Development and Implementation of A Hybrid Multi-Criteria Decision Making Technique

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.

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.

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.

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.

Public gatherings, transit hubs, stadiums, and crowded retail malls are just a few examples of places where crowd management has become an urgent issue in recent years. Effective crowd management strategies have been required due to the increasing population, urbanization, and frequency of large-scale meetings. These strategies are used in dynamic, sometimes chaotic, circumstances to protect people and facilitate their free movement. The purpose of this study is to analyze and rank various strategies for crowd management to reduce the risks of crushes and stampedes, improve security, and facilitate smoother traffic flow. This study used the single-valued neutrosophic set to deal with uncertain and vague information in the evaluation process. There are various factors in ranking the various strategies. So, the concept of multi-criteria decision-making (MCDM) is used to deal with various criteria. The neutrosophic set is integrated with the MCDM methodologies to rank various strategies. This study used the analytical hierarchy process (AHP) method to compute the weights of factors. Then the technique for order preference by similarity to the ideal solution (TOPSIS) method is used to rank the various strategies. An application was conducted to apply the proposed method. The outcome shows the safety and security factor is the heights important. The sensitivity analysis is applied to show the rank of strategies under various weights of factors. Finally, comparative analysis is applied to show the robustness of the proposed method compared with other MCDM methods.

Each decision-making tool should be tested and validated in real case studies to be practical and fit to global problems. The application of multi-criteria decision-making methods (MCDM) is currently a trend to rank alternatives. In the literature, there are several multi-criteria decision-making methods according to their classification. During our experimentation on the Combined Compromise Solution (CoCoSo) method, we encountered its limits for real cases. The authors examined the applicability of the CoCoFISo method (improved version of combined compromise solution), by a real case study in a university campus and compared the obtained results to other MCDMs such as Preference Ranking Organisation Method for Enrichment Evaluations (PROMETHEE), Weighted Sum Method (WSM) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS). Our research finding indicates that CoCoSo is an applied method that has been developed to solve complex multi variable assessment problems, while CoCoFISo can improve the shortages observed in CoCoSo and deliver stable outcomes compared to other developed tools.The findings imply that application of CoCoFISo is suggested to decision makers, experts and researchers while they are facing practical challenges and sensitive questions regarding the utilization of a reliable decision-making method. Unlike many prior studies, the current version of CoCoSo is unique, original and is presented for the first time. Its performance was approved using several strategies and examinations.

Manufacturing industry plays a very significant role in the economic functioning of any country. In recent times, reverse engineering (RE) has become an integral part of manufacturing set-up owing to its numerous applications. The quality of RE product primarily depends on the quality of digitization i.e., part measurement. There is a diverse range of digitization devices which can be employed in RE. These machines have variability in terms of cost, accuracy, ease of use, accessibility, scanning time, etc. Therefore, the decision regarding the selection of a suitable device becomes important in a particular RE application. The decisions taken in the planning stage for RE can have a long lasting impact on the functionality, quality and the economics of components to be used by manufacturing industries. To accomplish the selection procedure, a comparative study of three digitization techniques has been carried out. The determination of an appropriate digitization system is basically a multi-criteria decision making (MCDM) problem. MCDM techniques are yet to be applied in the selection of digitization systems for RE. MCDM is one of the most widely used decision methodologies in business and engineering spheres. The aim of this work is to describe various MCDM methods in the selection of digitization systems for RE. This paper intends to employ combinations between different MCDM methods such as group eigenvalue method (GEM), analytic hierarchy process (AHP), entropy method, elimination and choice expressing reality (ELECTRE), technique for order of preference by similarity to ideal solution (TOPSIS) and simple additive weighing (SAW) method. In this work, GEM, AHP, Entropy methods has been used to elicit weights of various selection criteria, while TOPSIS, ELECTRE and SAW have been applied to rank the alternatives. A comparative analysis has also been performed to determine the efficacies of different approaches. The conclusion of the paper reveals the best digitization system as well as the characteristics of different MCDM methods and their suitability in RE application.

Decision-making is a highly researched topic and various methods have been developed to facilitate a decision-maker (DM) in choosing the best alternative. Saaty's analytic hierarchy process (AHP) has been very popular since 1977 and has been adapted all over the world. However, AHP is a highly-debated topic. Technique for order of preference by similarity to ideal solution (TOPSIS) is another multi-criteria decision-making (MCDM) method developed by Hwang and Yoon in 1981 as a ranking method. This research is focused on identifying which is the MCDM method between AHP and TOPSIS. Since TOPSIS is a ranking method, the authors propose to combine AHP and TOPSIS methods and determine which method's ranking (AHP, AHP-TOPSIS combination, and TOPSIS with equal weights) aligns more closely with the DM's initial preference. Moreover, this research states the efficiency of the method by comparing the time it takes to make a decision and its reliability.

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.

