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1
Paris, Adrian Stere, and Constantin Târcolea. "Materials Ranking by Means of Multi Attribute Decision Making." Applied Mechanics and Materials 760 (May 2015): 135–40. http://dx.doi.org/10.4028/www.scientific.net/amm.760.135.
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The first part of the work shortly describes possible mathematical methods for decision making. The paper gives an overview with general comments on multi-criteria problems, related to the preferences and the priorities of the decision-makers. The paper proposed and solves an example for material rating by a multiple attribute decision making. The MADM and PCA methodologies have been applied to rank out ten alternatives. Similar materials were selected using both techniques.
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Li, Chun-Hao, Hui-Xin Ma, and Yan-Hui Jia. "A Target-Oriented Multiple-Attribute Decision-Making Approach Based on Probabilistic Linguistic Preference Relations." Mathematical Problems in Engineering 2021 (January 19, 2021): 1–16. http://dx.doi.org/10.1155/2021/6695059.
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While the approach to multiple-attribute decision-making (MADM) is widely used in a variety of fields, including models with fuzzy sets and corresponding extensions, it cannot solve target-oriented decision problems with both selective and targeted alternatives. Therefore, this study provides the first description of a target-oriented MADM problem and proposes a novel decision framework. An attribute value function for target orientation is defined by integrating range and frequency values derived under cumulative prospect theory and range-frequency theory. A Choquet integral with discrete fuzzy measures is then used to integrate attribute values and determine comprehensive values for selective alternatives. In this determination of comprehensive values, a parameter estimation model is also established, with its input assumed to be the pairwise comparison judgment matrix with probabilistic linguistic preference relation. This model as well as its transformation aims at determining the parameters of both the attribute value function and fuzzy measures. Finally, the process of target-oriented MADM is summarized, and an illustrative example is provided to demonstrate the applicability of the proposed techniques.
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Zhuo, Jinbao, Weifeng Shi, and Ying Lan. "Fuzzy Attribute Expansion Method for Multiple Attribute Decision-Making with Partial Attribute Values and Weights Unknown and Its Applications." Symmetry 10, no. 12 (2018): 717. http://dx.doi.org/10.3390/sym10120717.
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In the real world, there commonly exists types of multiple attribute decision-making (MADM) problems with partial attribute values and weights totally unknown. Symmetry among some attribute information that is already known and unknown, and symmetry between the pure attribute set and fuzzy attribute membership set can be a considerable way to solve this type of MADM problem. In this paper, a fuzzy attribute expansion method is proposed to solve this type of problem based on two key techniques: the spline interpolation technique and the attribute weight reconfiguration technique, which are respectively used for the determination of attribute values and the reconfiguration of attribute weights. The spline interpolation technique to expand attribute values can enhance the performance of some regression methods and clustering methods by the comparisons between the results of these methods dealing with practical cases with and without the application of the technique, which further illustrates the effectiveness of this technique. For MADM problems with partial attribute values and weights totally unknown, compared with traditional fuzzy comprehensive evaluation (FCE), FCE with the application of fuzzy attribute expansion method can obtain results more similar with the ones when all attribute values and weights are known, which is proved by the practical power quality evaluation example.
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Amrollahi, Biuki Nahid, Fataneh Imandar, Ali Azhdari, Mahmoudabadi Mohammad Zarei, and Ardakani Mohammad Shaker. "Identifying and Prioritizing Factors Behind Cyber loafing Use at Work Using Multi-Criteria Decision Making (MADM) Approach." International Journal of Management, Accounting and Economics 11, no. 10 (2024): 1342–65. https://doi.org/10.5281/zenodo.13895220.
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The aim of the current paper is to identify the effective factors in forming the cyberloafing phenomenon and rank these factors. In the present study, a multiple attribute decision-making method has been used. The statistical population of this study consists of managers and employees of the Yazd Provincial Tax Administration, and 27 of them were selected based on accessibility. This research is applied and descriptive-analytical in terms of purpose and type. Based on the literature review, eighteen effective factors in the formation of cyberloafing were identified. Different MADM techniques, such as SAW, TOPSIS, ELECTRE, and TOXONOMY, were used to rank each factor. Finally, the Copeland fusion technique was used to reach a consensus on the execution of the mentioned techniques. Our investigations have shown that organizational policy factors, organizational commitment, and having a lot of leisure time had the most significant impact on the formation of the cyberloafing phenomenon. Also, interpersonal conflict, role overload, and belief in disorder had the least impact on the formation of the cyberloafing phenomenon. The current research is the first one conducted with this approach, especially in Iran, and its results can be considered by managers in other organizations as well.
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Kazimieras Zavadskas, Edmundas, Jurgita Antucheviciene, and Prasenjit Chatterjee. "Multiple-Criteria Decision-Making (MCDM) Techniques for Business Processes Information Management." Information 10, no. 1 (2018): 4. http://dx.doi.org/10.3390/info10010004.
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Information management is a common paradigm in modern decision-making. A wide range of decision-making techniques have been proposed in the literature to model complex business processes. In this Special Issue, 16 selected and peer-reviewed original research articles contribute to business information management in various current real-world problems by proposing crisp or uncertain multiple-criteria decision-making (MCDM) models and techniques, mostly including multi-attribute decision-making (MADM) approaches in addition to a single paper proposing an interactive multi-objective decision-making (MODM) approach. The papers are mainly concentrated in three application areas: supplier selection and rational order allocation, the evaluation and selection of goods or facilities, and personnel selection/partner selection. A number of new approaches are proposed that are expected to attract great interest from the research community.
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R. Ramesh. "Application of Neutrosophic Sets in Multi Attribute Decision Making." Communications on Applied Nonlinear Analysis 32, no. 9s (2025): 2635–49. https://doi.org/10.52783/cana.v32.4545.
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The selection of an optimal hospital for healthcare services is a complex decision-making problem due to the involvement of multiple, often conflicting, criteria such as quality of care, infrastructure, staff expertise, cost, and patient satisfaction. This paper presents an innovative approach for hospital selection using a hybrid decision-making framework combining neutrosophic sets, entropy weight, and the Multi-Attribute Decision-Making (MADM) method. Neutrosophic sets are employed to handle the inherent uncertainty and indeterminacy in hospital evaluation criteria, allowing for more flexible and accurate representation of data that may not be fully precise or certain. The entropy weight method is applied to objectively determine the significance of each evaluation criterion, ensuring that the decision process is not biased by subjective judgments. Finally, the MADM technique integrates the weighted criteria into a comprehensive decision model to rank hospitals based on their overall performance. The proposed model is tested with a real-world dataset of hospitals, demonstrating its effectiveness in selecting the most suitable healthcare facility while considering diverse factors. The results highlight the potential of combining neutrosophic sets with advanced decision-making techniques for improving decision accuracy and reliability in complex healthcare decisions.
