are first prepared for different positions in the batting and bowling orders consisting of manageable numbers of candidate alternatives while imposing some constraints with respect to the minimum number of innings played (for batsmen), minimum number of tests played (for wicketkeepers and bowlers), and minimum numbers of runs scored and wickets taken (for all-rounders). The TOPSIS method is later adopted to rank those shortlisted cricketers and identify the best performers for inclusion in the proposed World XI Test team. The best World Test cricket team is thus formed as Alastair Cook (ENG) (c), Sunil Gavaskar (IND), Rahul Dravid (IND) (vc), Sachin Tendulkar (IND), Shivnarine Chanderpaul (WI), Jacques Kallis (SA), Adam Gilchrist (AUS) (wk), Glenn McGrath (AUS), Courtney Walsh (WI), Muttiah Muralitharan (SL) and Shane Warne (AUS).

In this paper, the ranking performance of six most popular and easily comprehensive multi-criteria decision-making (MCDM) methods, i.e. weighted sum method (WSM), weighted product method (WPM), weighted aggregated sum product assessment (WASPAS) method, multi-objective optimization on the basis of ratio analysis and reference point approach (MOORA) method, and multiplicative form of MOORA method (MULTIMOORA) is investigated using two real time industrial robot selection problems. Both single dimensional and high dimensional weight sensitivity analyses are performed to study the effects of weight variations of the most important as well as the most critical criterion on the ranking stability of all the six considered MCDM methods. The identified local weight stability interval indicates the range of weights within which the rank of the best alternative remains unaltered, whereas, the global weight stability interval determines the range of weights within which the overall rank order of all the alternatives remains unaffected. It is observed that for both the problems, multiplicative form of MOORA is the most robust method being least affected by the changing weights of the most important and the most critical criteria.

he VIKOR (Vlse Kriterijumska Optimizacija Kompromisno Resenje which means multi-criteria optimization and compromise solution, in Serbian) method has already become a quite popular multi-criteria decision making tool for its computational simplicity and solution accuracy. This method focuses on selecting and ranking from a set of feasible alternatives, and determines compromise solution for a problem with conflicting criteria to help the decision maker in reaching a final course of action. It determines the compromise ranking list based on the particular measure of closeness to the ideal solution. Depending upon the type of decision problem and necessity of the decision maker, apart from VIKOR method, different variants of it, like comprehensive VIKOR, fuzzy VIKOR, regret theory-based VIKOR, modified VIKOR and interval VIKOR methods have also been subsequently developed. In this paper, the ranking performance of original VIKOR method and its five variants is analyzed based on two demonstrative examples. It is observed that interval VIKOR method performs unsatisfactorily and when the information in a decision problem is imprecise, fuzzy VIKOR method should always be preferred. But, for any decision problem, original VIKOR is the best method for solution without unnecessarily complicating the related mathematical computations.

Supplier selection is always found to be a complex decision-making problem in manufacturing environment. The presence of several independent and conflicting evaluation criteria, either qualitative or quantitative, makes the supplier selection problem a candidate to be solved by multi-criteria decision-making (MCDM) methods. Even several MCDM methods have already been proposed for solving the supplier selection problems, the need for an efficient method that can deal with qualitative judgments related to supplier selection still persists. In this paper, the applicability and usefulness of measuring attractiveness by a categorical-based evaluation technique (MACBETH) is demonstrated to act as a decision support tool while solving two real time supplier selection problems having qualitative performance measures. The ability of MACBETH method to quantify the qualitative performance measures helps to provide a numerical judgment scale for ranking the alternative suppliers and selecting the best one. The results obtained from MACBETH method exactly corroborate with those derived by the past researchers employing different mathematical approaches.

The paper focuses on the application of TODIM (TOmada de Decisao Interativa Multicriterio), which means in Portuguese ‘interactive and multi-criteria decision making’) method in identify-ing the most attractive and affordable under-construction housing project in the city of Kolkata in India. In this decision making problem, 14 under-construction housing projects spanning in and around Kolkata are evaluated with respect to ten important criteria. The deployment of TODIM method can well be validated with respect to its ability to deal with both qualitative and quantitative criteria in the presence of risk factors. This method is also acknowledged to be a robust tool being almost unaffected by the varying values of the attenuation factor of losses. Using TODIM method, an under-construction housing project at Rajarhat in the eastern fringes of Kolkata city is selected as the optimal choice while meeting the requirements of investors, inhabitants and other stakeholders.

Since independence, India has been one of the few developing countries to invest extensively in both science and technical education. In India, technical education plays a pivotal role in human resource development while creating skilled manpower, increasing industrial productivity and enhancing the quality of life. If a technical institute means to be effective in developing learned and qualified engineers, then it would be useful to know the performance of that technical institution. However, measuring the performance of a technical institution has received very little attention because it is very difficult to measure its output. Thus, this paper focuses on assessing the performance of eight Indian Institutes of Technology (IITs) using a combined approach of data envelopment analysis (DEA) and technique of order preference by similarity to ideal solution (TOPSIS). In the first phase, DEA is applied to shortlist the efficient IITs having the desired characteristics from the stakeholders’ point of view, and TOPSIS method is then employed to rank those efficient IITs while also identifying the best performing IIT. It is observed that IIT Kharagpur outperforms all the considered IITs which exactly corroborates with the findings of the recently published surveys/reports.

