MultiAttributive Ideal-Real Comparative Analysis Research Articles

Valuation of Internet of Energy (IoE) Platforms in Smart Cities: A Hybrid Multi-Criteria Decision Making Approach

A smart city is a place where people live using computing, sensing, and networking technology in a variety of ways, including smart buildings, smart homes, smart transportation, smart workplaces, smart offices, smart industries, and smart agriculture. These smart technologies need to connect so; we need to use the Internet of Things (IoT) that has applications in many areas of our smart lives. IoT has been used due to the growing usage of renewable energy sources and the challenges of network management. Internet of Energy (IoE) integrates the IoT and smart grid capabilities. The basic role of smart grids or IoE is an essential element in creating smart cities. In this work, we present some IoE platforms that represent the alternatives and their criteria. The proposed study treats the uncertainty of multi-criteria decision-making (MCDM) modified by single value triangular neutrosophic number (SVTrN) by choosing the optimal IoE platform based on the criteria importance through inter-criteria correlation (CRITIC) method and multi-attributive ideal-real comparative analysis (MAIRCA). The proposed model proved its effectiveness by applying it to an experimental case. The results show that the DCC (A3) platform is the best one.

Enhanced MAIRCA technique for green supply chain management based on spherical linear diophantine fuzzy information

Spherical q-linear Diophantine fuzzy sets (Sq-LDFSs) showed a significant improvement to handling uncertainty in multi-criteria decision-making (MADM). It is advantageous for two-parametric data as well as for data with three variable parameters. One of the most crucial functions of supply chain management is to increase competitive pressure. The study’s standout innovation, Multi-Attributive Ideal Real Comparative Analysis (MAIRCA), has been implemented to give powerful group decision-making. An ecological perspective is becoming more prevalent due to the competitive climate and customer perception. Green supplier selection (GSS) has become a significant issue. In this study, we address the problem of GSS, which aims for flexibility, robustness, ecological sensitivity, leanness, and feasibility. The feasibility criteria in recycling, environmental, carbon footprints, and water consumption are different from those in standard supplier selection. The aim of our work is to introduced the weighted Average/Geometric aggregation operators based on Sq-LDFSs. For this we defined some operational rules as a foundation of aggregation operators. Secondly we proposed a MAIRCA approach for Sq-LDFSs to address these issues. The MAIRCA strategy, which uses multi-criteria group decision-making (MCGDM) to evaluate and choose traditional and environmental conventionalities, is used to reduce instability and ambiguity. The spherical q-linear Diophantine fuzzy MAIRCA approach provides comparative analysis of decision-makers and criteria. By merging Sq-LDFS and MAIRCA, a hybrid strategy is formed, successfully selecting the best provider among options based on the order of significance. These numerical examples demonstrate the suggested MCGDM approaches that were applied in actual situations, giving a realistic appreciation of their efficacy. The comparative study of the final ranking further supports the idea that these strategies are dependable in decision-making processes in addition to being practical and usable.

Applications of MCDM methods for the assessment of sustainable development: a case study of fashion textile group

PurposeThe textile industry harms the environment at every stage of production, from the acquisition of raw materials to the disposal of finished products. It is very important for the textile industry to adapt to the basic policies on environmental sensitivity and sustainability to keep up with the transformation in production processes and the rapid changes occurring around the world in order to exist in global competition. Within the scope of sustainable development goals, it is of great importance to measure and evaluate indicators of all processes of the sector. This paper aims to present application of multi-criteria decision making (MCDM) methods for the assessment of sustainable development in textile industry.Design/methodology/approachThe data of a multinational clothing company’s four-year sustainability performance between 2018 and 2021 were evaluated under 22 sustainability parameters determined using two new MCDM techniques, namely the combined consensus solution method and multi-attribute ideal real comparative analysis. In determining the criteria, priority key indicators were determined by taking into account the sector’s relationship with the environment, raw material consumption and social adequacy.FindingsAccording to the application results of both methods, the year 2021 shows the best performance. It has been seen that the sustainability performance of the Inditex group has increased over the years and the results of the applied models support each other. It can be suggested that the proposed approach be applied to evaluate the progress in the textile sector with the relevant data on a particular company or on a macro scale.Originality/valueThis study makes an important contribution to the field in terms of the fact that the methods used are recent and have no application in the field of textiles. It allows the evaluation of different sustainability criteria together using a single method. It is very important to share data on sustainability indicators with customers, employees, suppliers, investors, partner organizations and society and evaluate performance. Analyzing sustainability performance on the basis of annual reports is important in terms of identifying good practices, sharing them with the community and setting an example. In addition, using scientific methods in the evaluation of the sustainability report data published by companies regularly provides significant feedback for policymakers and academics.