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Sorooshian, Shahryar, Seyedh Mahboobeh Jamali, and Nader Ale Ebrahim. "Performance of the decision-making trial and evaluation laboratory." AIMS Mathematics 8, no. 3 (2023): 7490–514. http://dx.doi.org/10.3934/math.2023376.
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<abstract> <p>Multiple attribute decision-making (MADM) techniques constitute a practical approach for solving complex problems involving multiple and often conflicting criteria. Decision-making trial and evaluation laboratory (DEMATEL) is a popular MADM technique with both admirers and critics. This study presents a comprehensive review of DEMATEL through bibliometric analysis using the Scopus database. This article examined 3,521 papers published in journals, conferences or books between 1981 and 2023. We examined a few parameters for commenting on the performance of the technique. Among them are research outputs, the network of DEMATEL users, implementation subject areas, research zones, financing opportunities and publication hosts and their impact trends. We conclude from the findings of this study that the DEMATEL is capable of dealing with modern problem-solving in future environments. Although the growth of new MADMs is obvious, based on the gathered data, we forecast that more than 776 documents will be published in 2025 using DEMATEL for problem-solving. This expanding tendency will continue in the future. As distinct MADMs have diverse constraints, foundations, computing complexity and standpoints, which result in different performances, outmoded low-performance MADM techniques must be reported by researchers to continue this paper's objective to minimize ambiguity among decision-makers and practitioners. To facilitate such a comparison in the future, a quantitative performance coefficient was also developed here.</p> </abstract>
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Yu, Xin Qiao, and Wei Zhang. "Research on Services Oriented Group Decision Support System Integrated Platform-Multiple Attribute Group Services Based System." Applied Mechanics and Materials 44-47 (December 2010): 388–93. http://dx.doi.org/10.4028/www.scientific.net/amm.44-47.388.
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Group decision making process(GDM) has the characteristic of drawing on the wisdom of the mass, but in current social activities especially the e-business, the corresponding group decision support system(GDSS) are not widely and availably used currently. In this paper, we propose a new GDSS integrating platform with common interfaces to all kinds of GDSS. In this architecture, group decision making processes using multi-attribute decision making(MADM) techniques are regarded as services for new demands, which make decision making processes performed fast but without the loss of wisdom of the mass.
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Moghadam, Mansoor Kiani, Stephen Bonsall, Jin Wang, and Alan Wall. "Application of Multiple Attribute Decision-Making (MADM) and Analytical Hierarchy Process (AHP) Methods in the Selection Decisions for a Container Yard Operating System." Marine Technology Society Journal 43, no. 3 (2009): 34–50. http://dx.doi.org/10.4031/mtsj.43.3.3.
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AbstractThis study examines and evaluates important parameters in container yard operating systems. The objective of this study is to set up a basis for decision-making to select the best scenario among other options. It examines the important attributes determined using a multiple attribute decision-making (MADM) method. The MADM method is suited to the study of complex problems and allows the consideration of qualitative attributes expressed in linguistics terms and quantitative attributes illustrated in financial and throughput measures in container terminals. An analytical hierarchy process (AHP) technique is employed for solving the MADM problems. The AHP and principal eigenvector weighting techniques have been proposed in this study as weighting tools because they allow decomposition of a decision problem into a hierarchical order and enable a pair-wise comparison of the attributes with an acceptable level of consistency. The analysis assures that both qualitative and quantitative aspects of the decision are incorporated into the process. The results obtained from this study may be used to develop a basis for pair-wise comparison, judgment and selection of the best container yard operation option for the purpose of this study. For the first time, this study proposes the application of MADM and AHP for selection decisions in container terminals.
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IRVANIZAM, IRVANIZAM, ZAKIAL VIKKI, SUTARMAN SUTARMAN, and OPIM SALIM SITOMPUL. "A hybrid intelligent model based on logistic regression and fuzzy multiple-attribute decision-making for credit evaluation." Jurnal Natural 23, no. 3 (2023): 194–208. http://dx.doi.org/10.24815/jn.v23i3.32467.
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. One of the crucial issues in data mining is to select an appropriate classification algorithm. Due to it usually involves many criteria, the duty of algorithm selection can be widely described as multiple-attribute decision-making (MADM) problems, including credit risk evaluation. Many different MADM approaches select classifiers based on different perspectives, and hence they might generate diverse classifiers' rankings. This paper aims to propose a hybrid intelligent model to overcome credit risk assessment problems based on logistic regression and the fuzzy MADM method. Firstly, the Ordinal Priority Approach (OPA) method evaluates attributes involved in credit risk problems by considering professional assessments of a decision-maker and calculates a weight for each criterion. Secondly, all categorical data converted into triangular-fuzzy numbers (TFNs) and numerical data are evaluated using the MADM instrument to obtain an optimal solution dataset and logistic regression to calculate the probabilities of the optimal dataset. In this experimental study, three existing classification techniques and the proposed intelligent model evaluate three banking credit datasets with a different number of criteria under numerical and categorical data types. The prediction accuracy results generated by the proposed model are compared with the three existing classification methods. The results exhibit that there are slight differences between the three datasets. The experimental results demonstrate the proposed intelligent model has superiority in classifying the credit loan recipients especially for categorical datasets.
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Alibakhshi, Reza, and Mohammad Reza Sadeghi Moghadam. "A new algorithm for mutual funds evaluation based on multiple attribute decision making techniques." Kybernetes 45, no. 8 (2016): 1194–212. http://dx.doi.org/10.1108/k-10-2015-0256.
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Purpose The purpose of this paper is to consider compromise solutions of multiple attribute decision-making methods (TOPSIS, VIKOR, and similarity-based approach) in order to evaluate and rank mutual funds and to compare the capabilities of different approaches based on the different traditional indices of mutual funds assessment. In addition, a new algorithm for ranking mutual funds was proposed subsequently. Design/methodology/approach In this research, three groups of indices including general, risk-modified performance evaluation, and risk-modified performance evaluation indices using semivariance were used in the mutual funds assessment, which led to the comparison between selected mutual funds, using three mentioned methods and three different groups of criteria. The results of this comparison were compiled and synthesized with linear assignment method. At the end, an algorithm for decision making and investing in mutual funds for professional and unprofessional investors was proposed. Findings Using different methods and different criteria proved that the results of similarity-based approach as a MADM technique have the ability to rank and evaluate mutual funds regardless of the criteria used compared to TOPSIS and VIKOR. Furthermore, the authors propose the algorithm of this research as a new model of mutual funds evaluation which considers a wide range of variables with respect to amateur and professional points of view. Originality/value The originality of this paper is threefold: first, different criteria were considered to make the evaluation more comprehensive. Second, four different approaches were used to make the results more authentic. Third, a holistic algorithm with its implication was proposed.
