Integration of Fuzzy-Analytical Hierarchy Process and TOPSIS model for road maintenance contractor selection

n modern aircraft design, increased attention is being paid to the conceptual and preliminary design phases so as to increase the odds of creating a design that will ultimately be successful at the completion of the design process. Since aerospace systems are complex systems with interacting disciplines and technologies, the decision makers dealing with such design problems are involved in balancing multiple, potentially conflicting attributes/criteria, transforming a large amount of customer supplied guidelines into a solidly defined set of requirement definitions. As a result, the criteria have to be all simultaneously taken into account and a compromise essentially becomes part of the decision making process. Various methods and techniques are available to deal with such sort of multi-criteria decision making (MCDM) problems. In the 1970’s, Saaty proposed the Analytic Hierarchy Process (AHP), which facilitates the MCDM problems that have a hierarchical structure of attributes by reducing complex decisions to a series of pair-wise comparisons. In this method, the preference information is elicited as the pair-wise comparisons between attributes or alternatives and treated using the eigenvector method. The other straightforward method to handle the MCDM problem is the Overall Evaluation Criterion (OEC) technique, presented in Ref 3. The OEC is a single metric and is obtained by summing multiple non-dimensional attribute metrics normalized by the metric values of a relevant baseline. Another commonly used MCDM technique is the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). The “best” solution chosen by TOPSIS is the alternative that is the closest to the positive ideal solution and the furthest from the negative ideal solution. The separation between each alternative solution and the ideal solution, which is determined by the weighted criteria, is rather sensitive to criterion weights, so typically several weighting scenarios are investigated to determine the final solution. Among these developed MCDM methods, different methods have different underlying assumptions, information requirements, analysis models, and decision rules that are designed for solving a certain class of decision making problems. This implies that it is critical to use the most appropriate method to solve the problem under consideration since the use of unsuitable method always leads to misleading design decisions. Consequently, bad design decisions will result in big loss to the society, such as property damage or personal injury. Thus, it is necessary to review the existing MCDM methods, discuss in depth their advantages, disadvantages, applicability, computational complexity, etc. in order to make right decision when choosing the right method for the given problem. In this paper a hybrid MCDM method is developed to deal with the problem under consideration. Relative weights of the evaluation criteria are elicited by using the eigenvector method to describe the decision maker’s preference information. The TOPSIS method is used to analyze the qualitative and quantitative data of input parameters and find the solution to the given problem. An aircraft technology selection problem is conducted as a proof of implementation to demonstrate the functionality and effectiveness of the proposed methodology.

PurposeThe increasing complexity of supply chains and the corresponding demand for efficiency and reliability highlight the urgent need for enhanced performance and measurement standards. The drive for improved competitiveness is a central theme across all sectors, driving the demand for superior performance and high-quality services. Research on performance factors in the domain of inland waterway transport (IWT) is limited, and the existing studies lack the incorporation of practical methods that could effectively enhance the reliability of performance management results. Thus, this study aims to identify and analyse factors influencing performance perception in IWT and establish a benchmarking methodology for assessing UK IWT performance and four other European market leaders.Design/methodology/approachThe paper uses the fuzzy-analytical hierarchy process (FAHP) and the technique for order preference by similarity to the ideal solution (TOPSIS) based methodology to support the IWT benchmarking process which is divided into three stages. Firstly, the study identifies performance factors through literature analysis and, then, validates them through a structured questionnaire survey, In the second stage, the critical success factors are prioritized using FAHP and expert judgments. Finally, the UK’s IWT performance was benchmarked with four European market leaders using the TOPSIS method.FindingsThe study identified 48 performance factors in IWT supply chains, categorized into eight: mobility and reliability, efficiency, profitability, environmental impact, infrastructure condition, safety, security, economic development, innovative transport technology and policy formulation. Mobility and infrastructure conditions were found to be the most significant.Research limitations/implicationsThe present study will contribute by enhancing the overall understanding of performance management within IWT supply chains. The performance factors identified, along with the structural hierarchy taxonomic diagram will create a detailed performance database.Originality/valueThis study uses empirical data to identify performance determinants in intermodal IWT supply chains. It contributes to the theoretical framework surrounding the measurement and standards of IWT supply chain performance. The study also adopts the fuzzy-AHP method to evaluate and prioritize these performance factors to inform relevant stakeholders and policymakers of the most significant performance factors. Furthermore, this study serves as a preliminary reference for future research.

