MultiAttributive Ideal-Real Comparative Analysis Research Articles

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

Hybrid outranking of geospatial data: Multi attributive ideal-real comparative analysis and combined compromise solution

Identifying mineralization areas with an acceptable level of reliability is a complex matter demanding the implementation of supervised methods for mapping mineralization exploration aims. In this study, further collecting all available exploratory information and data of the region of study, their good processing and analysis, and then integrating them with an appropriate method, multi-criteria decision-making (MCDM) approaches were utilized to outranking porphyry Cu mineralization areas. This paper describes an acceptable procedure for obtaining evidential layers for recognizing porphyry Cu mineralization, assigning weight to the layers, and combining them. For this aim, first, a data-driven multi-class index overlay (DMIO) approach is applied as it is a proven method with proper results in mineral prospectivity mapping (MPM) to integrate evidential layers (criteria and sub-criteria emanated from geoscience data including geological, geochemical, remote sensing, and geophysical datasets). After that, two recent MCDM models, combined compromise solution (CoCoSo) and multi attributive ideal-real comparative analysis (MAIRCA), were used to prioritize the porphyry Cu potential areas producing MPM. Concentration-area (C–A) fractal method and prediction-area (P–A) plots by considering the areas of occurrence of the known mineralization and normalized density (Nd) as traditional methods were applied for weighting and integration evidential layers and evaluation of the final maps in a data-driven manner. Consequently, verifying the verisimilitude of the MAIRCA prospectivity map (Nd = 3.17) is similar to the DMIO map in delimiting the priority areas. The results of the CoCoSo model (Nd = 2.7) show this methodology was also successfully applied in mapping the porphyry Cu mineralization areas in the study.

Multi-Criteria Decision Making in the PMEDM Process by Using MARCOS, TOPSIS, and MAIRCA Methods

Multi-criteria decision making (MCDM) is used to determine the best alternative among various options. It is of great importance as it hugely affects the efficiency of activities in life, management, business, and engineering. This paper presents the results of a multi-criteria decision-making study when using powder-mixed electrical discharge machining (PMEDM) of cylindrically shaped parts in 90CrSi tool steel. In this study, powder concentration, pulse duration, pulse off time, pulse current, and host voltage were selected as the input process parameters. Moreover, the Taguchi method was used for the experimental design. To simultaneously ensure minimum surface roughness (RS) and maximum material-removal speed (MRS) and to implement multi-criteria decision making, MARCOS (Measurement of Alternatives and Ranking according to Compromise Solution), TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution), and MAIRCA (Multi-Attributive Ideal–Real Comparative Analysis) methods were applied. Additionally, the weight calculation for the criteria was calculated using the MEREC (Method based on the Removal Effects of Criteria) method. From the results, the best alternative for the multi-criteria problem with PMEDM cylindrically shaped parts was proposed.

Modification of the Logarithm Methodology of Additive Weights (LMAW) by a Triangular Fuzzy Number and Its Application in Multi-Criteria Decision Making

The Logarithm Methodology of Additive Weights (LMAW) method is a very young method and in its basic form is defined for crisp values. In this paper, the LMAW method was improved by being modified with triangular fuzzy numbers. The modification significantly improved the capacity of the LMAW method to consider uncertainty in decision making. The special importance of the method is reflected in a relatively simple mathematical apparatus due to which it is possible to define, with high quality, weight coefficients of criteria and rank alternative solutions in uncertain environments. The method was tested in solving the problem of the location selection for a landing operations point (LOP) in combat operations of the army. The validation of the obtained results was performed: (1) by means of comparison with the Fuzzy Simple Additive Weighting (FSAW) Method, the Fuzzy Multi-Attributive Border Approximation area Comparison (FMABAC), the fuzzy Višekriterijumsko KOmpromisno Rangiranje (FVIKOR), the fuzzy COmpressed PRoportional ASsessment (FCOPRAS), and the fuzzy Multi Attributive Ideal-Real Comparative Analysis (FMAIRCA); (2) by means of sensitivity analysis by changing the weight coefficients of criteria; and (3) using simulation software. In comparison with other methods, the quality of the ranking of alternative solutions was confirmed, which highlighted the special importance of the fuzzy LMAW method relative to that of certain standard methods, respectively, the ones that are often used and confirmed in practice. On the other hand, the sensitivity analysis, including the changing of the weight coefficients of criteria, showed that the model could tolerate smaller errors in defining the weight coefficients of criteria, and it provided stable results. Finally, the validation of results achieved with the use of simulation software confirmed the obtained output results. The output results confirmed the quality of the modified method.

