Informatica

The accelerated progress of aquaponics offers a promising remedy for food production in arid regions, where success heavily hinges on sustaining optimal water quality parameters of aquaponic system. However, managing water parameters in large-scale aquaponic farms, given their complex and interconnected nature, poses significant challenges. Various control approaches have been introduced over the years, but selecting the most suitable one is vital for ensuring stability, efficiency, and high productivity. In this study, a novel fuzzy-based Multiple Criteria Decision Making (MCDM) methodology is proposed, which combines the Intuitionistic Fuzzy Ordinary Priority Approach (OPA-IF) with the Neutrosophic-TOPSIS strategy. This methodology aims to identify the most appropriate control strategy for large-scale aquaponic systems. The OPA-IF analysis reveals that the ‘Capability to Handle MIMO Systems’ is the most critical criterion, leading to the conclusion, through the Neutrosophic-TOPSIS approach, that ‘Model Predictive Control (MPC)’ is the optimal choice for managing large-scale aquaponic systems. Additionally, a comparative analysis using the BWM-Neutrosophic-TOPSIS strategy further supports the findings of the proposed method. The results are further validated through statistical analysis and sensitivity testing, ensuring their robustness and reliability. Overall, this study not only contributes to the scientific understanding of control strategies in aquaponics but also offers practical insights for farmers and aquaponic practitioners. The ultimate goal is to enhance the sustainability and efficiency of aquaponic systems, promoting their adoption and long-term success in sustainable food production.

This paper examines ranking reversal (RR) in Multiple Criteria Decision Making (MCDM) using the Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS). Through a mathematical analysis of min-max and max normalization techniques and distance metrics (Euclidean, Manhattan, and Chebyshev), the study explores their impact on RR, particularly when new, high-performing alternatives are introduced. This research provides insight into the causes of RR, offering a framework that clarifies when and why RR occurs. The findings help decision-makers select appropriate techniques, promoting more consistent and reliable outcomes in real-world MCDM applications.

Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is a very common and useful method for solving multi-criteria decision making problems in certain and uncertain environments. Single valued neutrosophic hesitant fuzzy set (SVNHFS) and interval neutrosophic hesitant fuzzy set (INHFS) are developed on the integration of neutrosophic set and hesitant fuzzy set. In this paper, we extend TOPSIS method for multi-attribute decision making based on single valued neutrosophic hesitant fuzzy set and interval neutrosophic hesitant fuzzy set. Furthermore, we assume that the attribute weights are known, incompletely known or completely unknown. We establish two optimization models for SVNHFS and INHFS with the help of maximum deviation method. Finally, we provide two numerical examples to validate the proposed approach.

The purpose of this study is to apply the method of hybrid multiple criteria decision making (MCDM) to select public relations personnel for tourism industry in Taiwan. Fuzzy Delphi method, which can lead to better criteria selection, is used to modify criteria. Considering the interdependence among the selection criteria, analytic network process (ANP) is then used to obtain the weights of them. To avoid calculation and additional pairwise comparisons of ANP, technique for order preference by similarity to ideal solution (TOPSIS) is used to rank the alternatives. The use of a combination of fuzzy Delphi method, ANP and TOPSIS, proposing a MCDM model for public relations personnel selection, and applying these to a real case is the unique features of this study.

In this study, we evaluated the effects of the normalization procedures on decision outcomes of a given MADM method. For this aim, using the weights of a number of attributes calculated from FAHP method, we applied TOPSIS method to evaluate the financial performances of 13 Turkish deposit banks. In doing this, we used the most popular four normalization procedures. Our study revealed that vector normalization procedure, which is mostly used in the TOPSIS method by default, generated the most consistent results. Among the linear normalization procedures, max-min and max methods appeared as the possible alternatives to the vector normalization procedure.

“Strategy implementation” is an inseparable part of strategic management process. Transformation strategies to typical operations and daily functions of staff exert a significant role in organization success. Balanced scorecard (BSC) and strategy map help senior managers to perfectly implement and monitor the accomplishment of the strategies by transforming strategies into operational programs. Using BSC and strategy map, the strategies are translated into some action plans which help the achievement of organizational goals and strategies. Due to shortage of resources, usually all organization's action plans cannot be implemented completely; therefore, managers should make use of some tools for assigning and selecting more efective action plans. In this paper, a procedure is suggested on the basis of grey TOPSIS to determine the preference of action plans to better aid managers in selection of the most effective action plans in a group decision making process.

The aim of the current research is to measure objective congruence (incongruence) of the results obtained in a process of multiple criteria analysis when applying different MCDM methods. The methodology for evaluation of ranking results is developed on the ground of a case study of the redevelopment of derelict buildings as well as on composed experimental tasks. Fuzzified COPRAS, TOPSIS and VIKOR methods are applied for ranking the alternatives. Calculation results are evaluated by applying mathematical statistics methods. A methodology for measuring the congruence (incongruence) of the relative significances of alternatives is proposed.

The paper analyses the problem of ranking accuracy in multiple criteria decision-making (MCDM) methods. The methodology for measuring the accuracy of determining the relative significance of alternatives as a function of the criteria values is developed. An algorithm of the Technique for the Order Preference by Similarity to Ideal Solution (TOPSIS) that applies criteria values' transformation through a normalization of vectors and the linear transformation is considered. A computational experiment is presented, to compare the results of a multiple criteria analysis and the ranking accuracy in a particular situation.