Introduction: This paper involves assessing the most suitable insurance company for company X1 using Multiple Criteria Decision Making (MCDM). This company is one of the biggest financial organizations and problems were identified with the existing process of insurance tender selection. The manual nature of the current process is very tedious and takes almost three months to complete and this increases the probability of error and also leads to employee dissatisfaction.Artifact: To provide a solution to this problem, several MCDM models including Analytical Hierarchy Process (AHP), Analytical Network Process (ANP), and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) and Fuzzy Sets were researched to determine the best MCDM model for this scenario. After conducting a thorough research it was concluded that the best approach would be to use a hybrid methodology that combines AHP and TOPSIS. By using AHP to calculate the weights and using TOPSIS to determine the best alternative, accurate results can be obtained, as it combines the strengths of the two methodologies. In terms of time and complexity also this hybrid methodology doesn’t involve a high level of complexity as in ANP and also with regard to the time factor, although the calculation of weights may require some time, using TOPSIS the best alternative can be determined relatively fast.Methodology: To validate and verify the quality and to ensure that the system worked as intended, several testing strategies such as User Acceptance testing and Accuracy testing was used. The samples used for these testing methods were the staff of the insurance department in company X.Results: The results of the user acceptance testing showed an over 70% satisfaction with the system. The system had been greatly improved in terms of the time taken as well as the efficiency and accuracy of the decision. Two cases were taken for the accuracy testing and in both cases the manual calculation and system calculation matched except for slight differences to the decimal point. However the overall results were the same. This showed that the model worked successfully in determining the best insurance tender.Conclusion: AHP-TOPSIS could be combined to form a more effective model that combines the strengths of each model to reduce its limitations in order to select the best insurance tender. By using this model the throughput efficiency of the evaluation process was increased to 70% and the time taken to complete the overall process was reduced to at least a month.

This paper presented a new approach to the calculation of quality-adjusted life years (QALY) based on multi-criteria decision-making (MCDM) methods and using the EQ-5D-5L questionnaire. The health status utility values were calculated through a hybrid methodology. We combined the analytic hierarchy process (AHP), the AHP with a D-number extended fuzzy preference relation (D-AHP), the fuzzy analytic hierarchy process (F-AHP), and the technique for order preference by similarity to the ideal solution (TOPSIS) to obtain individual and aggregated utility values. The preference data were elicited using a sample of individuals from a Colombian university. In all tested methods, the ordinal preferences were consistent, and the weights were compared using the Euclidean distance criterion (EDC). We identified F-AHP-TOPSIS as the optimal method; its benefits were associated with modeling the response options of the EQ-5D in linguistic terms, it gave the best approximation to the initial preferences according to EDC, and it could be used as an alternative to the known prioritization method. This hybrid methodology was particularly useful in certain medical decisions concerned with understanding how a specific person values his or her current health or possible health outcomes from different interventions in small population samples and studies carried out in low- and middle-low-income countries.