Multi attributive ideal-real comparative analysis (MAIRCA) method for evaluating flood susceptibility in a temperate Mediterranean climate

ABSTRACT This study is focused on flood susceptibility evaluation across the Golestan Province, Iran, using novel ensemble models generated by Multi Attributive Ideal-Real Comparative Analysis (MAIRCA) with frequency ratio (FR) and weight of evidence (WOE). As MAIRCA was employed in flood susceptibility assessment for the first time, an attempt has been made to evaluate its capability by comparing the ensemble with multilayer perceptron (MLP) neural network-based models. Ten flood conditioning factors (altitude, slope, aspect, plan curvature, distance from rivers, topographic wetness index, rainfall, soil type, geology, and land use) and 240 flood and non-flood locations were applied for modelling, of which 70% were selected for training and 30% for validation. The results of validation, performed by the receiver operating characteristics curve method, indicate that the highest predictive accuracy was obtained by MLP-WOE (0.926), followed by MLP-FR (0.912), MAIRCA-WOE (0.885), and MAIRCA-FR (0.859). High precision of the models implies their capability in flood risk prediction.

Plithogenic CRITIC-MAIRCA Ranking of Feasible Livestock Feeding Stuffs

The objective of any decision-making method in a plithogenic environment is to make an optimal ranking of the alternatives subjected to core essential criteria. The preference for plithogenic decision-making methods is gaining momentum in recent times as plithogenic representations are more comprehensive and efficient in handling uncertain and imprecise decision-making data. In this paper, a plithogenic CRITIC-MAIRCA decision-making model is developed and applied to decision-making on livestock feeding stuff. A total of 20 alternatives under three major feed categories of green fodder, subsidiary fodder and concentrate feed are ranked using MAIRCA (The Multi Attributive Ideal-Real Comparative Analysis) method and the criterion weights are determined using the method of CRITIC (CRiteria Importance Through Inter-criteria Correlation). The final results of the plithogenic ranking are compared with fuzzy and crisp ranking methods and it is observed that the plithogenic CRITIC-MAIRCA method is highly efficient in making a feasible ranking.

Fuzzy Decision Model: Evaluating and Selecting Open Banking Business Partners

The finance supply chain has always been a different supply chain compared to product supply chain being a service supply chain. Open Banking (OB) is one of the most important milestones since the beginning of financial technology innovation and service supply chain. As these are activities provided by traditional banks, non-bank financial institutions also provide financial service with access to consumer banking, transactional and other financial data to develop financial applications and services tailored to their customers. The development of financial technology, “Open banking”, promotes financial services to begin this transformation. However, evaluating and selecting open banking business partners from multiple perspectives for banks are underexplored. Thus, the authors in this research proposed a hybrid multi-criteria decision-making model which includes a Spherical Fuzzy Analytical Hierarchy Process (SF-AHP) model and a Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA) model with supports from seasoned domain experts. The contribution of this research is a proposition of a fuzzy decision model for evaluating and selecting open banking business partners. The model successfully determined a suitable open bank provider in order to assist decision makers decide objectively.

Evaluating biological inspiration for biologically inspired design: An integrated DEMATEL‐MAIRCA based on fuzzy rough numbers

Biological inspiration evaluation has been widely acknowledged as one of the most important phases in biologically inspired design (BID) as it substantially determines the direction of the following-up design activities. However, it is inherently an interdisciplinary assessment, which includes both the engineering domain and the biological systems. Due to the lack of knowledge at the early stage of product design, the risk assessments mainly depend on experts' subjective judgments, which values are vague, imprecise, and even inconsistent. How to objectively evaluate the biological inspiration under such uncertain and interdisciplinary scenarios remains an open issue. To bridge such gaps, this study proposes a fuzzy rough number extended multi-criteria group decision-making (MCGDM) to evaluate the biological inspiration for BID. A fuzzy rough number is introduced to represent the individual decision maker's risk assessment and aggregate respective evaluation values within the decision-making group. A fuzzy rough number extended decision-making trial and evaluation laboratory is presented to determine the criteria weights and a fuzzy rough number extended multi-attribute ideal real comparative analysis is proposed to rank the candidate biological inspirations. Experimental results and comparative analysis validate the superiority of the proposed MCGDM in handling the subjectivity and uncertainty in biological inspiration evaluation.