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Yang, Lei, and Gang Chen. "Improving Interior Design Quality Evaluation With Advanced Cross-Entropy Technique in Triangular Fuzzy Neutrosophic Multiple-Attribute Decision-Making." International Journal of Decision Support System Technology 16, no. 1 (2024): 1–16. https://doi.org/10.4018/ijdsst.361893.
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Society is constantly developing, and people's economic level is also constantly improving. The quality of life and requirements are also increasing, and the quality of living environment is becoming more stringent. The interior design quality evaluation is viewed as the MADM. In this work, the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) technique is constructed in line with cross-entropy and triangular fuzzy neutrosophic sets (TFNSs). Then, the TFNN-CE technique is constructed to solve the MADM. Finally, a numerical example for interior design quality evaluation is constructed and serval comparisons are constructed to illustrate the advantages of put forward technique. The key contribution of this study is constructed: (1) the TFNN-CE model is put forward under TFNSs; (2) the TFNN-CE model is put forward for MADM under TFNSs; (3) the TFNN-CE model for interior design quality evaluation is put forward; (4) through employing serval comparisons, it is put forward that TFNN-CE model is effective for interior design quality evaluation.
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Senapati, Tapan, Radko Mesiar, Vladimir Simic, Aiyared Iampan, Ronnason Chinram, and Rifaqat Ali. "Analysis of Interval-Valued Intuitionistic Fuzzy Aczel–Alsina Geometric Aggregation Operators and Their Application to Multiple Attribute Decision-Making." Axioms 11, no. 6 (2022): 258. http://dx.doi.org/10.3390/axioms11060258.
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When dealing with the haziness that is intrinsic in decision analysis-driven decision making procedures, interval-valued intuitionistic fuzzy sets (IVIFSs) can be quite effective. Our approach to solving the multiple attribute decision making (MADM) difficulties, where all of the evidence provided by the decision-makers is demonstrated as interval-valued intuitionistic fuzzy (IVIF) decision matrices, in which all of the components are distinguished by an IVIF number (IVIFN), is based on Aczel–Alsina operational processes. We begin by introducing novel IVIFN operations including the Aczel–Alsina sum, product, scalar multiplication, and exponential. We may then create IVIF aggregation operators, such as the IVIF Aczel–Alsina weighted geometric operator, the IVIF Aczel–Alsina ordered weighted geometric operator, and the IVIF Aczel–Alsina hybrid geometric operator, among others. We present a MADM approach that relies on the IVIF aggregation operators that have been developed. A case study is used to demonstrate the practical applicability of the strategies proposed in this paper. By contrasting the newly developed technique with existing techniques, the method is capable of demonstrating the advantages of the newly developed approach. A key result of this work is the discovery that some of the current IVIF aggregation operators are subsets of the operators reported in this article.
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Mahmood, Tahir, Ubaid Ur Rehman, and Muhammad Naeem. "A novel approach towards Heronian mean operators in multiple attribute decision making under the environment of bipolar complex fuzzy information." AIMS Mathematics 8, no. 1 (2023): 1848–70. http://dx.doi.org/10.3934/math.2023095.
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<abstract> <p>One of the most effective and impressive approaches to tackle uncertainty is the theory of bipolar complex fuzzy set (BCFS). The theory of BCFS modified the theory of fuzzy set (FS), bipolar FS (BFS), and complex FS. Further, the Heronian mean (HM) and generalized HM (GHM) give the aggregation operators (AOs), which have the benefits of taking into account the interrelatedness among the parameters. Up till now, in the prevailing literature, these operators are not introduced in the setting of BCFS. Thus, in this article, our goal is to introduce HM and GHM operators under a bipolar complex fuzzy setting. Firstly, we initiate the bipolar complex fuzzy generalized Heronian mean (BCFGHM) operator. Then, a few of its particular cases by changing the values of the parameter to show its supremacy. We also initiate the bipolar complex fuzzy weighted generalized Heronian mean (BCFWGHM) operator. Secondly, we interpret a method called the "multiple attribute decision making" (MADM) procedure by employing the initiated operators. Next, we provide a descriptive example (selection of the finest renewable energy generation project) to portray the applicability and usefulness of the initiated MADM procedure. Finally, to demonstrate the usefulness of the propounded operators and MADM procedure we compare our initiated work with several present operators and MADM techniques.</p> </abstract>
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Saghari, Asad, Ivana Budinská, Masoud Hosseinimehr, and Shima Rahmani. "A Robust-Reliable Decision-Making Methodology Based on a Combination of Stakeholders’ Preferences Simulation and KDD Techniques for Selecting Automotive Platform Benchmark." Symmetry 15, no. 3 (2023): 750. http://dx.doi.org/10.3390/sym15030750.
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The automotive family design is known as one of the most complex engineering design problems with multiple groups of stakeholders involved from different domains of interest and contradictory attributes. Taking into account all stakeholders’ preferences, which are generally symmetrical, non-deterministic distributions around a mean value, and determining the right value of attributes for each alternative are two basic challenges for these types of decision-making problems. In this research, the possibility to achieve a robust-reliable decision by focusing on the two aforementioned challenges is explored. In the proposed methodology, a random simulation technique is used to elicit stakeholders’ preferences and determine the relative importance of attributes. The decision space and values of attributes are determined using the Knowledge Discovery in Databases (KDD) technique, and to achieve a robust-reliable decision, statistical and sensitivity analyses are performed. By implementing this methodology, the decision-maker is assured that the preferences of all stakeholders are taken into account and the determined values for attributes are reliable with the least degree of uncertainty. The proposed methodology aims to select benchmark platforms for the development of an automotive family. The decision space includes 546 automobiles in 11 different segments based on 34 platforms. There are 6223 unique possible states of stakeholders’ preferences. As a result, five platforms with the highest degree of desirability and robustness to diversity and uncertainty in the stakeholders’ preferences are selected. The presented methodology can be implemented in complex decision-making problems, including a large and diverse number of stakeholders and multiple attributes. In addition, this methodology is compatible with many Multi-Attribute Decision-Making (MADM) techniques, including SAW, AHP, SWARA, and TOPSIS.
