MultiAttributive Ideal-Real Comparative Analysis Research Articles

Occupational health and safety performance evaluation of countries based on MAIRCA

Purpose The purpose of this paper is to develop an approach to compare occupational health and safety (OHS) performances of countries. Additionally, another aim is to debate the impacts of using recorded data and ratios for OHS performance evaluations. Design/methodology/approach The number of fatal accidents (NFAs), fatal accident rates (FARs), numbers of lost days (NLDs) and accident severity rates (ASRs) are determined as main criteria and six economic activity areas (EAAs) are considered as sub-criteria. Two different initial decision matrices are used as the initial decision matrix, the first of which consists of recorded data of countries related to NFAs and NLDs, and the second consists of FAR and ASR values as ratios. Importance weights of main and sub-criteria regarding the recorded data and ratios are determined using four different weighting ways. Countries are ranked via utilizing Multi-Attributive Ideal-Real Comparative Analysis considering two different initial decision matrices. Findings It can be stated that an evaluation based on ratios for comparison of OHS performance provides more realistic results. Additionally, increasing the effect of the FAR values using the 6,000 equivalent lost days factor is also important in terms of differentiating the data of the countries in question. Originality/value To the best of the authors’ knowledge, there is no study in literature that discusses the ranking of countries by means of recorded data and ratios considering different criteria. Additionally, this study is a first in terms of the number of countries evaluated and the comparison of these countries according to their respective EAAs.

An integrated approach for fuzzy failure modes and effects analysis using fuzzy AHP and fuzzy MAIRCA

Failure mode and effect analysis (FMEA) is a proactive risk assessment technique, which has been widely used by engineers to meet the safety and reliability requirements of processes, products, structures, services, and systems. The major aim of the FMEA technique is to rank the failure modes according to their risk levels and subsequent actions are performed to eliminate/mitigate their consequences. In a typical FMEA, for each failure mode, three risk factors, namely severity (S), occurrence (O) and detection (D) are evaluated and a risk priority number (RPN) is estimated by multiplying these risk factors. In recent years a significant effort has been underway and different approaches have been proposed to improve FMEA, to overcome its several drawbacks. We notice that there is a significant amount of literature based on multi-criteria decision making (MCDM) methods, which have been solely aimed to improve the risk estimation process in FMEA by overcoming the drawbacks of the traditional FMEA technique. In this work, we propose a novel integrated MCDM approach by combining Fuzzy Analytical Hierarchy Process (FAHP) with the modified Fuzzy Multi-Attribute Ideal Real Comparative Analysis (modified FMAIRCA). At first, we calculate the fuzzy relative importance between the risk factors by using the FAHP method and then we use those importance values in our proposed modified FMAIRCA to rank the failure modes according to their risk level. Our modified FMAIRCA method is computationally inexpensive and is able to provide more viable decisions. We consider a benchmark example in FMEA domain to validate the ability of our integrated approach and highlight the usefulness of the same. Further, we compare the ranking result with other MCDM methods - FVIKOR, FCOPRAS, FMOORA, FMABAC, FTOPSIS and sensitivity analysis is also performed to highlight the robustness of the proposed approach.

A novel hybrid multi-criteria group decision making approach for failure mode and effect analysis: An essential requirement for sustainable manufacturing

Modern manufacturing organizations have started giving paramount importance to sustainable aspects of the manufacturing processes, realizingnot only that the natural resources are dwindling rapidly but also that they bear significant responsibility to the society and surroundings for the overall future development. Catastrophic failures and the maintenance of complex equipment can generate a large amount of hazardous waste within the organization that can affect the overall production level, environment, along with impacting the health of workers in the long run. Failure mode and effect analysis (FMEA) is an efficient risk analysis tool for processes, products, designs or services and has been adopted by different types of organizations. In this paper, for the first time in the literature, the consequences of failure modes of industrial equipment are considered from the sustainable point of view, which is believed to be a requirement for the establishment of a successful sustainable manufacturing strategy. Severities of failure modes are considered from environmental, societal and economic points of view, along with the chances of occurrences and detections. However, due to lack of exact data, these risk factors are evaluated linguistically by cross-functional experts, which made the situation complex. To properly prioritize the failure modes according to their risk levels, a novel hybrid Multi-Criteria Group Decision Making (MCGDM) approach by integrating Interval Type-2 Fuzzy Decision-Making Trial and Evaluation Laboratory (IT2F-DEMATEL) and Modified Fuzzy Multi-Attribute Ideal Real Comparative Analysis (Modified FMAIRCA) methods is proposed. Calculating the causal dependencies among the risk factors and finding out their relative importance are the twofold benefits of the IT2F-DEMATEL approach. Defuzzified criteria weights are further utilized in the proposed modified FMAIRCA approach for risk ranking of failure modes. The effectiveness of the proposed hybrid approach is demonstrated by considering a case-study from a process plant gearbox. Next, the obtained ranking results are compared with the results obtained from other commonly applied fuzzy MCDM methods in the FMEA domain. Stability and robustness of the proposed approach is also highlighted by performing sensitivity analysis.