A Fuzzy AHP-MAIRCA Model for Overtourism Assessment: The Case of Malaga Province

The unexpected outbreak of COVID-19 has placed the tourism sector in an unfavourable position. It has altered the reality of destinations worldwide, changing the paradigm of tourism and showing an unusual scenario within the tourism sector, the undertourism. This paper confirms how this global pandemic has put the Spanish tourism sector at a halt, making it essential to establish public–private response strategies that reactivate it and, in turn, avoid overtourism in the post-COVID-19 scenario. This is done to emphasise the necessity of embracing an adapted systematic approach that entails a realistic set of factors to quantitatively measure the impact of overtourism on the local community. In this sense, a fuzzy modified hybrid multi-criteria decision-making (MCDM) approach is proposed, combining the analytical hierarchy process (AHP) model, the multi-attribute ideal–real comparative analysis (MAIRCA) model, and the sensitivity analysis. The modified F-AHP model is used to identify the criteria importance. For the final ranking of the considered alternative municipalities, the modified F-MAIRCA is applied. To validate the applicability of the proposed approach, we designated the province of Malaga (Costa del Sol) as an empirical study. The findings show that the municipality of Nerja was the most affected by overtourism in the pre-pandemic scenario, and today it is found in undertourism. This calls for transformative decisions of fully engaged tourism policymakers and practitioners to thrive in the post-COVID-19 tourism scenarios in terms of sustainability.

An integrated interval type-2 fuzzy sets and multiplicative half quadratic programming-based MCDM framework for calculating aggregated risk ranking results of failure modes in FMECA

Failure modes, effects and criticality analysis (FMECA) is a popular methodology among the safety, reliability, and risk engineers, which can identify the potential failure modes of a system, process, or design, evaluate their cause(s), and rank them according to their criticality. The traditional risk priority number (RPN)-based risk ranking approach has multiple limitations, and researchers have been employing multi-criteria decision making (MCDM) methods to address those drawbacks. In this work, a novel integrated framework is proposed with threefold contributions. Firstly, to minimize the associated linguistic uncertainties during the evaluations of failure modes with respect to the risk factors, the concept of interval type-2 fuzzy sets (IT2FSs) is used. Secondly, to portray the causal dependencies among the risk factors and to compute their weights, an extended IT2F-DEMATEL (Decision Making Trial and Evaluation Laboratory) method is proposed for the group decision-making scenario. Thirdly, the risk ranking results of failure modes are calculated by proposing the concepts of IT2F-MAIRCA (Multi-Attributive Ideal Real Comparative Analysis), IT2F-MARCOS (Measurement of Alternatives and Ranking according to COmpromise Solution) methods, and modified IT2F-TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) methods. Further, after observing that each of the proposed method calculates different ranking results of failure modes, the concept of half-quadratic (HQ) minimization is used to calculate the aggregated ranking results along with the consensus index and trust level. The potential of the integrated framework is highlighted by considering a benchmark FMECA example of a process plant gearbox. Finally, sensitivity analyses are carried out to observe the robustness of the proposed framework, and each of the developed method.

Novel Fuzzy Composite Indicators for Locating a Logistics Platform under Sustainability Perspectives

The purpose of this paper is to help decision-makers choose the location of a logistics platform with sustainability perspectives. This study presents a compensatory and partially compensatory approach to build composite indicators, using mainly fuzzy multi-criteria decision-making methods. In the first instance, the fuzzy full consistency method (F-FUCOM) was used to calculate the weight of the criteria and sub-criteria. In the second instance, two aggregation methods, namely the fuzzy multi-attribute ideal-real comparative analysis (F-MAIRCA) and the fuzzy preference ranking organization method for enrichment evaluation (F-PROMETHEE), were used to rank the location of a logistics platform. The novelty of the work lays in studying the impact of limited sustainability and weak sustainability on the location of a logistics platform. In this respect, the aggregation of various sustainability criterion in fuzzy compensatory and partially compensatory composite indicators is an innovative and interesting approach used to locate a logistics platform. The obtained results show that economic sustainability is the most important criterion for the selection of a logistics platform, followed by the environmental criterion. Obviously, the F-MAIRCA and F-PROMETHEE methods provided the same ranking orders. Finally, sensitivity analyses were performed to validate the robustness of the proposed approach.