G20 Ülkelerinin İnovasyon Performans Analizi: COVID-19 Dönemini İçeren Yeni Bütünleşik LOPCOW-MAIRCA ÇKKV Yaklaşımı

Purpose: This study aims to examine the innovation performance of G20 countries in 2018-2022 with multi criteria decision making methods. When the 5-year performance was analyzed, it was also revealed whether the COVID-19 outbreak has an impact on the innovation performance of the countries.
 Methodology: An integrated LOPCOW (Logarithmic Percentage Change-driven Objective Weighting) - MAIRCA (Multi Attribute Ideal-Real Comparative Analysis) method was applied in the study. First, the indicators representing innovation performance (institutions, human capital, and research, infrastructure, market sophistication, business sophistication, knowledge and technology outputs, creative outputs) was objectively weighted by the LOPCOW method. Then, the innovation performance of G20 countries was calculated with the MAIRCA method. Finally, a comparative analysis was also presented to support the findings.
 Findings: As a result of the innovation performance analysis using multi criteria decision making methods, human capital, and research were found to be the most important indicators, and the United States was found to be the country with the best innovation performance. In the sensitivity and comparative analysis, it was concluded that the integrated LOPCOW-MAIRCA method provides robust outputs.
 Originality: This study makes original contributions by analyzing the impact of the COVID-19 pandemic on the innovation performance of countries considering the 2018-2022 period and the integrated multi criteria decision making methods it uses that have not yet been applied in the literature.

ASSESSMENT OF SUSTAINABLE WASTEWATER TREATMENT TECHNOLOGIES USING INTERVAL-VALUED INTUITIONISTIC FUZZY DISTANCE MEASURE-BASED MAIRCA METHOD

Effective wastewater treatment has significant effects on saving water and preventing unnecessary water scarcity. An appropriate wastewater treatment technology (WWTT) brings economic benefits through reuse in different sectors and benefits the society and environment. This study aims to develop a decision-making framework for evaluating the sustainable WWTTs under interval-valued intuitionistic fuzzy set (IVIFS) environment. The proposed MCDM framework is divided into two stages. First, a new Hellinger distance measure is developed to determine the degree of difference between IVIFSs and also discussed its desirable characteristics. Second, an interval-valued intuitionistic fuzzy extension of multi-attribute ideal-real comparative analysis (MAIRCA) model is developed using the proposed Hellinger distance measure-based weighting tool. Further, the proposed model is implemented on an empirical study of sustainable WWTTs evaluation problem. Sensitivity and comparative studies are made. The results indicate that odor impacts, sludge production, maintenance and operation are the most effective sustainable factors and Microbial fuel cell (MFC) technology is the best WWTT followed by natural treatment methods.