New integrated approaches based on MC-HFLTS for healthcare waste treatment technology selection

Purpose Selecting the most appropriate healthcare waste treatment technology (HCWTT) is an uncertain and complex decision-making problem because there exist more than one alternative and many conflicting qualitative and quantitative criteria. However, the use of fuzzy and comparative values, instead of specific crisp values, provides more accurate results, so that the alternatives may be evaluated in accordance with hesitant human nature. The purpose of this paper is to select the best HCWTT using a hesitant fuzzy linguistic term set (HFLTS). Design/methodology/approach Five main criteria were identified for HCWTT selection, such as economic, social, environmental, technical and ergonomic criteria. In total, 19 sub-criteria were examined, and the hierarchy of the criteria was formed. The criteria weights were determined using the multi-criteria hesitant fuzzy linguistic term set (MC-HFLTS). The selection processes of incineration (A1), steam sterilization (A2), microwave (A3) and landfill (A4) alternatives were carried out using the multi-attributive ideal-real comparative analysis (MAIRCA) and multi-attributive border approximation area comparison (MABAC) methods. In the comparative analyses, Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) and technique for order preference by similarity to an ideal solution (TOPSIS) methods were used. Findings The comparison of the results of the MABAC and MAIRCA methods with the results of VIKOR and TOPSIS methods indicated that A2 (steam sterilization) alternative was the best one and produced the same ranking of the technology alternatives (A2 > A3 > A1 > A4). As a result, the study concluded that these methods can be successfully used for HCWTT selection problems. Originality/value To the best of the authors’ knowledge, MC-HFLTS has not been used to select HCWTT in the existing literature. For the first time, MC-HFLTS&MAIRCA and MC-HFLTS&MABAC approaches were used in order to choose the best treatment method for healthcare waste under the effect of multiple conflicting hierarchical criteria. It has been provided that MABAC and MAIRCA select alternative choices by taking into consideration the hierarchical criteria. Unlike other studies, this study also considered ergonomic criteria that are important for people working during the process of using the treatment technology.

Application of interval valued fuzzy-rough numbers in multi-criteria decision making: The IVFRN-MAIRCA model

This paper presents a new approach for the treatment of uncertainty and imprecision based on interval-valued fuzzy-rough numbers (IVFRNs). IVFRNs make a decision making possible using only the internal knowledge from the data, using objective indeterminacy without the need to rely on models of any assumption. Namely, instead of subjectively entering external uncertainties, the structure of the given data is used. Taking into account the given assumptions, we developed an original multi-criteria model based upon the IVFR approach. In the multi-criteria model the traditional MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) method was modified. The model was tested and validated on a case study, considering selection of the optimal landing operations point for overcoming water obstacles. The sensitivity analysis of the IVFRN MAIRCA model was carried out through 24 scenarios which showed that our results are of a high stability degree.

Ocenjivanje menija bazirano na "rough MAIRCA" i "BW" metodama

The evaluation of dishes represents basic activity in the structuring of menu, which allows the optimal use of resources in order to fully satisfy the expectations of users and restaurant management. This study presents a new approach in menu evaluation using the MultiAttributive Ideal-Real Comparative Analysis (MAIRCA) method, modified by application of rough numbers in dealing with imprecisions in group decision making and the Best-Worst Method (BWM) for determining the interval values of weight coefficients which objectivize the inconsistencies in expert judgment. The model was successfully tested on the menu of a collective nutrition restaurant, where the evaluation of six existing breakfast dishes and a new dish that should replace the worst ranking dish was carried out. Validation of the model was done by comparing the results obtained by application of MABAC and VIKOR methods, which confirmed high reliability. The validated values were discussed in sensitivity analysis, through 27 scenarios with the change in value of weight coefficients, which showed a large correlation of the obtained ranks and confirmed credibility of the obtained values.

