A comparison of TOPSIS, grey relational analysis and COPRAS methods for machine selection problem in the food industry of Turkey

Multi criteria decision making through TOPSIS and COPRAS on drilling parameters of magnesium AZ91

Evaluation of entropy-coupled multi-criteria decision-making methods for enhancing machinability

Determining the optimal intermix of various input parameters for an end milling process is an important task to explore its fullest performance along with the achievement of the desired responses. Several multi-criteria decision-making techniques, like technique for order preference by similarity to ideal solution (TOPSIS), grey relational analysis and multiobjective optimisation on the basis of ratio analysis (MOORA), have been applied for solving parametric optimisation problems of milling processes while assigning equal importance to the considered responses. In this paper, the optimal combinations of cutting speed, feed rate and depth of cut of an end milling process during the machining of Al 1070 alloy are determined using the weighted sum model (WSM), weighted product model (WPM), weighted aggregated sum product assessment (WASPAS), MOORA, TOPSIS, evaluation based on distance from average solution (EDAS), additive ratio assessment (ARAS) and complex proportional assessment (COPRAS) methods with the allocation of unequal weights to the responses determined by standard deviation and entropy techniques. The derived solutions are also compared when the same importance is assigned to the responses. From the results, a parametric combination of 255 rpm cutting speed, 82 mm/min feed rate and 0.75 depth of cut is identified by WSM, WPM, WASPAS, COPRAS, TOPSIS and ARAS methods to achieve the optimal values of material removal rate (MRR) and average surface roughness (Ra) as 595.23 mm3/min and 13.79 µm, respectively. It is also noticed that both feed rate and depth of cut significantly influence MRR and Ra during the machining of Al 1070 alloy.

Application of MCDM technique for selection of fuel in power plant

Nowadays, where Industry 4.0 is discussed extensively, the selection of industrial robots has become an important issue. These robots enable production companies to produce higher quality products with high efficiency and in a cost-effective manner. However, an incorrect selection of these robots can cause significant losses for companies. Various factors need to be considered for the effective selection of industrial robots. In this study, a decision model is presented for industrial robot selection. Quality function deployment (QFD), a well-known and powerful tool that converts customer requirements into final design characteristics, is used in this study, with Group Decision Making (GDM) perspective. In GDM, decision-makers who have different backgrounds or ideas can state their preferences in various formats. The Multiple Preference Relations (MPR) technique is used to combine different assessments. Therefore, this study combines QFD with MPR to handle the different forms of information while calculating the customer requirements importance. Furthermore, the Complex Proportional Assessment (COPRAS) method is used to choose the most suitable industrial robot for the proposed study. The presented method was analyzed in a case study on the robot selection problem for the assembly line of a company operating in the manufacturing industry. The alternatives evaluated with the COPRAS method were also applied with the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) method. The results of both methods were compared and found to be consistent.

Aiming to determine the quantitative sapling characteristics that are important for sapling nursing methods, the present study was carried out on 2-year-old naked root Anatolian Black Pine (Pinus nigra Arnold. subsp. pallasiana (Lamb.) Holmboe) saplings obtained from four different seed origins from Denizli Forest Sapling Nursery Directorate. In this study, the selection of these four seed origins was examined using multicriteria decision-making methods (MCDM) by the determination of quantitative differences between origins and the use of suitable origins in practice. Within the context of the present study, the weight coefficients (significance degrees) of saplings such as height, root neck diameter, and last-year shoot length, and root length were measured using the Criteria Importance Through Intercritera Correlation (CRITIC) method. Secondly, the development performances of these four black pine origins were examined using Grey Relation Analysis (GRA), The Complex Proportional Assessment (COPRAS) and Weighted Aggregated Sum Product Assessment (WASPAS) methods. Besides that, since CRITIC-based GRA, COPRAS, and WASPAS methods showed complete consistency in terms of performance ranking, proportional values were very close to each other, and these methods have positive and very high levels of relationships with each other, it was considered that these methods can be alternative to each other in any measurement ranking or performance measurement. Accordingly, all these multicriteria decision-making methods (GRA, COPRAS, and WASPA) were successfully implemented in origin selection. Given the results obtained, it was determined that, in all three methods, İnceler origin ranked first and Çatak origin ranked last.

