A novel approach to method selection in multi-criteria decision-making

This research project utilizes Multi-Criteria Decision-Making (MCDM) techniques, specifically Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Multi-Objective Optimization on the basis of Ratio Analysis (MOORA), to enhance the selection of High Renewable Energy Systems (HRES). As global environmental concerns intensify, HRES selection becomes pivotal for sustainable energy solutions. The project's primary objective is to furnish decision-makers with a structured framework that accommodates numerous criteria and objectives. Initially, an extensive set of criteria, spanning environmental, economic, and technical aspects of HRES, is established. AHP assigns relative weights to these criteria, ensuring a well-informed decision-making process. Subsequently, TOPSIS ranks HRES alternatives based on their performance against these criteria, yielding a shortlist of viable HRES solutions. MOORA extends the analysis by addressing conflicting objectives within the decision-making process, accommodating both maximization and minimization objectives. Combining insights from AHP, TOPSIS, and MOORA offers a comprehensive overview of the most appropriate HRES alternatives. This holistic approach equips decision-makers with valuable information on trade- offs and benefits, facilitating the selection of HRES solutions that align with diverse goals. By leveraging these MCDM methods, this project promotes systematic and informed HRES selection, fostering the advancement of sustainable energy solutions and contributing to a greener future. Key Words: MCDM; AHP; TOPSIS; MOORA; HRES.

Optimization of process parameters in machining process leads to enhancement in process outcomes. Turning process is one of the primary operations in manufacturing industries. Multi-Criteria Decision Making (MCDM) concepts are used by researchers to optimize the process parameters in turning process. These MCDM methods are used to rank and find out the best combination of process parameters from given number of alternatives. In this study, a detailed literature survey is carried out in the area of application of different MCDM methods which are used for optimization of turning process parameters. There are different MCDM methods are available, but in this work only focused application related to manufacturing domain and methods are reviewed namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), VlseKriterijumska Optimizacija I Kompromisno Resenje in Serbian (VIKOR), Multi-Objective Optimization on the basis of Ratio Analysis (MOORA) method, and Analytic Hierarchy Process (AHP) method. The result of review work indicates that the above-mentioned MCDM methods are capable of solving multiple criteria problems for turning process parameter optimization.

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a popular multi-criteria decision-making method that ranks the available alternatives by examining the ideal-positive and ideal-negative solutions for each decision criterion. The first step of using TOPSIS is to normalize the presence of incommensurable data in the decision matrix. There are several normalization methods, and the choice of these methods does affect TOPSIS results. As such, some efforts were made in the past to compare and recommend suitable normalization methods for TOPSIS. However, such studies merely compared a limited collection of normalization methods or used a noncomprehensive procedure to evaluate each method's suitability, leading to equivocal recommendations. This study, therefore, employed an alternate, comprehensive procedure to evaluate and recommend suitable benefit/cost criteria-based normalization methods for TOPSIS (out of ten methods extracted from past literature). The procedure was devised based on three evaluation metrics: the average Spearman's rank correlation, average Pearson correlation, and standard deviation metrics, combined with the Borda count technique.•The first study examined the suitability of ten benefit/cost criteria-based normalization methods over TOPSIS.•Users should combine the sum-based method and vector method into the TOPSIS application for safer decision-making.•The maximum method (version I) or Jüttler's-Körth's method has an identical effect on TOPSIS results.

Soil erosion (SE) creates several environmental problems in fragile tropical watersheds, such as channel degradation, reservoir sedimentation, and flash flood. Therefore, identification of erosion susceptible areas at the sub-watershed level is necessary for the sustainable management of available resources. Previous studies on sub-watershed prioritization are based on a particular method, which leads to uncertainty because other methods may produce different results. Therefore, in the present work, a novel MCDM (multi-criteria decision-making)–based ensemble approach has been used for the prioritization of sub-watersheds of the Dwarkeswar River basin. Five MCDM models were used in the present study, namely simple additive weighting (SAW), complex proportional assessment (COPRAS), additive ratio assessment (ARAS), technique for order preference by similarity to ideal solution (TOPSIS), and multi-objective optimization on the basis of ratio analysis (MOORA). The non-parametric Spearman rank correlation method was used to determine the best model and measure the degree of similarity among the results obtained from different models. Rank correlation indicated that the COPRAS model has the highest accuracy in the prediction of erosion susceptibility. But the final ranks were given to the sub-watersheds by averaging the ranks obtained from different MCDM models. The TOPSIS model was not included for averaging because TOPSIS shows a negative correlation with the SAW (− 0.082) and ARAS (− 0.179) models and a very low positive correlation with the COPRAS (0.181) model. This combined ensemble method placed sub-watersheds 10 and 11 in first and second ranks, respectively, on the basis of susceptibility to SE.