A novel MADM approach to the ground-handling agent selection problem in B2B markets

PurposeGround-handling services are important for effective aircraft operations in the air transportation system. Airlines often outsource these services to ground-handling agents through business-to-business (B2B) marketing decisions. Therefore, this paper aims to address the problem of ground-handling agent selection in the airline industry.Design/methodology/approachA real-world case study was carried out to demonstrate the applicability of the integrated best worst method and fuzzy multi-attribute ideal real comparative analysis (F-MAIRCA) approach to solve ground-handling agent selection problems under uncertainty and imprecision. A two-stage sensitivity analysis was also conducted to ensure the credibility and validity of the application.FindingsIn the weighting stage, “Quality” was determined as the most important criterion in terms of supplier performance. With regard to the performance of the ground-handling agents, A2 was found as the optimal supplier in terms of both credibility and validity.Practical implicationsThis study enumerated several criteria that ground-handling agents must meet in order to effectively supply services for the airlines. In addition, this study provides a novel framework from which managers can gain additional benefits from their businesses. Finally, it is concluded that this approach will help airline managers quantitatively in choosing the most appropriate ground-handling agent.Originality/valueThe contributions of this study to the existing literature are twofold. First, we propose a novel multiple attribute decision-making approach to address the problem of supplier selection for airlines under uncertainty and imprecision. Second, the selection of ground-handling agents from the B2B perspective is addressed for the first time in literature.

Evaluation of the Effect of COVID-19 on Countries’ Sustainable Development Level: A comparative MCDM framework

The extent of the outbreak of coronavirus disease (COVID-19) had a major impact on health, social life, economic and environmental activities in almost every country over the world. It has disrupted the sustainable development of countries and brought many uncertainties for their future capabilities. In this study, the effects of the COVID-19 on OECD countries' sustainable development were investigated, and the sustainable development performance of the countries was evaluated by the MultiAttributive Ideal-Real Comparative Analysis (MAIRCA) method. Data for the second quarter of 2020 and the same quarter of the previous year is considered. Then, the results obtained by the MAIRCA method were compared with two different multi-criteria decision-making (MCDM) methods called MABAC (Multi‐Attributive Border Approximation Area Comparison) and WASPAS (Weighted Aggregated Sum Product Assessment). The effectiveness and validity of the results obtained from these methods were tested with Spearman's correlation coefficient. Finally, to examine the effect of COVID-19 on the indicators of sustainable development, a non-parametric Wilcoxon signed-rank test was applied. As a result, it was concluded that COVID-19 negatively affected the sustainable development of countries. However, sustainable development performances of developed countries have been observed to be better than developing countries.

Assessment of occupational risks from human health and environmental perspectives: a new integrated approach and its application using fuzzy BWM and fuzzy MAIRCA

Occupational safety issues encountered in the worksite environment are the issues that companies should consider in improving their operations with a view to human health and environmental awareness. Many methods with different rationales have been existed in the literature to prioritize hazards according to their risk levels and to mitigate their consequences. In this study, a new model is developed for occupational risk assessment by merging two well-known multi-criteria decision-making methods named best and worst method (BWM) and multi attribute ideal real comparative analysis (MAIRCA) under fuzzy environment. The proposed model differentiates from other similar models by three aspects. First, it considers severity of a hazard and its associated risk from the human and environmental riskiness perspectives. Secondly, it applies fuzzy BWM (F-BWM) to calculate the relative importance of three risk factors named as “probability, frequency and severity” of traditional Fine–Kinney method. Thirdly, it applies fuzzy MAIRCA (F-MAIRCA) to rank hazards according to their risk level using importance values obtained by F-BWM. To show applicability of the approach, a case study of risk assessment in a marble factory is fulfilled. Additionally, a number of validation studies including benchmarking analysis with fuzzy VIKOR and fuzzy TOPSIS methods; a sensitivity analysis by varying importance weights of risk factors are carried out to highlight the solidity of the proposed approach.