In this paper, nineteen models were used to estimate the monthly average hourly global solar irradiation from the daily global irradiation value; at the “Cirque de Mafate” which is an isolated high mountain and rugged relief site in Reunion Island. These models are divided into three groups; the first depends on solar parameters like hour angle or solar time, the second implies that the estimation function follows a Gaussian distribution, and the third is a simplified form of the first. The main target is to find, for the site, the best model to estimate the abovementioned monthly average hourly irradiation. The measured data used to validate the models are from an in situ weather station. The following statistical criteria; normalized mean bias error, normalized absolute mean bias error, normalized root mean square, t-statistical test, correlation coefficient, relative standard error and Nash-Sutcliffe Equation were used to evaluate the performance for each model. To rank and compare the nineteen models by the abovementioned seven criteria, the Multi-Criteria Decision Making (MCDM) approach has been used and especially the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). The basic principle of TOPSIS is to define the ideal model and the worst model by the set of the statistical criteria’s value for all models. Then the Euclidian distance to the ideal model and/or the worst model is calculated. The best model is the one that is nearest the ideal model and farthest the worst model. To use the TOPSIS, a normalized weight, that indicates the importance or priority, for each statistical criterion has been calculated by objective and subjective way. As result, it was found that the best model came from the first group and it is the Collares-Pereira and Rabl model modified by Gueymard (CPRG) and in second position is the Gueymard model.

Predicting efficiency in Islamic banks: An integrated multicriteria decision making (MCDM) approach

PurposeThe measurement of regional competitiveness is becoming essential for policymakers to address territorial disparities, while considering the issue of correlations among indicators. Therefore, the purpose of this paper is to measure regional competitiveness using the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) by considering different distance measures and two levels of analysis to provide a comparative and comprehensive measurement of regional competitiveness in Europe.Design/methodology/approachThe authors apply TOPSIS based on three different distance measures (the Manhattan, the Euclidean and the Mahalanobis distance measures) to the regions of the EU Regional Competitiveness Index (RCI) 2019, which is taken as the frame of reference.FindingsThe authors replicate the RCI by using TOPSIS with a less preferred choice of distance measure, indicating TOPSIS as a valuable method for policymakers in the analysis of regional competitiveness. The authors argue in favour of the Mahalanobis distance measure as the best of the three, as it considers correlations among macro-economic indicators.Originality/valueThis study aims to make three contributions. Firstly, by replicating the RCI by means of TOPSIS with a less preferred choice of distance measure, the paper provides a benchmark for future research on regional competitiveness. Secondly, by suggesting the use of TOPSIS with the use of the Mahalanobis distance measure, the authors show how to measure regional competitiveness by taking into account correlations among pillars. Thirdly, the authors argue in favour of considering clusters of regions when measuring regional competitiveness.

Comparison of first aggregation and last aggregation in fuzzy group TOPSIS

El This study addresses supply chain management in the automotive industry, a highly competitive sector in which outsourcing plays a critical role in cost reduction and operational flexibility. A representative case involves the subcontracting of cardboard box manufacturing for glass packaging, whose efficiency directly affects production performance. To optimize supplier selection, a hybrid multicriteria decision-making (MCDM) approach integrating the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) was applied. AHP was employed to determine the relative weights of the evaluation criteria through pairwise comparisons, while TOPSIS was used to rank suppliers based on their relative closeness to the ideal solution, incorporating both quantitative and qualitative factors. The methodology was implemented in a real-world automotive case study, assessing cardboard packaging suppliers according to four key criteria: unit cost, quality, delivery time, and environmental sustainability. The integration of expert judgment with verifiable technical data ensured a robust and objective evaluation of the available alternatives. The results revealed that, among the shortlisted candidates, the selected supplier emerged as the most favorable option. This supplier demonstrated a competitive unit cost and acceptable delivery time, despite exhibiting comparatively lower performance in environmental sustainability. This finding highlights the necessity of balancing organizational priorities in accordance with economic and operational constraints. In conclusion, the study demonstrates that the application of multicriteria decision-making methods (AHP–TOPSIS) constitutes an effective decision-support tool for supplier selection, enabling improved operational efficiency and enhanced competitiveness within the automotive supply chain.

