Professional Baseball Team Starting Pitcher Selection Using AHP and TOPSIS Methods

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.

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.

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.

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

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.

Objective: This research aims to identify the most important qualification criteria for selecting subcontractors in the construction supply chain. Design/Methods/Approach: The criteria calculations were analyzed based on the Analytical Hierarchy Process (AHP) method applied to obtain the weight of the subcontractor selection criteria. The Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) method was used to evaluate the different subcontractors against 22 sub-criteria indicators. The research sample is the Project Manager and Commercial Manager, who are the decision-makers in selecting subcontractors. Findings: Quality, Health, Safety, and Environment (HSE) and Price are the highest priority criteria for subcontractor selection, with the most influential sub-criteria being quality work specifications, tender prices, and having an HSE supervisor on the project. Originality: Selection of the right Subcontractor is very important for the successful completion of the project and the continuity of the contractor's business as most of the construction project work is carried out by subcontractors. This research on subcontractor selection is to reduce errors in the selection of subcontractors in construction projects and to understand multi-criteria decision-making using the AHP and TOPSIS methods.

In today’s competitive and rapidly changing business landscape, organizations face significant challenges such as resource limitations, fluctuating demand, and evolving customer needs. Addressing these challenges requires effective strategies, with supplier selection playing a vital role in building resilient and efficient supply chains. This study introduces an innovative framework for supplier evaluation and selection, integrating the analytic hierarchy process (AHP) and the technique for order preference by similarity to the ideal solution (TOPSIS) within a fuzzy environment. The AHP method was employed to systematically identify and prioritize key performance indicators (KPIs) critical for evaluating suppliers. Criteria such as transportation cost, flexibility in meeting product requirements, defect reduction, and effective communication and responsiveness were identified as the most significant factors. These priorities formed the foundation for applying the fuzzy TOPSIS method, which facilitated the ranking of suppliers under conditions of uncertainty. The analysis revealed Sepidar Darb, Aram Plastic Sabalan, Sanaye Plastic Markaz, and Amin Avar Plastic as the top-performing suppliers, followed by Pegah Zanjan Company. The relevance of this research is heightened by the impact of the COVID-19 pandemic, which has disrupted global supply chains and fundamentally altered supplier selection criteria. While pre-pandemic evaluations predominantly focused on cost efficiency and product quality, the pandemic has underscored the importance of additional criteria such as supplier agility, risk management capabilities, geographical proximity, and digital integration. These emerging priorities highlight the necessity of rethinking traditional approaches to supplier selection and adapting to the evolving demands of global supply chains. By incorporating these updated criteria into the AHP-TOPSIS framework, this study offers a robust and practical tool for supplier evaluation in uncertain and dynamic environments. The proposed framework not only improves upon traditional methods but also provides valuable insights for organizations striving to create resilient and adaptable supply chains capable of withstanding future disruptions.

Wrong decisions or inappropriate selection of equipment may lead to increase in cost and reduction in efficiency and effectiveness. Selecting right equipment has always been a key factor in the success of the process it is used for. In this study, superiority and inferiority ranking (SIR) methodis utilized for evaluation of most suitable offer for procurement of equipment installed inside a facility, whereas, analytical hierarchy process (AHP) is used to calculate the weights of factors that influence procurement decision. To achieve this target, a methodological framework of a series of interviews are conducted, then two questionnaire surveys are developed for identifying the important factors affecting the selection process of equipment and determining their relative importance. A solution of the problem is then designed in a model using AHP and SIR methods in addition to using the simple additive weighting (SAW) and technique for order preference by similarity to the ideal solution (TOPSIS) procedures to generate the superiority and inferiority flows. The model is generic and flexible and is used for the application of the multiple criteria decision making (MCDM) methods in the procurement process. The model also offers an efficient and convenient tool that aids its users to act in an orderly and methodical thinking, and guides them in making logical and robust decisions. A case study is presented to demonstrate the use of the developed model and sensitivity analysis is carried out to measure the robustness of the model in different scenarios.

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.

Bicycle-sharing systems (BSSs) are an effective solution to reduce private car usage in most cities and are an influential factor in encouraging citizens to shift to more sustainable transport modes. In this sense, the location of BSS stations has a critical impact on the system’s efficiency. This study proposed an integrated geographic information system–multi-criteria decision-making (GIS-MCDM) framework that includes the analytic hierarchy process (AHP), technique for order preference by similarity to the ideal solution (TOPSIS), and spatial data processing in GIS to determine a ranking of potential locations for BSS stations. The results of the proposed GIS-MCDM method can be used for both planning a new BSS or expanding one that is currently under operation. The framework was applied to a case study for expanding GIRA, the BSS of Lisbon, Portugal. In it, location criteria were selected in four categories, including criteria from the literature and extracted from available transaction data; in addition, we also suggested some criteria. The rebalancing operator’s staff were the decision makers in this study via their responses to the AHP questionnaire. The rebalancing staff believed that the main criterion of “city infrastructure” with the two sub-criteria of “population density” and “slope” were the most important. Furthermore, the proximity to the “bike network” with the sub-criterion of “proximity to the current bike stations” had less importance. Each criterion’s weight and inconsistency rate were obtained using the Expert Choice software. The geographic values of each criterion were created utilizing the ArcGIS software, and its network analyst module was employed for applying location techniques. Based on the created suitability map, the city’s center was the main suitable area for establishing new stations. Forty-five new bike stations were identified in those areas and ranked using the TOPSIS technique.

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

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

Background: Wireless Sensor Network (WSN) is a major technology for the Internet of Things (IoT) and is used within an IoT system to facilitate collaboration of heterogeneous information systems and services. Due to its distributed nature, these networks are highly vulnerable to various security threats which adversely affect their performance. Trust is one of the influential factors that applies in the security of WSNs to have its applications in cloud system, e-commerce etc. The secure and efficient neighbor selection is an issue of Multiple Criteria Decision Making (MCDM), where many Quality of Service (QoS) parameters play a vital role in the process of best neighbor selection. Methods: A dynamic and efficient trust model is proposed in this paper based on the ranking method for recommendation of appropriate secure neighbor node. To rank the available neighbors, we use voting approach and also a hybrid method of Analytical Hierarchy Process (AHP) and Technique for Order Preference by Similarity to the Ideal Solution (TOPSIS) methods. Results: The paper included a case study to demonstrate the effectiveness of proposed method which maximizes the defense against internal attacks. Complexity analysis has been done to show the superiority of the proposed method. Time complexity of the proposed algorithm is O (n2) against the compared algorithm the growth rate of which is O (2n). Conclusion: This method evaluates trustworthiness of neighbor node quantitatively as a fraction in the range 0 and 1. The proposed algorithm when applied, selects the best node among the alternatives.