Multi-Criteria Decision Making (MCDM) stands as a widely employed technique for ranking alternatives and identifying the most suitable option across diverse domains. However, the inherent algorithmic variations among different MCDM methods can lead to discrepancies in the ranking outcomes when applied to the same problem. Consequently, to enhance the reliability of alternative rankings, it is crucial to address the problem using multiple distinct MCDM approaches. This study integrates three prominent methods: Simple Additive Weighting (SAW), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), and Root Assessment Method (RAM) to concurrently rank alternatives within a representative food-related example, specifically the nutritional value assessment of various nut types. The SAW, TOPSIS, and RAM methodologies were applied to rank eight types of nuts: almond, Brazil nut, cashew, hazelnut, macadamia, pecan, chestnut, and walnut, each characterized by nineteen distinct nutritional attributes. The results demonstrate a consistent identification of the top-ranked nut across all methods. Furthermore, the ranking order of the remaining alternatives exhibited minimal variation among the three approaches. Spearman's rank correlation coefficients were 0.905 between SAW and TOPSIS, 0.929 between SAW and RAM, and 0.976 between TOPSIS and RAM. These findings not only offer valuable guidance for consumers in selecting the optimal nut product, but also provide a clear direction for practitioners to consider the combined application of these three MCDM methods for ranking alternatives in other fields.

With the growing population increase and following young population’s desire to study at the university, political authorities are supporting university and higher education investments, especially in the last 10 years. This situation has increased the number of universities considerably. Because a university will provide socioeconomic dynamism to both the development of the country and the region, choosing the right university location has become a significant problem nowadays. In line with this, this study is focused on supporting the new university location decision in a wide region in Turkey where currently the number of universities in the region is relatively low despite the high population density in the area. Alternative cities to be utilized in the study are determined particularly taking the demographic structure into consideration and various multicriteria decision-making (MCDM) techniques are applied. Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Weighted Aggregated Sum Product Assessment (WASPAS), and Multi-Objective Optimization on the Basis of Ratio Analysis (MOORA) are applied to a real case study. Related criteria and alternative locations are specified by consulting seven experts. Within the study, both the results of these methods are presented, and also sensitivity analyses are conducted to observe how sensitive the results are to the changes in the criteria weights. The results obtained are purposed to aid decision makers in this field.

Selecting the best carpet from a set of available alternatives for a particular end-use situation is a difficult job because of association with multiple decision criteria. Three multicriteria decision-making (MCDM) methods, i.e., weighted sum model (WSM), weighted product model (WPM), and technique for order preference by similarity to ideal solution (TOPSIS) were used to evaluate the overall durability value of handmade carpets considering abrasion loss, compression recovery, thickness loss after dynamic loading, and thickness loss after prolonged heavy static loading as decision criteria. The weights of carpet properties (decision criteria) were determined by considering opinion of 10 domain experts. Abrasion loss, compression recovery, thickness loss after dynamic loading and thickness loss after prolonged heavy static loading received weights of 0.366, 0.186, 0.308, and 0.140, respectively. All the three MCDM methods yielded the same ranking for the first position. Moreover, WSM and WPM had complete agreement for the first seven positions. Rank correlation coefficient between WSM-WPM, WSM-TOPSIS, and WPM-TOPSIS are found to be 0.990, 0.943, and 0.973, respectively i.e. very high. Therefore, any one of these MCDM methods can be used for handmade carpet selection problem. Sensitivity analysis proved that the ranking produced by TOPSIS is quite robust in response to the change in weights of decision criteria.

In today's world purchasing a smart phone has become complicated task for the customers due to range of specifications like Battery, Camera, Screen Size, Cost, Performance, etc. To counter this problem we have number of techniques available with us like the multi-criteria decision-making (MCDM), fuzzy logic, etc. We have used TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and MOORA (Multi-Objective Optimization on the basis of Ratio Analysis). The objective of this study methodology is to obtain an effective and efficient multi criteria decision making (MCDM) approach to evaluate different smart phone alternatives according to consumer preferences and to find out the best optimal smart phone. Main 12 attributes were identified through many literature reviews and discussion with experts from the Indian smart phone industry and consumers. Integrated TOPSIS-MOORA approach is proposed for prioritizing alternatives based on different selected attributes. Five smart phone brands have been selected for evaluation for this methodology. Battery, Camera, Screen Size, Cost and Performance are the top five prioritized factor taken in this study according to consumers point of views. The results obtained by used methodology will be beneficial for the consumers to differentiate between different smart phones of current industries.

Optimal thermochemical material selection for a hybrid thermal energy storage system for low temperature applications using multi criteria optimization technique

This research aims to design and develop a web-based employee performance assessment information system at the Regional Water Supply Company Tirta Argapura, Probolinggo Regency. It employs the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Multi Objective Optimization On The Basis Of Ratio Analysis (MOORA), and Weight Aggregated Sum Product Assessment (WASPAS) methods for performance evaluation. A comparison among these methods is conducted using sensitivity analysis to determine the best-performing method. The system development follows the Rapid Application Development (RAD) methodology, involving requirement planning, user design, construction, and cutover phases. Blackbox and Whitebox testing techniques are employed to ensure the system's functionality and correctness after development. The research findings indicate that the rankings from TOPSIS, MOORA, and WASPAS align closely with manual calculations used as benchmarks during the method implementation for the employee assessment website design. Sensitivity testing reveals sensitivity values of 0.081 for TOPSIS, -5.542 for MOORA, and -5.964 for WASPAS. Consequently, WASPAS shows high sensitivity while TOPSIS remains stable against changes in criteria weights, leading to the conclusion that TOPSIS is the optimal method for assessing employee performance at the Regional Water Supply Company Tirta Argapura, Probolinggo Regency.