With ever increasing demand for manufactured products of hard alloys and metals with high surface finish and complex shape geometry, more interest is now being paid to non-traditional machining (NTM) processes, where energy in its direct form is used to remove material from workpiece surface. Compared to conventional machining processes, NTM processes possess almost unlimited capabilities and there is a strong believe that use of NTM processes would go on increasing in diverse range of applications. Presence of a large number of NTM processes along with complex characteristics and capabilities, and lack of experts in NTM process selection domain compel for development of a structured approach for NTM process selection for a given machining application. Past researchers have already attempted to solve NTM process selection problems using various complex mathematical approaches which often require a profound knowledge in mathematics/artificial intelligence from the part of process engineers. In this paper, four NTM process selection problems are solved using an integrated PROMETHEE (preference ranking organization method for enrichment evaluation) and GAIA (geometrical analysis for interactive aid) method which would act as a visual decision aid to the process engineers. The observed results are quite satisfactory and exactly match with the expected solutions.

In this study, the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) was applied to rank the telemedicine performance of seven major Japanese cities: Fukuoka, Sapporo, Yokohama, Kyoto, Osaka, Nagoya, and Tokyo. The ranking was consistent throughout the analysis based on the five criteria—Infrastructure, Healthcare System Integration, Accessibility & Equity, Service Breadth & Quality, and Regulatory Environment. Fukuoka was the best performer, followed by Sapporo, and Tokyo, even with its high medical resources, ranked last. The same ranking was observed even when the cost criterion was given a 50% weight, which means that structural and governance factors have more influence than affordability in determining performance. The findings question the view that telemedicine success relies exclusively on economic and medical scale. Rather, they point to the effectiveness of Japan's regional innovation policies; for example, proactive local governance, as in the case of Fukuoka's Special Zone status, and a clear demographic mandate for rural healthcare access, as in Hokkaido, are considered more important drivers of a robust and equitable telemedicine ecosystem.

This study evaluates the relative efficiency of digital health adoption across ten major German cities and regions. In a landscape where digital transformation is critical, this research moves beyond mere technological assessment to determine which locales most effectively convert inputs into outputs. A multi-criteria decision-making framework is employed, integrating Data Envelopment Analysis (DEA) under both constant and variable returns-to-scale assumptions and the TOPSIS method. Key metrics include Infrastructure, Healthcare System Integration, Service Breadth, Regulatory Environment, and a critical cost factor, Accessibility & Equity. Results from DEA models highlight a system-wide scale inefficiency, yet identify specific efficient units under variable returns, including Berlin, Rhineland-Pfalz, Leipzig, and Aachen. The TOPSIS analysis, particularly when prioritizing cost-equity, reveals a distinct ranking: Rhineland-Pfalz, Leipzig, and Aachen emerge as top performers, while major economic hubs like Munich, Frankfurt, and Stuttgart demonstrate lower efficiency due to high costs and systemic fragmentation. The findings challenge the presumption that economic scale guarantees digital health efficiency, instead underscoring the superior performance of strategically coordinated and equity-focused models. This study provides policymakers with a robust framework for benchmarking and highlights governance and integration, not just investment, as the key levers for enhancing digital health system performance.

This research applies TOPSIS, which stands for Technique for Order of Preference by Similarity to Ideal Solution, to evaluate and rank the performance of telemedicine in the seven Chinese cities—Hangzhou, Wuhan, Chengdu, Shanghai, Beijing, Guangzhou, and Shenzhen. The evaluation was based on five major criteria: Infrastructure, Healthcare System Integration, Accessibility & Equity, Service Breadth & Quality and Regulatory Environment. In a noteworthy finding, Hangzhou was at the top all the time, while Shenzhen was at the bottom despite its technological might. The result was very strong, being the same even when the cost criterion was given 50% weight which showed that the price factor alone does not overpower other performance disparities. The outcomes are a surprise to the common understanding that very high-quality medical institutions in a city and very good technology infrastructure are the only factors that determine the success of telemedicine. Rather, a city’s ability to build an integrated, user-centered digital ecosystem determines the leadership in telemedicine. The first position of Hangzhou indicates the benefit of unrestricted healthcare accessibility through a platform-based system whereas the last position of Shenzhen points out the drawbacks of an expensive and inequitable system. This study shifts the paradigm for future telemedicine cities from "Medical Resource" to "Integrated Ecosystem" model, thus, posing significant implications for the decision makers who strive to ensure effective and equal access to digital health services.