Picture fuzzy prospect theory-MAIRCA method considering optimal reference point for emergency decision-making

In the emergency decision-making process, decision-makers usually cannot give rational evaluations, and existing decision-making methods do not adequately consider the risk attitude of decision-makers either. To solve these problems, a combined method based on the prospect theory and the multi-attributive ideal-real comparative analysis (MAIRCA) method is put forward in the picture fuzzy environment. Firstly, the optimal aggregation (OA) model is proposed to obtain the ideal evaluations with the least disagreement among decision-makers. Regarding the evaluations as reference systems, the OA-based prospect theory is put forward, which could calculate the prospect matrix more reasonably. Secondly, considering the prospect matrix and alternative preference, the improved MAIRCA method is proposed, which overcomes the defects of theory and has the better ranking ability. Then, the OA-based prospect theory-MAIRCA method is further put forward to effectively complete the decision-making process with risk attitudes. Finally, an illustrative example of earthquake emergency assessment and a series of comparative experiments are presented. The analyses of results show that the proposed method has great guiding significance in the field of emergency decision-making management.

Evaluating the blockchain-based healthcare supply chain using interval-valued Pythagorean fuzzy entropy-based decision support system

In the current era, blockchain technology has emerged as a novel technique to maintain the operations of healthcare management systems. The global COVID-19 disease has led to increase in the utilization of technology for healthcare supply chain, patient data management, and claims settlement. Data management in healthcare industry is a complex procedure where multiple organizations offer appropriate supply chain facilities in everyday life. Inadequate data handling disrupts the supply chain, which has a longstanding impact on the healthcare region. Blockchain-based solutions are renowned for their ability to produce safe and traceable results and effective in the health sector for secure data retrieval and storage, resulting in more effectual product creation and tracking. These technologies can deliver data provenance, sponsors candid healthcare industry demands, and certifies the immutability of multi direction transactions. Due to involvement of multiple factors/criteria, the selection of an effective blockchain platform-based healthcare supply chain can be treated as a decision support system (DSS) problem. Thus, the objective of this paper is to present a novel interval-valued Pythagorean fuzzy DSS for evaluating the blockchain platforms for healthcare supply chain and selecting a suitable one with respect to multiple criteria and uncertainty. The proposed DSS framework is divided into three stages. First, some fairly aggregation operators are presented to combine the individual’s information and also discussed their desirable characteristics on interval-valued Pythagorean fuzzy sets (IVPFSs). Second, an integrated weighting tool is developed using entropy-based tool for objective weight of criteria and pivot pairwise relative criteria importance assessment (PIPRECIA) model for subjective weight of criteria with IVPFSs. For this purpose, new entropy is presented on IVPFSs. Third, the multi-attribute ideal-real comparative analysis (MAIRCA) model is developed with the combination of fairly aggregation operators, entropy-based weighting tool and PIPRECIA model with interval-valued Pythagorean fuzzy information. Further, the proposed DSS is used on a case study of blockchain platforms for healthcare supply chain evaluation, which confirms its applicability and usefulness. Sensitivity and comparative assessments are carried out to check the consistency, robustness and efficiency of the presented method.

A hybrid approach based on fuzzy BWM and MAIRCA for selection of engine in rotary wing unmanned aerial vehicle design

The use of Unmanned Aerial Vehicle (UAV) platforms has been increasing day by day and it has become an important technology. In this study, how the engines should be selected in the design of a rotary wing UAV system is considered a multi-criteria decision-making (MCDM) problem. This MCDM problem has not yet been encountered in the literature. Therefore, a hybrid MCDM approach based on the fuzzy Best Worst Method (BWM) and Multi Attributive Ideal-Real Comparative Analysis (MAIRCA) is proposed to solve this problem. In the proposed approach, the decision makers determine 6 criteria (KV value, thrust, weight, efficiency, battery, electronic speed controller (ESC)) and 6 different engine (A1, A2, A3, A4, A5, A6) alternatives. The fuzzy BWM was used to calculate the weights of criteria, while the MAIRCA was used for the selection of alternatives. According to the results obtained, the three most effective criteria were thrust, KV value, and weight, respectively. The three best engine options were found as A3, A1, and A6. Moreover, sensitivity analysis was performed to observe the change in the ranking of alternatives according to different weights of criteria. MABAC, MARCOS, and COPRAS methods were used to compare the alternative rankings found with the MAIRCA.