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Tirth, Vineet, Ram Karan Singh, Saiful Islam, et al. "Kharif Crops Selection for Sustainable Farming Practices in the Rajasthan-India Using Multiple Attribute-Based Decision-Making." Agronomy 10, no. 4 (2020): 536. http://dx.doi.org/10.3390/agronomy10040536.
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Sustainability of agricultural practices depends on economic, environmental, and social conditions. The Rajasthan state of India has arid climatic conditions where kharif crops are commonly grown. In this work, the four major criteria are considered such as the farm area, crop yield per unit area, the cost prices, and the market sales price. Merged analytic hierarchy process (AHP) and entropy techniques have been employed to give reasonable weight coefficients for the objective and subjective weights to each criterion. Multiple attribute-based decision-making models (MADM) have been developed using three proven techniques, namely the Exprom2, the technique for order of preference by similarity to ideal solution (TOPSIS), and the VlseKriterijumska Optimizacija I Kompromisno Resenje (VIKOR). The crop Pennisetum glaucum emerged as the most productive kharif crop in the arid climatic conditions of Rajasthan, India under the given criteria. The sensitivity analysis of the three methods identifies the most significant criteria and validates that Pennisetum glaucum is the first ranked crop despite the interchange of the weights. The methodology used in this study may be applied across the globe to select appropriate crops for maximizing the profit, optimizing the natural resources, and promoting sustainable agricultural practices. This study may be used to enhance the agricultural gross domestic product (GDP) to make the agriculturalists self-sufficient and to help the state policymakers in making effective regional policies.
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Mahmood, Tahir, Ahmad Idrees, Majed Albaity, and Ubaid ur Rehman. "Selection of artificial intelligence provider via multi-attribute decision-making technique under the model of complex intuitionistic fuzzy rough sets." AIMS Mathematics 9, no. 11 (2024): 33087–138. http://dx.doi.org/10.3934/math.20241581.
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<p>Choosing an optimal artificial intelligence (AI) provider involves multiple factors, including scalability, cost, performance, and dependability. To ensure that decisions align with organizational objectives, multi-attribute decision-making (MADM) approaches aid in the systematic evaluation and comparison of AI vendors. Therefore, in this article, we propose a MADM technique based on the framework of the complex intuitionistic fuzzy rough model. This approach effectively manages the complex truth grade and complex false grade along with lower and upper approximation. Furthermore, we introduced aggregation operators based on Dombi t-norm and t-conorm, including complex intuitionistic fuzzy rough (CIFR) Dombi weighted averaging (CIFRDWA), CIFR Dombi ordered weighted averaging (CIFRDOWA), CIFR Dombi weighted geometric (CIFRDWG), and CIFR Dombi ordered weighted geometric (CIFRDOWG) operators, which were integrated into our MADM technique. We then demonstrated the application of this technique in a case study on AI provider selection. To highlight its advantages, we compared our proposed method with other approaches, showing its superiority in handling complex decision-making scenarios.</p>
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Dwivedi, S., V. Mishra, and Y. Kosta. "Application of Teaching Learning Based Optimization in antenna designing." Advanced Electromagnetics 4, no. 1 (2015): 68. http://dx.doi.org/10.7716/aem.v4i1.255.
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Numerous optimization techniques are studied and applied on antenna designs to optimize various performance parameters. Authors used many Multiple Attributes Decision Making (MADM) methods, which include, Weighted Sum Method (WSM), Weighted Product Method (WPM), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Analytic Hierarchy Process (AHP), ELECTRE, etc. Of these many MADM methods, TOPSIS and AHP are more widely used decision making methods. Both TOPSIS and AHP are logical decision making approaches and deal with the problem of choosing an alternative from a set of alternatives which are characterized in terms of some attributes. Analytic Hierarchy Process (AHP) is explained in detail and compared with WSM and WPM. Authors fi- nally used Teaching-Learning-Based Optimization (TLBO) technique; which is a novel method for constrained antenna design optimization problems.
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Al-Mashhadani, Athraa, Muna Salman, and Fatimah Al-Yasiri. "Quality of service attributes based hybrid decision-making framework for ranking cloud service providers under Fermatean fuzzy environment." Yugoslav Journal of Operations Research, no. 00 (2024): 10. http://dx.doi.org/10.2298/yjor231215010a.
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Cloud Computing has gained substantial popularity due to its ability to offer diverse and dependable computing services suited to clients demands. Given the rapid expansion of this technology, an increasing number of IT service providers are competing to deliver cloud services that are both of excellent quality and cost-efficient, in order to best meet the requirements of their clients. With the extensive range of options available, selecting the best Cloud Service Provider (CSP) has become a challenging dilemma for the majority of cloud clients. When evaluating services offered by many CSPs, it is important to consider multiple attributes. Efficiently addressing the selection of the best CSP involves tackling a challenging Multi-Attribute Decision Making (MADM) problem. Several MADM techniques have been proposed in academic literature for evaluating CSPs. However, the persisting problems of inconsistency, uncertainty, and rank reversal remain unresolved. In this paper the authors present a hybrid MADM framework to rank eight CSPs using nine Quality of Service (QoS) attributes. In order to achieve this objective, Fermatean fuzzy sets-full consistency method (FFS-FUCOM) is combined with Grey-Relational-Analysis and the Technique-for-Order-Preference-by-Similarity-to-Ideal-Solution (Grey-TOPSIS) technique. The framework successfully resolved the aforementioned problems. Sensitivity analysis is conducted to assess the stability and robustness of the results produced by the proposed framework. The sensitivity analysis results indicate that the proposed framework offers an accurate and robust solution. A systematic ranking test is undertaken to ensure that the results are ranked in a systematic manner. Additionally, a comparative analysis is carried out with the most relevant study.
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Liu, Mengxue. "Enhanced Cross-Entropy Technique for Triangular Fuzzy Neutrosophic Multiple-Attribute Decision-Making and Applications to Quality Evaluation of Urban-Park Environmental Design." International Journal of Fuzzy System Applications 13, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijfsa.357694.
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The evaluation of urban park environmental design quality is essential for ensuring that parks meet community needs and environmental sustainability. By comprehensively considering indicators such as functionality, aesthetics, sustainability, and user experience, this evaluation helps designers optimize spatial layouts and enhance the efficiency and aesthetic value of parks. Additionally, good environmental design promotes ecological protection, improves the quality of life for residents, and adds more green spaces to the city, making parks an integral part of daily urban life. The evaluation of urban park environmental design quality is framed as a multiple-attribute decision-making (MADM) problem. This study introduces the triangular fuzzy neutrosophic number cross-entropy (TFNN-CE) method, which integrates traditional cross-entropy with triangular fuzzy neutrosophic sets (TFNSs). Subsequently, the TFNN-CE technique for MADM is established. A numerical example illustrating the evaluation of urban park environmental design quality is provided, alongside several comparative analyses to highlight the advantages of the proposed approach. The key contributions of this research are as follows: (1) the TFNN-CE technique is introduced through the integration of cross-entropy and TFNSs; (2) the TFNN-CE technique is formulated to address MADM problems using TFNSs; (3) an empirical application for assessing urban park environmental design quality is presented to validate the proposed methodology; (4) comparative studies are conducted to substantiate the rationality of the TFNN-CE technique.