Supplier Selection using Rough BWM-MAIRCA model: A case study in Pharmaceutical Supplying in Libya

The quality of health system in Libya has witnessed a considerable decline since the revolution in 2011. One of the major problems this sector is facing is the loss of control over supply medicines and pharmaceutical equipments from international suppliers for both public and private sectors. In order to take the right decision and select the best medical suppliers among the available ones, many criteria have to be considered and tested. This paper presents a multiple criteria decision-making analysis using modified BWM (Best-Worst method) and MAIRCA (Multi-Attribute Ideal-Real Comparative Analysis) methods. In the present case study five criteria and three suppliers are identified for supplier selection. The results of the study show that cost comes first, followed by quality as the second and company profile as the third relevant criterion. The model was tested and validated on a study of the optimal selection of supplier.

A Novel Rough WASPAS Approach for Supplier Selection in a Company Manufacturing PVC Carpentry Products

The decision-making process requires the prior definition and fulfillment of certain factors, especially when it comes to complex areas such as supply chain management. One of the most important items in the initial phase of the supply chain, which strongly influences its further flow, is to decide on the most favorable supplier. In this paper a selection of suppliers in a company producing polyvinyl chloride (PVC) carpentry was made based on the new approach developed in the field of multi-criteria decision making (MCDM). The relative values of the weight coefficients of the criteria are calculated using the rough analytical hierarchical process (AHP) method. The evaluation and ranking of suppliers is carried out using the new rough weighted aggregated sum product assessment (WASPAS) method. In order to determine the stability of the model and the ability to apply the developed rough WASPAS approach, the paper analyzes its sensitivity, which involves changing the value of the coefficient λ in the first part. The second part of the sensitivity analysis relates to the application of different multi-criteria decision-making methods in combination with rough numbers that have been developed in the very recent past. The model presented in the paper is solved by using the following methods: rough Simple Additive Weighting (SAW), rough Evaluation based on Distancefrom Average Solution (EDAS), rough MultiAttributive Border Approximation area Comparison (MABAC), rough Višekriterijumsko kompromisno rangiranje (VIKOR), rough MultiAttributiveIdeal-Real Comparative Analysis (MAIRCA) and rough Multi-objective optimization by ratio analysis plus the full multiplicative form (MULTIMOORA). In addition, in the third part of the sensitivity analysis, the Spearman correlation coefficient (SCC) of the ranks obtained was calculated which confirms the applicability of all the proposed approaches. The proposed rough model allows the evaluation of alternatives despite the imprecision and lack of quantitative information in the information-management process.

Evaluating the performance of suppliers based on using the R'AMATEL-MAIRCA method for green supply chain implementation in electronics industry

Abstract Green supply chain management (GSCM) practitioners striving to create a healthier environment should first identify the key criteria pertinent to the process of implementing the appropriate sustainable policies, particularly in the most rapidly growing electronics sector. Since the decision to adopt GSCM in electronics industry is associated with the use of a multi-dimensional approach involving a number of qualitative criteria, the paper examines GSCM based on fifteen criteria expressed in five dimensions and proposes a multi-criteria evaluation framework for selecting suitable green suppliers. In real life, the assessment of this decision is based on vague information or imprecise data of the expert's subjective judgements, including the feedback from the criteria and their interdependence. Thus to treat this uncertainty in multi-criteria decision making (MCDM) process, rough number (RN) is applied here using only the internal knowledge in the operative data available to the decision-makers. In this way objective imprecisions and uncertainties are used and there is no need to rely on models of assumptions. Instead of different external parameters in the application of RN, the structure of the given data is used. Therefore, the identified components are incorporated into a rough DEMATEL-ANP (R'AMATEL) method, combining the Decision Making Trial and Evaluation Laboratory Model (DEMATEL) and the Analytical Network Process (ANP) in a rough context. In group decision making, a rough number-based approach aggregates individual judgements and handles imprecision. The structure of the relationships between the criteria expressed in different dimensions is determined by using the rough DEMATEL (R'DAMETEL) method and building an influential network relation mapping, based on which the rough ANP (R'ANP) method is implemented to obtain the respective criteria weights. Then, the rough multi-attribute Ideal-Real Comparative Analysis (R'MAIRCA) is used to evaluate the environmental performance of suppliers for each evaluation criterion. Sensitivity analysis is performed to determine the impact of the weights of criteria and the influence of the decision maker's preferences on the final evaluation results. Applying the Spearman's rank correlation coefficient and other ranking methods, the stability of the alternative rankings based on the variation in the criteria weights is checked. The results obtained in the study show that the proposed method significantly increases the objectivity of supplier assessment in a subjective environment.