Evaluation of Enterprise Decarbonization Scheme Based on Grey-MEREC-MAIRCA Hybrid MCDM Method

Engineering and technological breakthroughs in sustainability play a crucial role in reducing carbon emissions. An important aspect of this is the active participation of enterprises in addressing carbon reduction as a systemic approach. In response to government incentives in the People’s Republic of China, Chinese enterprises have developed carbon reduction systems to align their organizational goals with national long-term plans. This paper evaluates the carbon reduction schemes employed by six companies as a multi-criteria decision-making (MCDM) problem. To this end, we propose a new hybrid MCDM method called the grey-MEREC-MAIRCA method. This method combines the recently developed method based on the removal effects of criteria (MEREC) for weighting and multi-attribute ideal-real comparative analysis (MAIRCA) based on the grey system theory. The proposed hybrid method provides the additional benefit of accounting for uncertainty in decision making. Notable findings of this research, based on the decision-maker scores, are that the control of direct carbon emissions and energy-saving efficiency are top priorities. In contrast, committing to corporate social responsibility through carbon public welfare and information disclosure are considered lesser priorities. Furthermore, the ranking results obtained using this method are compared with those from the classical weighted sum model and the technique for order preference by similarity to ideal solution (TOPSIS), confirming the selection of the best company. Despite the limitation of the proposed method and the additional steps needed in the evaluation, it opens up opportunities for future research to develop simpler MCDM methods under uncertainty.

DIBR–DOMBI–FUZZY MAIRCA Model for Strategy Selection in the System of Defense

Strategic management has applications in many areas of social life. One of the basic steps in the process of strategic management is formulating a strategy by choosing the optimal strategy. Improving the process of selecting the optimal strategy with MCDM methods and theories that treat uncertainty well in this process, as well as the application of other and different selection criteria, is the basic idea and goal of this research. The improvement of the process of the aforementioned selection in the defense system was carried out by applying a hybrid model of multicriteria decision-making based on methods defining interrelationships between ranked criteria (DIBR) and multiattributive ideal-real comparative analysis (MAIRCA) modified by triangular fuzzy numbers–“DIBR–DOMBI–Fuzzy MAIRCA model.” The DIBR method was used to determine the weight coefficients of the criteria, while the selection of the optimal strategy, from the set of offered methods, was carried out by the MAIRCA method. This was done in a fuzzy environment with the aim of better treatment of imprecise information and better translation of quantitative data into qualitative data. In the research, an analysis of the model’s sensitivity to changes in weight coefficients was performed. Additionally, a comparison of the obtained results with the results obtained using other multicriteria decision-making methods was conducted, which validated the model and confirmed stable results. In the end, it was concluded that the proposed MCDM methodology can be used for choosing a strategy in the defense system, that the results of the MCDM model are stable and valid, and that the process has been improved by making the choice easier for decision makers and by defining new and more comprehensive criteria for selection.

Evaluate teaching quality of physical education using a hybrid multi-criteria decision-making framework.

The teaching quality evaluation of physical education is an important measure to promote the professional development of physical teachers, improve the quality of school teaching and personnel training. It is helpful for students to achieve all-round development and better meet the needs of modern talents in the new era. This study aims to establish a novel MCDM (multi-criteria decision-making) framework for evaluating teaching quality of physical education. First, PFNs (picture fuzzy numbers) are suggested to reflect dissimilar attitudes or preferences of decision makers. Then, the typical SWARA (Step-wise Weight Assessment Ratio Analysis) model is modified with PFNs to calculate the weights of evaluation criteria. Considering that some criteria are non-compensatory during the evaluation process, the idea of ELECTRE (elimination and choice translating reality) is introduced to obtain the ranking results of alternatives. Specially, the MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method is extended to construct the difference matrix in a picture fuzzy environment. Last, the hybrid MCDM model is utilized to assess the teaching quality of physical education. Its superiority is justified through comparison analyses. Results show that our approach is practicable and can provide instructions for the teaching quality evaluation of physical education.