Decision making remains a prominent issue in all the problem domains. To make better decisions, multiple factors of the given problem need to be considered and evaluated. Multi-criteria decision-making methods have been used popularly for solving decision-making problems characterized by multiple factors. When multiple factors are considered, it is recommended to categorize the factors into the main criteria and sub-criteria. In this paper, GRAP-an integrated ranking algorithm has been developed by combining Grey Relational Analysis, Rank Sum, and Preference Ranking Organization Method Enrichment Evaluation methods (PROMETHEE) to solve decision-making problems. The weights of the sub-criteria are calculated using the Rank Sum method. Grey Relational Analysis method is used to convert the sub-criteria values into main criteria values in the form of evaluation scores of alternatives. The final ranking scores of the alternatives are obtained using the PROMETHEE method. A decision model is developed using the proposed GRAP algorithm and applied to the Job Profile selection case study. The developed decision model showed much better results compared to other MCDM approaches namely the Simple Additive Weight method, TOPSIS, VIKOR, and Complex Proportional Assessment (COPRAS). Further, a sanity check has been carried out by comparing the results of the decision model with experts’ opinions.

The COPRAS method or Complex Proportional Assessment is a method of decision making that assumes direct and proportional dependence of the significance level of alternative uses. Can determine the best solution to the ratio of the worst ideal solution. The need for new workers in the company is very important. For this reason, careful calculations are needed in making decisions. One of the methods used in managing prospective workers is to use the COPRAS (Complex Proportional Assessment) method. The stages of the COPRAS method are: preparing the attributes to be identified, normalizing the matrix, normalizing the weighted matrix, calculating the maximum and minimum index values, calculating the relative weights, and calculating the quantitative utility of each alternative. The conclusion with the COPRAS method in the Decision Support System Prospective workers according to requests, can simplify the process of making reports

Sustainable building practices face various problematic areas in the European region, such as climate change, high initial costs for sustainable materials and technologies, regulation, sourcing sustainable materials, performance, staff competencies, energy efficiency, waste management, environmental quality and impact, community perception and awareness, integration with existing infrastructure, and others. In order to address these problems, it is essential to adopt appropriate methods and techniques that facilitate informed decision-making and the creation of sustainable strategies for developing building practices. One of the methods for analyzing and developing sustainable building practices is multi-criteria assessment methods that allow for the consideration of multiple criteria simultaneously, providing a more holistic view of the alternatives being assessed. These methods enhance the transparency of the decision-making process by clearly defining the criteria and weights used in the evaluation, and they can be adapted to various building practices. In this study, the COPRAS (Complex Proportional Assessment) method was chosen which is a multi-criteria decision-making method that provides a systematic and quantitative approach for evaluating and ranking alternatives based on multiple criteria and ensures a more objective and data-driven decision-making process. This study aims to analyze the development peculiarities of sustainable building practices in the European region by applying the COPRAS approach and method. In this study, a scientific literature review, the COPRAS method, and expert evaluation were applied. The results of the expert evaluation showed that confidence in the quality and structural properties of secondary materials (traceability) criteria received the highest weight among other criteria. Applying the comprehensive COPRAS method highlighted that assessing the set of criteria is vital for the construction and manufacturing sectors. These findings could be significant for policymakers in developing sustainable building practices.

This study aims to evaluate the members of the Black Sea Economic Cooperation Organization (BSEC) in terms of human capital performance using CRITIC (Criteria Importance Through Intercriteria Correlation) and COPRAS (Complex Proportional Assessment) methods. A hybrid method has been used in analysis. This integrated model consists of a combination of CRITIC and COPRAS methods. The CRITIC method was used to find the objective weights of the criteria. The COPRAS method was used to rank the countries according to their performance. The infant mortality rate (per 1,000 live births), unemployment rate (percentage of the total labour force), average life expectancy at birth, total (years), labour force participation rate (percentage of the total population aged 15-64), current health expenditure (percentage of GDP), internet users (percentage of the total population) and population aged 15-64 (percentage of the total population) are used as criteria for measuring the human capital of countries. 2000, 2005, 2010, 2015, and 2018 data of the countries were used in the study. According to the analysis result, Serbia, Greece, Romania in 2000, Greece, Romania, Bulgaria in 2005, Greece, Russia, Bulgaria, in 2010, Russia, Moldova, Bulgaria in 2015, Russia, Romania and Bulgaria in 2018 are the top three countries with the highest human capital performance. Countries with the lowest human capital performance are as follows: Azerbaijan, Albania, Armenia in 2000 and 2005; Azerbaijan, Armenia, Turkey, in 2010; Turkey, Azerbaijan, Albania in 2015 and 2018.