Selecting starting pitchers is a strategic issue with a significant effect on the performance of a professional team. Choosing optimal starting pitchers from many alternatives is a multi-criteria decision-making (MCDM) problem. This study develops an evaluation model, based on the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), to help managers and coaches of a professional baseball team make the optimal selection for starting pitchers. The AHP was used to analyze the structure of starting-pitcher selection and determines weights of the criteria, whereas the TOPSIS method makes the final ranking. Empirical analysis illustrates model utilization for selecting starting pitchers. The results of this study demonstrate the effectiveness and feasibility of the proposed model.

This paper presents an efficiency assessment of the Angolan banks using Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). TOPSIS is a multi‐criteria decision‐making technique similar to data envelopment analysis, which ranks a finite set of units based on the minimisation of distance from an ideal point and the maximisation of distance from an anti‐ideal point. In this research, TOPSIS is used first in a two‐stage approach to assess the relative efficiency of Angolan banks using the most frequent indicators adopted by the literature. Then, in the second stage, neural networks are combined with TOPSIS results as part of an attempt to produce a model for banking performance with effective predictive ability. The results reveal that variables related to cost structure have a prominent negative impact on efficiency. Findings also indicate that the Angolan banking market would benefit from higher level of competition between institutions.

Suitability analysis for siting MSW landfills and its multicriteria spatial decision support system: Method, implementation and case study

Abstract. The problem of Urban Municipal solid waste disposal is a challenging task faced by civic bodies and planning authorities in almost all the cities of rapidly developing countries like India. A similar situation is being faced by Dehradun, the capital, and the fastest growing city of Uttarakhand, India. In the current study, an attempt has been made to find out the suitable sites for waste disposal in the area around Dehradun city using Geospatial Multi-criteria Decision Analysis (MCDA) techniques from remote sensing data. Two different decision rules of MCDA are used, namely, Analytical Hierarchical Process based Weighted Linear Combination (AHP – WLC) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). WLC has been used previously for similar studies for its ease and simplicity to apply in raster format but TOPSIS has an advantage over WLC, it orders a set of alternatives on the basis of their separation from the ideal point. It defines the best alternative as the one that is simultaneously closest to the ideal alternative and farthest from the negative ideal point. Raster-based suitability analysis has been done and the results obtained by the two methods are compared. Identical results with minor differences identifying best suitable sites outside the eastern boundary of the city where the existing dumping site is located are obtained. Also, new potential sites are identified in the western part of the city which faces the problem of waste disposal more acutely because of expansion of the city in that direction.

Optimizing flow through metal foams (MFs) in double-tube heat exchangers for solar flat plate collectors is critical for enhancing heat transfer while minimizing pressure drop. This research employs a multi-objective approach to achieve this balance by optimizing the thermal and fluid dynamics of the system. The study uses the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) and modified TOPSIS (M-TOPSIS) for comparative analysis to identify the most efficient MF configurations. Copper and Aluminum foams with varying porosities and pore densities are examined, focusing on five key criteria that prioritize both flow resistance and heat transfer. Results indicate that higher pore density and Copper materials generally yield superior performance. For instance, 20 PPI Copper foam achieves a score of 0.92 at a Reynolds number of 3,000, with only a slight decrease of 0.25% at 6,200. When balancing pressure drop and heat transfer equally, the 20 PPI Copper foam positioned on the inner lateral pipe yields the highest score of 0.910. A comparative analysis of TOPSIS and M-TOPSIS further underscores their effectiveness in multi-criteria decision-making.

Selecting starting pitchers is a strategic issue with a significant effect on the performance of a professional team. Choosing optimal starting pitchers from many alternatives is a multi-criteria decision-making (MCDM) problem. This study develops an evaluation model, based on the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS), to help managers and coaches of a professional baseball team make the optimal selection for starting pitchers. The AHP was used to analyze the structure of starting-pitcher selection and determines weights of the criteria, whereas the TOPSIS method makes the final ranking. Empirical analysis illustrates model utilization for selecting starting pitchers. The results of this study demonstrate the effectiveness and feasibility of the proposed model.