A Sensitivity Index to Perform the Territorial Sustainability in Uncertain Decision-Making Conditions

The issue of sustainability is central to development strategies. Although this alignment is acknowledged and shared world-wide, territorial development in a sustainable light must also take the ongoing COVID-19 pandemic into consideration, specifically by evaluating the effects of COVID-19 on the global health, social order, and economic-environmental system. The research suggests a sensitivity index to gauge the degree of territorial sustainability taking the COVID-19 pandemic’s impacts into account. A study set of countries, as identified by the Organization for Economic Co-operation and Development (OECD), is used to test the developed index. The index evaluates a country’s performance in terms of economic, social, and environmental sustainability while also considering the relative risk of COVID-19. The proposed index measures territorial sustainability from a variety of angles by enabling comparisons between the circumstances before and after current shocks in the socioeconomic and environmental performance frames by pandemic emergency. The index was created using an integrated assessment method that was based on the Choquet Integral (CI) mathematical framework and Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA). The study establishes a unique and up-scaling methodology for constructing the sensitivity index, significantly advancing the suggestions for sustainable accounting under uncertain circumstances at the territorial scale. Adopting indices that quantify territorial sustainability under uncertainty may help guide policy decisions from an investment programming viewpoint, particularly when it comes to allocating financial resources to the economic sectors most impacted by shock events such as the COVID-19 pandemic.

MACBETH-MAIRCA Plithogenic Decision-Making on Feasible Strategies of Extended Producer's Responsibility towards Environmental Sustainability

The environmental disarray caused by plastic usage alarms the world leaders to join hands in making coordinated efforts in creating a green globe to support environment for the future generation. The responsibility of promoting environmental sustainability is not confined to government alone as all the producer companies have equal share in mitigating the plastic waste. One of the ideal strategies of environmental conservation practiced by many of the nations at recent times is extended producer’s responsibility (EPR). This research work purposes in building a multi-criteria decision-making model integrating the methods of MACBETH (Measuring attractiveness through a categorical-based evaluation technique) and MAIRCA (Multi Attributive Ideal-Real Comparative Analysis) in plithogenic environment to make optimal decisions on the plastic recycling methods subjected to four core criteria in EPR context. The efficiency of the newly developed integrated model is determined by comparing the model with other models integrating MAIRCA with the methods of CRITIC (CRiteria Importance Through Intercriteria Correlation) FUCOM (Full Consistency Method). The sensitivity analysis helps in listing the merits and limitations of the proposed integrated model and also the consistency of the criterion weights and the ranking of the alternatives are checked.

A Lance Distance-Based MAIRCA Method for q-Rung Orthopair Fuzzy MCDM with Completely Unknown Weight Information

The purpose of this manuscript is to develop a novel MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method to solve the MCDM (Multiple Criteria Decision-Making) problems with completely unknown weights in the q-rung orthopair fuzzy (q-ROF) setting. Firstly, the new concepts of q-ROF Lance distance are defined and some related properties are discussed in this paper, from which we establish the maximizing deviation method (MDM) model for q-ROF numbers to determine the optimal criteria weight. Then, the Lance distance-based MAIRCA (MAIRCA-L) method is designed. In it, the preference, theoretical and real evaluation matrices are calculated considering the interaction relationship in q-ROF numbers, and the q-ROF Lance distance is applied to obtain the gap matrix. Finally, we manifest the effectiveness and advantage of the q-ROF MAIRCA-L method by two numerical examples.