NEIGHBORHOOD SELECTION FOR A NEWCOMER VIA A NOVEL BWM-BASED REVISED MAIRCA INTEGRATED MODEL: A CASE FROM THE COQUIMBO-LA SERENA CONURBATION, CHILE

Nowadays, cities are developing differently according to their needs, limitations and certain strategic plans. Moreover, conurbation areas will be more common in so many countries like Chile when there are two or more cities developing one next to another, leaning on each other. In this atmosphere, typical residents live in a region or a neighborhood based on certain criteria, so they know how and where they are going to live. From another point of view, a newcomer is usually faced with a city full of contrasts which make things completely and surprisingly complicated. In order to illustrate this, a real case was selected based on the research field, qualitative and quantitative (real) data. The Coquimbo-La Serena conurbation and it’s regions as “Comuna (in Chile)” is a really suitable case to show the complexity of the study. In order to face the challenge, a new hybrid Multiple-Attribute Decision-Making (MADM) method is introduced based on the Best-Worst Method (BWM) and Multi-Attributive Ideal-Real Comparative Analysis (MAIRCA). The five best different neighborhoods as Comunas of the conurbation were analyzed based on the two main scenarios: having a private car or using only public transportation. To obtain more reliable results, a sensitivity analysis was made so as to determine the behavior of the proposed model against weight changes. Besides, the final results were compared with the other MADM methods, for example: Multi-Attributive Border Approximation Area Comparison (MABAC), VIsekriterijumsko KOmpromisno Rangiranje (VIKOR) and COmbinative Distance-based ASsessment (CODAS).

A novel decision-making approach for light weight environment friendly material selection

Abstract Since the last few decades, the use of light weight automotive is increasing day by day to reduce fuel consumption and vehicle emissions. Now days, the automotive industries are emphasizing more on the use of light weight environment friendly materials (LWEFMs) including aluminium, polymers, magnesium, steel blends and composites due to their several inherent advantages, environmental concerns, government regulations and consumer demand. Light weight metal alloys are much preferable for their low density and high specific strength, as well as other attractive characteristics like high corrosion resistance, dimensional stability, energy saving ability and sustainability. However, deciding on the most competent LWEFM for a distinct application considering multi-perspective criteria is not an easy task to perform and restrain a great challenge for the design engineers. On the other hand, improper material selection often leads to premature product failure with reduced efficiency and poor product performance, thereby negatively affecting the productivity, profitability and status of the organization. In this paper, a humble endeavour is taken to propose a novel and contemporary decision-making approach for LWEFM selection in which criteria weights are computed using the Entropy technique and ranking of the alternatives is determined by an almost unexplored multi-attributive ideal real comparative analysis (MAIRCA) method. Seventeen candidate materials are considered as alternatives for the evaluation with respect to thirteen criteria for an automotive application. It has been observed that Ultra High Strength Steel emerges out as the best material for the considered case study. Comparison with other existing well established methods has also been performed to prove the feasibility and validity of the proposed approach

Multi-criteria FUCOM-MAIRCA model for the evaluation of level crossings: case study in the Republic of Serbia

A level crossing, as a point of the crossing of road and rail traffic in the same level, is a place of conflicts subject to traffic accidents. In Serbia, the selection of the level crossings to be secured is mostly done based on the media and society pressure, as a result of an increase of the number of accidents at level crossings. In Serbia, there is no defined methodology for the evaluation of level crossings, respectively, for the development of a strategy for making relevant decision. This paper presents the application of a group multi-criteria FUCOM-MAIRCA (Full Consistency Method – Multi Attributive Ideal-Real Comparative Analysis) model that supports the process of selecting a level crossing in terms of investing in its security equipment. The FUCOM-MAIRCA multi-criteria model is tested in a case study which included the evaluation of ten level crossings within the railway infrastructure in the Republic of Serbia. The evaluation of the crossings is carried out through the assessment according to seven criteria set out on the basis of representative literature and surveys of experts. Sensitivity test of the FUCOM-MAIRCA model is performed by changing the weight coefficients of criteria and statistically processing the results using Spearman’s rank correlation coefficient.