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Subramani, Meenakshi, and Vinoth Babu Kumaravelu. "A fuzzy based vertical handover network selection scheme for device-to-device communication." Indonesian Journal of Electrical Engineering and Computer Science 17, no. 1 (2020): 324. http://dx.doi.org/10.11591/ijeecs.v17.i1.pp324-330.
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<p>One of the most attractive and challenging areas in the upcoming next-generation 5G wirelessnetworkistheverticalhandover(VHO).Recently,manyoftheheterogeneous wireless communication technologies are introduced to satisfy the demands of users in all situations. Due to the deployment of heterogeneous networks, the users can access the internet anywhere, anytime through different wireless networks. To obtain seamless service and service continuity, the device should be handed over to the best wireless networks. Here, a half handover scheme for Device-to-Device (D2D) communication is implemented for the selection of the best network. The target network selection for vertical handover can be handled using multiple attribute decision making (MADM) methods. An intelligent and fast vertical handover decision is much needed, which should be reliable even for random and uncertain environments. Fuzzy logic is proved to be effective in handling imprecise data. Hence, in this work, the impact of combining fuzzy with the conventional MADM scheme, simple additive weighting(SAW)isanalyzedandthehybridschemeiscomparedwiththeconventional MADM schemes like SAW, Techniques for order preference by similarity to ideal solution (TOPSIS), VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR) in terms of handover decision delay. Since, the numbers of handovers executed are low,thehandoverdecisiondelayperformanceoftheproposedschemeissuperiorthan the considered classical MADM schemes.</p>
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Meenakshi, Subramani, and Babu Kumaravelu Vinoth. "A fuzzy based vertical handover network selection scheme for device-to-device communication." Indonesian Journal of Electrical Engineering and Computer Science (IJEECS) 17, no. 1 (2020): 324–30. https://doi.org/10.11591/ijeecs.v17i1.pp324-330.
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One of the most attractive and challenging areas in the upcoming next-generation 5G wireless network is the vertical handover (VHO). Recently, many of the heterogeneous wireless communication technologies are introduced to satisfy the demands of users in all situations. Due to the deployment of heterogeneous networks, the users can access the internet anywhere, anytime through different wireless networks. To obtain seamless service and service continuity, the device should be handed over to the best wireless networks. Here, a half handover scheme for Device-to-Device (D2D) communication is implemented for the selection of the best network. The target network selection for vertical handover can be handled using multiple attribute decision making (MADM) methods. An intelligent and fast vertical handover decision is much needed, which should be reliable even for random and uncertain environments. Fuzzy logic is proved to be effective in handling imprecise data. Hence, in this work, the impact of combining fuzzy with the conventional MADM scheme, simple additive weighting (SAW) is analyzed and the hybrid scheme is compared with the conventional MADM schemes like SAW, Techniques for order preference by similarity to ideal solution (TOPSIS), VlseKriterijumska optimizacija I Kompromisno Resenje (VIKOR) in terms of handover decision delay. Since, the numbers of handovers executed are low, the handover decision delay performance of the proposed scheme is superior than the considered classical MADM schemes.
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Ramachandran, M., Manjula Selvam, and Vidhya Prasanth. "Performance evaluation of Wireless Network selection using Gray Rational Analysis (GRA) Method." 1 1, no. 1 (2022): 9–16. http://dx.doi.org/10.46632/jeae/1/1/2.
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This paper proposes a network selection solution that is user location- and knowledge-based, aiming to diversify by choosing the best value network in a wireless network environment, improving video delivery. When multi-attribute decision making (MADM) techniques are used for wireless network vertical approval, the impact of weighting techniques combined with Gray Rational Analysis (GRA) is studied. Performance analysis of the GRA algorithm is conducted for hierarchical, background, conversational, interactive, and streaming classes of services using Random Weighting (RW) and Least Square Weight (LSW) techniques. In the MCDM context, multiple alternatives (e.g., wireless networks) refer to attributes such as Network 1, Network 2, Network 3, Network 4, and Network 5. Delay (msec), Jitter (msec), BER (x 10), Throughput (kbps), and Cost (units) are used as evaluation methods. In this type of analysis, gray relational analysis (GRA) methods determine the best solution for negative short distances and very long distances to settlement, but do not consider the relative importance of these distances. As a result, Wireless Network 5 ranked first, and Wireless Network 4 ranked the lowest.
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Palanikumar, Murugan, Nasreen Kausar, Shams Forruque Ahmed, Seyyed Ahmad Edalatpanah, Ebru Ozbilge, and Alper Bulut. "New applications of various distance techniques to multi-criteria decision-making challenges for ranking vague sets." AIMS Mathematics 8, no. 5 (2023): 11397–424. http://dx.doi.org/10.3934/math.2023577.
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<abstract><p>Using the Fermatean vague normal set (FVNS), problems requiring multiple attribute decision making (MADM) have been resolved in this article. This article focuses on the log Fermatean vague normal weighted averaging (log FVNWA), logarithmic Fermatean vague normal weighted geometric (log FVNWG), log generalized Fermatean vague normal weighted averaging (log GFVNWA) and log generalized Fermatean vague normal weighted geometric (log GFVNWG) operators. Described the scoring function, accuracy function and operational laws of the log FVNS. The Euclidean and Humming distance are extended with numerical examples. The features of the log FVNS based on the algebraic operations, including idempotency, boundedness, commutativity and monotonicity are also examined. A field of applied engineering called agricultural robotics has been compared to computer science and machine tool technology. Five distinct agricultural robotics including autonomous mobile robots, articulated robots, humanoid robots, cobot robots, and hybrid robots are randomly chosen. Findings can be compared to established criteria to determine which robotics are the most successful. The results of the models are expressed as a natural number $ \alpha $. We contrast several existing with those that have been developed in order to show the effectiveness and accuracy of the models.</p></abstract>
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Xiong, Lei, Cheng-Lein Teng, Yu-Qi Li, Yuan-Zone Lee, Bo-Wei Zhu, and Kun Liu. "A Qualitative-Quantitative Evaluation Model for Systematical Improving the Creativity of Students’ Design Scheme." Sustainability 11, no. 10 (2019): 2792. http://dx.doi.org/10.3390/su11102792.