Industrial filtration technologies selection for contamination control in natural gas processing plants: A sustainability and maintainability-based decision support system under q-rung orthopair fuzzy set

The challenges of sustainability and maintainability to implement the industrial filtration technologies in the natural gas industry necessitate a systematic and practical assessment. With the target of balance between the issues in the context of sustainability and maintainability policies, criteria (principles) including economic, environmental, social, and maintainability aspects are taken into consideration. This work aims at developing a novel hybrid decision support system (DSS) namely q-rung orthorpair fuzzy set-based MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis). The proposed DSS is applied to prioritize industrial filtration technologies considering the sustainability and maintainability principles. These technologies are considered for controlling contaminants produced by the natural gas industry in Iran. The prioritization of the considered technologies is obtained from the most to least preferred as: Cyclo-filter, Cyclone separator, Backwash, Gravity separator, and Basket strainer, respectively. The Cyclo-filter technology is selected as the preferable alternative. The adaptability of this technology with the regional and local states of the study area has assisted to make it the most suitable technology. The decision-making framework can help to give a new and practical insight for the policymaking in the field of process energy and environmental systems especially in the natural gas industry to control contamination.

Analyzing the champions league teams via decision models

PurposeThe purpose of this study is to evaluate the performance of the Champions League teams using the entropy-integrated Multi Attribute Ideal-Real Comparative Analysis (MAIRCA) and super-slack-based data envelopment analysis for the 2012–2022 period.Design/methodology/approachThis study consists of two sections. First, this study uses the entropy-integrated MAIRCA approach, which is a novel multi-criteria decision-making (MCDM) technique developed by Gigović, to measure the performance of Champions League clubs. Second, this study proceeds with the super-slack-based DEA to evaluate the efficiency of the Champions League clubs.FindingsAs per the empirical results, Real Madrid is found to be the best-performing club over the past 10 years in terms of financial and sportive performance. Over the analyzed period, teams from the five Major Leagues of Europe perform better.Originality/valueTo the best of the authors’ knowledge, performance measurement studies in football have focused on either DEA or MCDM. This study aims to present novelty for football literature by evaluating holistically both the sportive and financial dimensions. This paper also analyzes Champions League teams from the perspective of both MCDM and super-slack-based DEA methods.

A study on multi-criteria decision-making in powder mixed electric discharge machining cylindrical shaped parts

In life as well as in engineering, many times, it is necessary to choose the best option among many different options. That will be more difficult when the criteria given for the selection contradict each other. For example, when external cylindrical grinding, the minimum surface roughness requirement necessitates a small depth of cut and feed rate. The material removal rate will be reduced in this case, and this requirement will conflict with the maximum material removal rate requirement. To solve the above problem, a very useful tool is multi-criteria decision-making (MCDM). In this paper, for the first time, MCDM results for powder mixed discharge machining (PMEDM) cylindrical parts of SKD11 tool steel with copper electrodes have been presented. In this work, eighteen experiments with the L18 (16×53) design using the Taguchi method were conducted. Six main input process parameters include the powder concentration, the pulse current, the servo voltage, the pulse on time, and the pulse off time. To select an alternative that simultaneously ensures two criteria including minimum surface roughness (RS) and maximum material removal speed (MRS), four different MCDM methods including MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), TOPSIS (Technique for order of preference by similarity to ideal solution), and EAMR (Area-based Method of Ranking) and two methods of criteria weight calculation including MEREC (Method based on the Removal Effects of Criteria) and Entropy methods were selected. The results of MCDM when PMEDM SKD11 tool steel cylindrical parts with two methods for weight determination and four methods for solving MCDM problem were evaluated. In addition, the best alternative to ensure simultaneous minimum RS and maximum MRS was proposed.