Novel Integrated Multi-Criteria Model for Supplier Selection: Case Study Construction Company

Supply chain presents a very complex field involving a large number of participants. The aim of the complete supply chain is finding an optimum from the aspect of all participants, which is a rather complex task. In order to ensure optimum satisfaction for all participants, it is necessary that the beginning phase consists of correct evaluations and supplier selection. In this study, the supplier selection was performed in the construction company, on the basis of a new approach in the field of multi-criteria model. Weight coefficients were obtained by DEMATEL (Decision Making Trial and Evaluation Laboratory) method, based on the rough numbers. Evaluation and the supplier selection were made on the basis of a new Rough EDAS (Evaluation based on Distance from Average Solution) method, which presents one of the latest methods in this field. In order to determine the stability of the model and the applicability of the proposed Rough EDAS method, an extension of the COPRAS and MULTIMOORA method by rough numbers was also performed in this study, and the findings of the comparative analysis were presented. Besides the new approaches based on the extension by rough numbers, the results are also compared with the Rough MABAC (MultiAttributive Border Approximation area Comparison) and Rough MAIRCA (MultiAttributive Ideal-Real Comparative Analysis). In addition, in the sensitivity analysis, 18 different scenarios were formed, the ones in which criteria change their original values. At the end of the sensitivity analysis, SCC (Spearman Correlation Coefficient) of the obtained ranges was carried out, confirming the applicability of the proposed approaches.

Location Selection for Wind Farms Using GIS Multi-Criteria Hybrid Model: An Approach Based on Fuzzy and Rough Numbers

This paper presents spatial mathematical model in order to identify sites for the wind farms installment which can have significant support for the planners in the area of strategy and management of wind power use. The suggested model is based on combined use of Geographical Information Systems (GIS) with multi-criteria techniques of Best-Worst method (BWM) and MultiAttributive Ideal-Real Comparative Analysis (MAIRCA). Rough numbers and fuzzy logic are used to exploit uncertainty during data analysis in spatial mathematical model. The model is applied on the case study. Rough BWM model is used to determine weight coefficients of the criteria and rough MAIRCA method is used to rank separated sustainable locations. The implementation of MAIRCA method has shown that the location L3 is the most suitable for the wind farm in the area covered in the case study. Therefore, the suggested spatial mathematical model can be successfully used to identify the potential suitable sites for the wind farms in other areas with similar geographic conditions.

The Combination of Expert Judgment and GIS-MAIRCA Analysis for the Selection of Sites for Ammunition Depots

This paper suggests spatial multi-criteria model in order to assist decision makers in the selection of sites which are suitable for ammunition depots (AD). They represent military facilities which have more criteria that need to be matched than civil structures. The proposed model is based on combined use of Geographic information systems (GIS) and multi-criteria techniques. The model application is presented in the case study of Carpathian region, the Eastern part of Serbia. The model deals with nine restrictions and six evaluation criteria. Decision Making Trial and Evaluation Laboratory—Analytic Network Process (DEMATEL-ANP) multi-criteria techniques are used to determine weight coefficients of evaluation criteria. Along with the above mentioned methods, this paper introduces a new technique for the multi-criteria decision making—MAIRCA (MultiAttributive Ideal-Real Comparative Analysis) method. The MAIRCA method is used for the ranking and selection of suitable locations. The results have shown that 45 km2 of the Carpathian region is very suitable for ammunition depot construction. The MAIRCA method chose location L1 as the most appropriate. Sensitivity analysis shows that the model is capable of identifying a suitable ammunition depot location. This approach can be helpful in determining suitable ammunition depot locations in other regions with similar geographic conditions and can also be successfully used for the suitability assessment of existing ammunition depots.