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

Multicriteria decision‐making (MCDM) methods are concerned with the ranking of alternatives based on expert judgements made using a number of criteria. In the MCDM field, the distance‐based approach is one popular method for obtaining a final ranking. The technique for order preference by similarity to the ideal solution (TOPSIS) is a commonly used example of this kind of MCDM method. The TOPSIS ranks the alternatives with respect to their geometric distance from the positive and negative ideal solutions. Unfortunately, two reference points are often insufficient, especially for nonlinear problems. As a consequence of this situation, the final result ranking is prone to errors, including the rank reversals phenomenon.This study proposes a new distance‐based MCDM method: the characteristic objects method. In this approach, the preferences of each alternative are obtained on the basis of the distance from the nearest characteristic objects and their values. For this purpose, we have determined the domain and Fuzzy number set for all the considered criteria. The characteristic objects are obtained as the combination of the crisp values of all the Fuzzy numbers. The preference values of all the characteristic object are determined on the basis of the tournament method and the principle of indifference. Finally, the Fuzzy model is constructed and is used to calculate preference values of the alternatives, making it a multicriteria model that is free of rank reversal. The numerical example is used to illustrate the efficiency of the proposed method with respect to results from the TOPSIS method. The characteristic objects method results are more realistic than the TOPSIS results. Copyright © 2014 John Wiley & Sons, Ltd.

The object of the research consists of testing the suitability of the vector normalization procedure (NP) in the Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method. One of the most problematic steps of the Multi-Criteria Decision Making (MCDM) process is related to the application of NPs by default to transform different measurement units of criteria into a comparable unit. This is because of the absence of a universal agreement that defines which NP is the most suitable for a given MCDM method. In the literature, there are thirty-one available NPs, each one of them has its strengths and weaknesses and, accordingly, can efficiently be applied to an MCDM method and even worst to another. Let’s note that many NPs (e. g., NPs of sum, max-min, vector, and max) have been used by default (i. e., without suitability study) in the TOPSIS method. Consequently, outcomes of multi-criteria evaluation and rankings of alternatives considered in the decision problems could have led to inconsistent solutions, and, therefore, decision-makers could have made irrational or inappropriate decisions. That’s why suitability studies of NPs become indispensable. Moreover, a description of the methodology, proposed in this research, is outlined as follows: 1) method of weighting based on an ordinal ranking of criteria and Lagrange multiplier (for determining criteria weights); 2) TOPSIS method (for ranking considered alternatives); 3) a statistical approach with 3-estimate (for comparing effects generated by the used NPs). In the research, twelve different NPs are compared to each other in the TOPSIS method via a numerical example, which deals with the wheel loader selection problem. The results of the comparison indicate that, amongst the twelve different NPs analyzed in this suitability study, vector NP has the lesser effect on the considered alternatives’ evaluation outcomes, when used with the TOPSIS method. The vector NP-TOPSIS approach can therefore be applied to solve multi-criteria decision problems. Its application further allows the decision-makers and users to better select efficient solutions and, consequently, to make conclusive decisions.

Multi-Criteria Decision Making is a branch of Operation Research (OR) having a comparatively short history of about 40 years. It is being popularly used in the field of engineering, banking, fixing policy matters etc. It can also be applied for taking decisions in daily life like selecting a car to purchase, selecting bride or groom and many others. Various MCDM methods namely Weighted Sum Model (WSM), Weighted Product Model (WPM), Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) and Elimination and Choice Translating Reality (ELECTRE) are there to solve many decision making problems, each having its own limitations. However it is very difficult to decide which MCDM method is the best. MCDM methods are prospective quantitative approaches for solving decision problems involving finite number of alternatives and criteria. Very few research works in textiles have been carried out with the help of this technique particularly where decision taking among several alternatives becomes the major problem based on some criteria which are conflicting in nature. Gradation of jute fibres on the basis of the criteria like strength, root content, defects, colour, density, fineness etc. is an important task to perform. The MCDM technique provides enough scope to be applied for the gradation of jute fibres or ranking among several varieties keeping in view a particular object and on the basis of some selection criteria and their relative weightage. The present paper is an attempt to explore the scope of applying the multiplicative AHP method of multi-criteria decision making technique to determine the quality values of selected jute fibres on the basis of some above stated important criteria and ranking them accordingly. A good agreement in ranking is observed between the existing Bureau of Indian Standards (BIS) grading and proposed method.

This study investigated the prioritization and ranking problem of the appropriate locations at which to deploy solar photovoltaic (PV) farms. Although different Multicriteria Decision Making (MCDM) methods can be found in the literature to address this problem, a comparative analysis of those methods is missing. The aim of this study is to compare four different MCDM approaches to evaluate and rank suitable areas for the deployment of solar PV farms, with the island of Rhodes (Greece) being used as an example. Feasible areas for the location of such facilities were identified with the use of Geographical Information Systems (GIS), by applying certain exclusion criteria found either in the national legislative framework or in the international literature. Data were obtained from Greek open geospatial data. The feasible sites were evaluated and ranked using four different MCDM methods: the Analytical Hierarchy Process (AHP), the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), the VIKOR (VIseKriterijumska Optimizacija I Kompromisno Resenje), and the PROMETHEE II (Preference Ranking Organization METHod for Enrichment of Evaluations) method. The best alternative rated according to three TOPSIS, VIKOR and PROMETHEE is site (S2). The second-best alternative in the above three methods is site (S1), while the worst is site (S3). The best alternative rated according to AHP (S4) is in sixth position according to TOPSIS and in fifth position VIKOR and PROMETHEE. The comparison demonstrated that different MCDM techniques may generate different ranks. The simultaneous use of several MCDM methods in energy siting problems is considered advantageous as it can help decision makers to select the most sustainable sites, avoiding the disadvantages and availing the advantages of each method.