Evaluation of Cryptocurrencies for Investment Decisions in the Era of Industry 4.0: A Borda Count-Based Intuitionistic Fuzzy Set Extensions EDAS-MAIRCA-MARCOS Multi-Criteria Methodology

Blockchain technologies, which form the basis of Industry 4.0, paved the way for cryptocurrencies to emerge as technological innovation in the technology age. Recently, investors worldwide have been interested in cryptocurrencies with increasing acceleration due to high earning expectations though they have no backing and intrinsic value. As such, this paper seeks to identify the most proper cryptocurrencies from an investment standpoint in our technological era. Fifteen well-known cryptocurrencies with the highest market capitalization are evaluated as per sixteen factors. An intuitionistic fuzzy set-driven methodology incorporating Evaluation Based on Distance from Average Solution (EDAS), Multi-Attributive Ideal Real Comparative Analysis (MAIRCA, and Measurement of Alternatives and Ranking according to COmpromise Solution (MARCOS), which is the study’s prominent novelty, has been applied to provide a strong group decision vehicle for cryptocurrency selection. Notwithstanding, although the results obtained with the three approaches are highly consistent, investors would not like to doubt the instrument they will invest in. The Borda count is then applied to obtain a compromise for the rankings obtained from each approach. As per our findings, Ethereum, Tether, and Bitcoin are the most suitable cryptocurrencies, whereas reliable software, ease of inclusion in the wallet, and stability are the foremost factors to consider when investing in cryptocurrencies. The findings are further discussed in detail from a financial perspective. The proposed approach could be employed to select different investment instruments in future studies.

Selection of Plant Location for Hemp Fibre Production in TR72 Region

This study has emerged within the scope of the sectoral studies requested by the Ministry of Industry and Technology of Turkish Republic from the Development Agencies, and aims to select the optimal plant location among the organized industrial zones in the TR72 Region consisting of Kayseri, Sivas and Yozgat provinces in order to produce processed fibre from the raw fibre obtained as a by-product of hemp cultivation. The criteria such as parcel unit price, electricity unit price, service water unit price, waste water unit price, natural gas unit price, insurance premium employer's share support period, investment contribution rate for insurance premium employer’s share support, tax reduction rate, investment contribution rate for tax reduction, interest or profit share support, access to raw material sources, access to market and access to qualified labour supply that form the basis for the selection of plant location were determined and weighted according to the Stepwise Weight Assessment Ratio Analysis (SWARA) method The most available plant location among the emerging alternatives was determined by using Multi-Criteria Decision Making Methods consisting of Combined Comprimise Solution (COCOSO), Complex Proportional Assessment (COPRAS) and Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA). Consequently, it was concluded that Kaleseramik Private Organized Industrial Zone (OIZ) located in Yozgat province is the most available plant location among the other seven alternatives.

Application of topsis, mairca and EAMR methods for multi-criteria decision making in cubic boron nitride grinding

Determining the best cutting mode is a common problem for machining processes as well as for CBN (Cubic Boron Nitride) grinding on Computer Numerical Control (CNC) machines. It is even more important when it is necessary to choose a solution that meets many goals, which are in conflict. This paper presents the results of a multi-criteria decision-making (MCDM) study on CBN grinding of cylindrical-shaped parts on CNC milling machines. Three MCDM methods, including TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), MAIRCA (Multi-Attributive Ideal-Real Comparative Analysis), and EAMR (Evaluation by an Area-based Method of Ranking) were applied in this work. Besides, MEREC (Method based on the Removal Effects of Criteria) and Entropy methods were used to determine the weights of the criteria. In addition, the Taguchi method with L18 orthogonal array (6^1+3^3) design was used for the design of an experiment, which has four input factors including the depth of dressing cut, the spindle speed, the feed rate, and the wheel diameter. Two criteria, including the surface roughness (SR) and the material removal speed (MRS) were selected as the response outputs. The reason for choosing these two criteria is because SR and MRS are two very important output factors of a mechanical machining process as well as of the CBN grinding process on a CNC milling machine. In particular, these two criteria are always in conflict with each other. Small SR requirements will require small values of the feed speed and the depth of cut. This will lead to the reduction of MRS. From the results of this study, the use of different methods for MCDM was evaluated. In addition, rankings of alternatives have been given according to MCDM methods. Furthermore, the best alternative to guarantee both the minimum SR and the maximum MRS has been found