Accurate multi-criteria decision making methodology for recommending machine learning algorithm

In this research work, aluminium oxide (Al 2 O 3 ), copper oxide (CuO) and gold (Au) nanofluids are prepared with the volume concentrations of 0.1%, 0.2%, 0.3% and 0.4% nanoparticles and tested in solar flat plate collector to estimate the heat transfer characteristics and collector efficiency. The influence of the input variable such as material (type of nanofluid), nanoparticle concentration and the mass flow rate (such as 0.016 kg/s, 0.033 kg/s and 0.05 kg/s) are studied experimentally. With the aim of determining the best possible heat transfer and the collector efficiency with minimum pressure drop, the parameters were optimised using multi-criterion decision-making (MCDM) optimisation techniques. Considering the rate of heat transfer, collector efficiency and drop in pressure are the objective functions, the prime ranks of the optimised variables were obtained using TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and MOORA (Multi-Objective Optimization on the basis of Ratio Analysis) techniques. Finally, the prediction accuracy of the models and the confidence levels were evaluated and analysed through ELECTRE (ELimination Et Choix Traduisant la REalite) method to create the hypothesis of the experiment. Al 2 O 3 nanofluid with 0.1% and 0.2% volume concentrations of nanoparticle at 0.05kg/s mass flow rate was obtained as best alternatives from others and it shows good agreement between experimental analysis and optimisation techniques. While using, when compared to water, Al 2 O 3 nanofluid with 0.05kg/s containing 0.1% and 0.2% nanoparticle concentrations, the enhancements were found to be 11.25% and 14.45%, respectively, for heat transfer rate; 11.16% and 14.34%, respectively, for collector efficiency; and 22.7% and 37.7%, respectively, for pressure drop across the collector.

The development of renewable energy is not only an urgent solution for addressing climate change but also a driving force for sustainable economic growth. The transition to clean, inexhaustible energy sources not only helps to reduce greenhouse gas emissions and protect the environment but also ensures national energy security, creates employment opportunities, and enhances the quality of life for individuals. Presently, various technologies exist for sustainable energy development, each characterized by multiple criteria, complicating the evaluation of their performance. This study presents a straightforward method for identifying the best option among eight sustainable energy development alternatives: hydropower, geothermal, biomass, wind, solar, concentrated solar power, coal technology, and oil-fired power plants, each of which is characterized by 17 distinct criteria. The simple method utilized is the Preference Selection Index (PSI) method, which eliminates the need for criteria weighting. This absence of criteria weight calculation in the PSI method distinguishes it from other ranking techniques that typically require such calculations. Therefore, the PSI method significantly simplifies the comparison of the available options compared to other ranking methods, as it bypasses the need for criteria weight calculations. The optimal option identified through the PSI method was also compared with the optimal option identified using 6 other methods: Multi Atributive Ideal Real Com parative Analysis (MAIRCA), Evaluation Based on Distance from Average Solution (EDAS), COmplex PRroportional ASsessment (COPRAPS), Multiobjective Optimization On the basis of Ratio Analysis (MOORA), Proximity Indexed Value (PIV), and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Notably, all employed methods consistently identified geothermal energy as the optimal choice.

This study developed an approach to assess the vulnerability to climate change and variability using various group multi-criteria decision-making (MCDM) methods and identified the sources of uncertainty in assessments. MCDM methods include the weighted sum method, one of the most common MCDM methods, the technique for order preference by similarity to ideal solution (TOPSIS), fuzzy-based TOPSIS, TOPSIS in a group-decision environment, and TOPSIS combined with the voting methods (Borda count and Copeland’s methods). The approach was applied to a water-resource system in South Korea, and the assessment was performed at the province level by categorizing water resources into water supply and conservation, flood control and water-quality sectors according to their management objectives. Key indicators for each category were profiled with the Delphi surveys, a series of questionnaires interspersed with controlled opinion feedback. The sectoral vulnerability scores were further aggregated into one composite score for water-resource vulnerability. Rankings among different MCDM methods varied in different degrees, but noticeable differences in the rankings from the fuzzy- and non-fuzzy-based methods suggested that the uncertainty with crisp data, rather widely used, should be acknowledged in vulnerability assessment. Also rankings from the voting-based methods did not differ much from those from non-voting-based (i.e., average-based) methods. Vulnerability rankings varied significantly among the different sectors of the water-resource systems, highlighting the need to assess the vulnerability of water-resource systems according to objectives, even though one composite index is often used for simplicity.

Application of MCDM technique for selection of fuel in power plant