Decision Support System for Selection of the Best Employees with the Complex Proportional Assessment (Copras) Method. Awards given by the company to the best employees can encourage every employee to always provide the best performance for the company in carrying out their duties and obligations in the company. Decision-making to determine the best employee can be done by the company by assessing the performance that has been carried out by its employees within a certain period. Evaluation of employee performance at the company is influenced by several criteria, namely performance, work attitude, teamwork, work results, and discipline. The decision support system for selecting the best employees uses the Complex Proportional Assessment (Copras) method, where the decision-making process is carried out by assessing choices based on predetermined criteria. Calculations from the Complex Proportional Assessment (Copras) method yield results, namely the order of importance of the criteria and the best employee recommendations, where the order of the most important criteria starts from performance, work attitude, teamwork, work results, and discipline. As for the results of the research on selecting the best employee, the A7 alternative with a score of 100 is Deni's name

Interval neutrosophic number (INN), which is descripted by the degree of truth-membership, indeterminacy-membership and falsity-membership of an element, is a powerful information tool with many application areas. The purpose of this study is to develop an extended form of COPRAS (Complex Proportional Assessment) method used for solving the decision making problems in which all the data presented by decision makers is in the form of interval neutrosophic matrix presented by INNs, and the information about criterion weights is partially known or completely unknown. In order to accomplish this goal, a new score function and an accuracy function that consider the decision maker’s risk attitude are defined under a parameter called the risk index to determine the ordering of INNs. Then, based on Maclaurin symmetric mean (MSM) operator that can capture the interrelationships among multi-input arguments, some aggregation operators are defined, such as interval neutrosophic Maclaurin symmetric mean (INMSM) operator and interval neutrosophic weighted Maclaurin symmetric mean (INWMSM) operator. Moreover, some optimization models are established to determine the subjective and objective weights of decision criteria. A numerical problem is solved to demonstrate the effective and feasible structure of the developed method. Then, a sensitive analysis is conducted according to the decision maker’s risk attitude, and the advantages of the developed method are listed. Finally, a comparison analysis is provided to present the relationships among the developed method and existing methods, and some conclusions are given at the end of the study.

The simplified neutrosophic set (SNS) is a useful model to describe the indeterminacy information which widely exists in the real world. In this paper, we develop a multicriteria decision-making (MCDM) method under simplified neutrosophic environment in which the information about weights of criteria is completely unknown, and the decision criterion values take the form of simplified neutrosophic numbers (SNNs). In order to determine the weighting vector of the criteria, we establish an optimization model based on the basic ideal of the traditional gray relational analysis (GRA) method. By solving this model, we get a simple and exact formula which can be used to determine the criterion weights. Moreover, we utilize the dice similarity measure to determine the similarity measures between each alternative decision and the related ideal decisions. Then, based on the traditional GRA method and the technique for order preference by similarity to ideal solution (TOPSIS), some calculation steps are presented for solving a simplified neutrosophic multicriteria decision-making problem with completely unknown weight information. To avoid information loss, this model does not use the aggregation process of decision information. Comparisons of the suggested methodology with other methods are also made. Finally, a numerical example and an experimental analysis are proposed to illustrate the application of the proposed model.

<p>Supplier selection is essential to the business. The risks posed by suppliers will have a significant impact on the company's performance. Each business has distinct supplier features to mitigate this.These characteristics are divided into criteria in supplier selection. In this study, the criteria in supplier selection will be weighted by Best Worst Method (BWM) and comparing the three ranking methods, Complex Proportional Assessment (COPRAS), Multi-Objective Optimization berdasarkan Analisis Rasio (MOORA) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The sample in this study is an ABC manufacturing company engaged in mining from Indonesia. From the results of the study, there were 16 criteria using the Delphi Method. These criteria are divided into four main criteria, namely service, quality, cost and time. From the results of weighting BWM, the price sub criteria on cost criteria have the greatest weight for ABC companies. The results of the weighting are then carried out by supplier ranking by comparing the COPRAS, MOORA and TOPSIS approaches. In comparing these three methods, approaches are used based on accuracy and complexity. The COPRAS method has the highest accuracy and lowest complexity according to the ABC company.</p>

PurposeSelecting a suitable contract to outsource construction projects is an ongoing concern for project managers and organizational directors. This study aims to propose a comprehensive model to manage the risks of outsourced construction project contracts.Design/methodology/approachTo employ the proposed model, firstly, the types of contracts and risks in the organization should be identified, then, to prioritize the contracts, the identified risks are considered as criteria. After receiving the experts' opinions, the best–worst method (BWM) integrated with grey relation analysis (GRA) method was used to prioritize the contracts. BWM and GRA are multi-criteria decision-making methods with different approaches and applications. In the current study, BWM has been employed to calculate the weights of criteria because it has better performance than other methods such as the analytic hierarchy process (AHP). After calculating the weights of criteria, the GRA method has been utilized for ranking the alternatives.FindingsAccording to the results obtained from the case study, the cost plus award fee contract is the most suitable alternative for outsourcing construction projects. The proposed methodology can be practically applied through different types of the projects such as construction or “engineering, procurement and construction”.Originality/valueTo the best of our knowledge, this is the first time a conceptual model has been proposed to select an appropriate contract for construction projects. Also, for the first time, the BWM integrated with GRA method has been used to prioritize project contracts based on the potential risks. The proposed model can contribute to project managers for selecting a suitable contract with the least risk in construction projects.