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For teaching architectural design, if creativity in students’ practice assignments can be effectively evaluated, it will help make certain the ranking of the performance of different design schemes and provide a critical decision basis for the continuous improvement of each scheme. Based on the review of related literature, this study integrates the grounded theory (GT) from the social sciences’ domain and the method of multiple attribute decision making (MADM) from operations research by analyzing their advantages and congruence to create a qualitative–quantitative evaluation model such as the GT-DANP-mV (GT, DEMATEL-based analytic network process [ANP] with modified VIKOR) model. Then, the characteristics and advantages of this evaluation model were examined by applying it to empirical research. Overall, this evaluation model fully utilized the advantages of the analytical techniques of GT and the MADM method to solve the corresponding problems at different stages of the evaluation study, which realizes their complementary advantages. More crucial is that, whether in the related evaluation study or practice assignments, the application of the GT-DANP-mV model could facilitate the formulation of continuous improvement strategies for architectural design schemes based on performance evaluation results under conditions of limited resources.
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Beiki Ashkezari, Ali, Mahsa Zokaee, Amir Aghsami, Fariborz Jolai, and Maziar Yazdani. "Selecting an Appropriate Configuration in a Construction Project Using a Hybrid Multiple Attribute Decision Making and Failure Analysis Methods." Buildings 12, no. 5 (2022): 643. http://dx.doi.org/10.3390/buildings12050643.
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To successfully complete a project, selecting the most appropriate construction method and configuration is critical. There are, however, plenty of challenges associated with these complex decision-making processes. Clients require projects with the desired cost, time, and quality, so contractors should trade-off project goals through project configuration. To address this problem, in this study, an integrated FTA-DFMEA approach is proposed that implements the integrated AHP-TOPSIS method to improve construction project configuration. The proposed approach applies quality management techniques and MADM methods concurrently for the first time to improve construction project configuration considering project risks, costs and quality. At first, the Client’s requirements and market feedback are considered to identify potential failures in fulfilling project goals, and an integrated AHP-TOPSIS is used to select the most critical potential failure. Then fault tree analysis is used to indicate minimal paths. An inverse search in the operational model is performed to determine relevant tasks and identify defective project tasks based on WBS. Afterward, failure modes and effect analysis are applied to identify failure modes, and an integrated AHP-TOPSIS is used to rank failure modes and select the most critical one. Then Corrective actions are carried out for failure modes based on their priority, and project configuration is improved. This study considers construction resource suppliers with different policies, delivery lead times, warranty costs, and purchasing costs. Moreover, redundancy allocation and different configuration systems such as series and parallel are taken into account based on the arrangement and precedence of tasks. Finally, a case study of a building construction project is presented to test the viability of the proposed approach. The results indicate that the proposed approach is applicable as a time-efficient and powerful tool in the improvement of construction project configuration, which provides the optimal output by considering various criteria with respect to the client’s requirements and contractor’s obligations. Moreover, the algorithm provides various options for the contractor to improve the implementation of construction projects and better respond to challenges when fulfilling project goals.
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Sii, H. S., and J. Wang. "A design—decision support framework for evaluation of design options/proposals using a composite structure methodology based on the approximate reasoning approach and the evidential reasoning method." Proceedings of the Institution of Mechanical Engineers, Part E: Journal of Process Mechanical Engineering 217, no. 1 (2003): 59–76. http://dx.doi.org/10.1243/09544080360562990.
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With the cost of construction, operation and maintenance estimates in the multi-millions of pounds, the offshore industry is seeking ways of reducing both the time and money spent in providing the high-quality offshore structures needed to support oil and gas extraction and production. Most real world design evaluation and risk-based decision support combine quantitative and qualitative (linguistic) variables. Decision making based on conventional mathematics that combines qualitative and quantitative concepts always exhibits difficulty in the modelling of actual problems. The successful selection process for choosing a design/procurement proposal is based on a high degree of technical integrity, safety levels and low costs in construction, corrective measures, maintenance, operation, inspection, as well as preventive measures. However, the objectives of maximizing the degree of technical performance, maximizing the safety levels and minimizing the costs incurred are usually in conflict, and the evaluation of the technical performance, safety and costs is always associated with uncertainty, especially for a novel system at the initial concept design stage. In this paper, a design—decision support framework using a composite structure methodology grounded in an approximate reasoning approach and an evidential reasoning method is suggested for design evaluation of offshore engineering products at the initial stages. It is a multiple attribute decision-making (MADM) or multiple-criteria decision-making framework, which provides a juxtaposition of cost, safety, and technical performance and other objectives of a system during evaluation to assist decision makers in selecting the winning design/procurement proposal that best satisfies the requirement in hand. It has also been shown that the formal decision-making techniques such as the Analytical Hierarchy Process (AHP) and the Delphi method can be incorporated with the proposed framework in carrying out design support evaluation. An illustrative example is used to demonstrate the application of the proposed framework.
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-, Teguh Sujana, and Rahmiati -. "Evaluasi Dosen Dibidang Pengajaran Menggunakan Metode Technique For Order Preference By Similarity To Ideal Solution (TOPSIS) (Studi Kasus : STMIK-AMIK Riau)." SATIN - Sains dan Teknologi Informasi 3, no. 2 (2018): 9–18. http://dx.doi.org/10.33372/stn.v3i2.361.
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Salah satu penentu kualitas pendidikan tinggi adalah dosen. Dosen yang kompeten untuk melaksanakan tugasnya secara profesional adalah dosen yang memiliki kompetensi pedagogik, profesional, kepribadian dan sosial, sehingga perlu adanya penilaian yang mengacu kepada kompetensi dosen. Untuk mendapatkan hasil yang lebih sesuai dengan kualitas dosen maka penilaian yang dilakukan tidak hanya dari mahasiswa, tetapi juga dari teman sejawat, atasan, dan diri sendiri. Namun untuk memilih yang terbaik dari berbagai aspek penilaian menjadikan kendala dalam evaluasi dosen dibidang pengajaran. Metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) didasarkan pada konsep dimana alternatif terpilih yang terbaik tidak hanya memiliki jarak terpendek dari solusi ideal positif, namun juga memiliki jarak terpanjang dari solusi ideal negatif. Konsep ini banyak digunakan pada beberapa model MADM untuk menyelesaikan masalah keputusan secara praktis. Multiple Attribute Decision Making (MADM) digunakan untuk menyelesaikan masalah-masalah dalam ruang diskret. Oleh karena itu MADM biasanya digunakan untuk melakukan penilaian atau seleksi terhadap beberapa alternatif dalam jumlah yang terbatas. Hasil dari penelitian ini adalah dengan penggunaan metode TOPSIS dalam evaluasi dosen dibidang pengajaran dapat ditentukan dosen terbaik dibidang pengajaran yang berkompeten sehingga diharapkan dapat mendorong dosen untuk secara berkelanjutan meningkatkan profesionalismenya
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Cheng, Ching Hsue, Sue Fen Huang, and Chen Tung Chen. "A Study on Enterprise Performance Prediction Model by Reducing Financial Ratio." Advanced Materials Research 211-212 (February 2011): 1221–25. http://dx.doi.org/10.4028/www.scientific.net/amr.211-212.1221.
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Financial ratio is an important indicator to represent the operation performance of an enterprise. Operating performance is defined as some important financial ratios which can indicate enterprise financial with positive operation and growth. The strength and weakness of financial health can not only show the results of operation performance of the organization, but also indicate its growth and potential in the future. However, how to select a set of representative financial ratios is an important issue for evaluating the operating performance of an enterprise. Traditionally, more related researchers have long used statistical methods for handling these problems. However, these conventional methods become more complex when relationships in the input/output dataset are nonlinear. Nevertheless, statistical techniques always rely on the restrictive assumptions on linear separability for the predictive variables, multivariate normality, and many of the models of financial performance violate these assumptions. Therefore, to overcome these existing shortcomings, the proposal proposed attribute selection method to extract financial ratio attributes and OWA based multiple attribute decision making (MADM) to analyze enterprise operating performance situation for stakeholders (i.e., management, investors, employees, shareholders and other interested parties). The financial ratios are collected from the open source information retrieval systems of dataset for publicly traded enterprises in Taiwan stock market from 2008. The proposed model use singular value decomposition (SVD) + ordered weight averaging (OWA) for evaluating enterprise operating performance. At last, the results indicate that the proposed selection attribute can explain enterprise financial situation, and proposed model can objectively evaluate the performance of enterprise.
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Nakhoda, Maryam, Sirous Alidousti, and Gholam Reza Fadaie. "Selecting an appropriate change management model to the Iranian academic libraries using MADM methods." libri 61, no. 3 (2011): 190–204. https://doi.org/10.5281/zenodo.13985523.
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Today's life is not insulated from different kinds of “change.‎” Change is also experienced in organizations as a somehow daily process.‎ Academic libraries are organizations which need to be active in confrontation with “change” because of their essential role in education and research.‎ These libraries could be more successful in meeting users' information needs, if they manage change in a planned manner.‎ In this regard, Iranian academic libraries are not exceptional.‎ They are faced with pressures from their internal and external environments, which make it essential for them to respond and implement necessary changes.‎ In order to assist these libraries in overcoming challenges stemming from change and improving the change management capacity in libraries, this article aims to select an appropriate change management model for Iranian academic libraries.‎ After reviewing the literature, finding and selecting basic change models including Kotter's Change Model, Penfold's Information Services Framework for Managing Change, and Curzon's Cycle of Change, a research questionnaire was designed and answered by a group of experts using the consensus method.‎ The results were analyzed by Multiple Attribute Decision Making (MADM) techniques including Technique for Order- Preference by Similarity to Ideal Solution (TOPSIS) and Simple Additive Weighting Method (SAWM).‎ Penfold's Information Services Framework for Managing Change received the highest scores from the application of both techniques.‎ It is concluded that Penfold's Information Services Framework for Managing Change could be applied as an appropriate change model in the context of Iranian academic libraries.‎
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Lai, Hsin-Hung, Kuei-Hu Chang, and Chien-Liang Lin. "A Novel Method for Evaluating Dredging Productivity Using a Data Envelopment Analysis-Based Technique." Mathematical Problems in Engineering 2019 (January 21, 2019): 1–22. http://dx.doi.org/10.1155/2019/5130835.
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The increase in the frequency of extreme weather has caused the impact of natural disasters to become more extensive. Natural disasters reduce the effective storage capacity of reservoirs and affect their normal function. Reservoir dredging is a key issue in the management of water resources and is a complicated multiple-attribute decision-making (MADM) problem. The traditional assessment of dredging productivity has been performed using a labor productivity method to evaluate the related issues of dredging performance. However, the traditional labor productivity method only deals with the single-input/single-output evaluation factor for various forms of productivity. The traditional labor productivity method cannot address complicated MADM problems in the assessment of dredging productivity. To resolve the limitations of the traditional labor productivity method, this paper extended data envelopment analysis (DEA) and proposed a novel method for evaluating dredging productivity. The proposed method can handle various combinations of evaluation factors (single-input, multi-input, single-output, and multioutput). Three real cases of reservoir dredging are applied to verify the effectiveness of the proposed method. The simulation results show that the proposed method can be applied generally and correctly assesses the related issues of dredging performance.
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Wang, Chia-Nan, Yu-Chi Chung, Fajar Dwi Wibowo, Thanh-Tuan Dang, and Ngoc-Ai-Thy Nguyen. "Sustainable Last-Mile Delivery Solution Evaluation in the Context of a Developing Country: A Novel OPA–Fuzzy MARCOS Approach." Sustainability 15, no. 17 (2023): 12866. http://dx.doi.org/10.3390/su151712866.
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With the surge in e-commerce volumes during COVID-19, improving last-mile logistics is extremely challenging, specifically for developing economies, due to poor infrastructures, lack of stakeholders’ cooperation, and untapped resources. In the context of Vietnam, there are certain solutions that can bring more efficient and sustainable last-mile logistics. In this paper, to evaluate and rank these potentially sustainable last-mile solutions (LMSs), we propose a novel hybrid multiple attribute decision-making (MADM) model that combines the Ordinal Priority Approach (OPA) and fuzzy Measurement of Alternatives and Ranking according to the COmpromise Solution (fuzzy MARCOS). Twelve sustainability factors of technical, economic, social, and environmental aspects were determined through a literature review and experts’ opinions to employ the MADM approach. A case study evaluating five LMSs in Vietnam concerning their sustainable implementation is solved to exhibit the proposed framework’s applicability. From the OPA findings, “efficiency”, “costs of implementation and control”, “voice of customer”, “reliability”, and “flexibility” are the topmost criteria when considering a new LMS implementation in the context of Vietnam. Moreover, sensitivity analysis and comparative analysis were performed to test the robustness of the approach. The results illustrate that the applied methods reach consistent solution rankings, where LMS-03 (convenience store pickup), LMS-02 (parcel lockers), and LMS-01 (green vehicles) are the best solutions in Vietnam. The study holds novelty in evaluating last-mile initiatives for Vietnam by utilizing a unique approach in the form of two novel MADM techniques, thus providing significant insights for research and applications.
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Hady Sofyan, Hady Sofyan, Yansa Nur Sany, and Haris Sandi Yudha. "Pemilihan Supplier Material Baja untuk Proyek Tower Menggunakan Metode Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) di PT. XSF." Jurnal Teknologika 13, no. 2 (2023): 170–78. http://dx.doi.org/10.51132/teknologika.v13i2.278.
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Menentukan pemasok untuk pengadaan barang merupakan tugas yang rumit karena banyak kriteria yang harus dipertimbangkan dan beberapa kriteria sulit untuk dinilai. Perubahan kinerja pemasok dan kurangnya informasi yang tersedia dapat mempersulit pengambilan keputusan. Pemilihan pemasok yang tepat dapat membantu mengurangi biaya operasional, meningkatkan keuntungan dan kualitas produk, meningkatkan daya saing di pasar, dan memenuhi kebutuhan pelanggan secara efisien. Oleh karena itu, tujuan dari penelitian ini adalah untuk mengembangkan suatu metode yang dapat membantu praktisi dalam memilih pemasok secara logis, terutama dalam situasi ketidakpastian dan/atau kekurangan informasi. Implementasi metode multi-kriteria decision making (MCDM) digunakan untuk memilih solusi terbaik berdasarkan kriteria yang telah ditetapkan. Salah satu metode MCDM yang dapat digunakan adalah Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), yang dipilih dalam beberapa model Multiple Attribute Decision Making (MADM) karena konsepnya yang sederhana dan mudah dipahami, serta memiliki beberapa keunggulan. Penelitian ini dilakukan pada perusahaan rekayasa dan fabrikasi struktur baja dimana salah satu produk yang dihasilkan adalah konstruksi baja untuk jembatan, tower, dan fabrikasi. Bahan utama yang digunakan adalah baja siku. Dikarenakan banyaknya supplier yang dapat menyediakan barang maka penelitian ini mengkaji tentang pemilihan supplier baja siku untuk material utama perusahaan. Hasil perhitungan TOPSIS dalam mensuplai bahan baku utama adalah supplier PT. KW dengan memperhatikan 4 kriteria yaitu Kualitas, Pengiriman, Garansi Dan Kebijakan Klaim, dan Harga.
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Nugroho, Oktafianto Adhi, and Akbar Adhi Utama. "Improving Regional Sulawesi Downstream Oil and Gas Construction Risk Management: A Literature Review." European Journal of Business and Management Research 10, no. 2 (2025): 28–32. https://doi.org/10.24018/ejbmr.2025.10.2.2603.
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Managing risk effectively is critical for the success of businesses, particularly in high-stakes industries such as downstream oil and gas construction. This sector can be defined by complex initiatives that are highly vulnerable to financial, immediate, and operational hazards. Effective risk management not only reduces possible losses but also improves decision-making and resource allocation, therefore guaranteeing the achievement of project objectives. This study constructs a conceptual framework to establish a Risk Management Process that aligns with organizational needs by integrating elements from two globally recognized frameworks: PMBOK (Project Management Body of Knowledge) and AACE (Association for the Advancement of Cost Engineering). The proposed methodology combines the strengths of both frameworks to address the key project management requirements and resolve cost- and schedule-related challenges, which are critical in downstream oil and gas projects. The framework's development uses Multiple Attribute Decision-Making (MADM) methods to systematically identify and prioritize relevant risk management qualities. The initial screening utilized tools including Likert-scale analysis and Non-Dimensional Scaling, while ordinal ranking and weighting factor methods ensured conformity with organizational priorities and objectives. This systematic approach facilitated the identification of the most relevant risk management techniques from PMBOK and AACE, resulting in a tailored strategy designed to enhance project performance. The study's findings highlight the necessity of integrating strategies to address industry-specific challenges, providing a practical and adaptable framework for risk management in complex projects. This conceptual framework establishes a foundation for future empirical research and can be applied across many sectors and enterprises to enhance risk management effectiveness.
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ZOLFANI, Sarfaraz HASHEMKHANI, and Reza MAKNOON. "MULTIPLE ATTRIBUTE DECISION MAKING (MADM) BASED SCENARIOS." International Journal of Strategic Property Management 20, no. 1 (2016): 101–11. http://dx.doi.org/10.3846/1648715x.2015.1132487.
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Decision making takes into account a myriad of factors about the future topics, which often prove challenging and quite complicated. Multiple Attribute Decision-Making (MADM) methods still have not become powerful enough to help decision makers to adopt the best solutions regarding future issues. Different scenarios are suitable for developing an appropriate outlook toward different probable futures. Scenarios are not inherently quantitative, but recently different integrated quantitative methods have been incorporated with the processes in various studies. Previously, different types of scenario-based MADM methods have been presented in different studies, but they just considered each case separately. In those studies, MADM methods were only applied to evaluate the situation in scenario-based MADM. This research concentrates on another paradigm in applying scenarios to upcoming events, MADM methods in the new area are explored, and the concept, which is called MADM based scenarios, is presented. In different situations and scenarios, different MADM models will happen. New concepts about most useful criterion and applicable alternatives are introduced in this new approach for decision-making about the future. In addition, a general framework is proposed for applying MADM-based scenarios for unpredictable scenarios and situations, which can be almost controlled future in practice.
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CHEN, YUH-WEN. "A GROUP GAME OF MULTIPLE ATTRIBUTE DECISION MAKING." Asia-Pacific Journal of Operational Research 24, no. 05 (2007): 631–45. http://dx.doi.org/10.1142/s0217595907001425.
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Multiple Attribute Decision Making (MADM) problem is a management science technique, which is popularly used to rank the priority of alternatives with respect to their competing attributes. It is popularly used in diverse fields such as engineering management, portfolio selection, transportation planning, and performance evaluation. Weights form the core of MADM: it is obvious that different weights lead to various evaluation results and decisions. Several approaches have been developed for assessing the weights of MADM problems, e.g., the eigenvector method, ELECTRE, and TOPSIS. However, an assessment approach of weights in MADM, which meets both the need of simplicity interface for practitioners and concrete theory for scholars is not easy, and balancing these two aspects is a challenging and tough task. Since the pay-off matrix in game theory could be regarded as a simple interface for data input/output, and very few scholars had ever explored the two-person zero-sum game on MADM problems. In this paper, the weights of a MADM problem are obtained by formulating it as a two-person zero-sum game with multiple decision makers. The group equilibrium solution, i.e., consensus of weights and the resolution steps for such a group MADM game has also been originally developed and validated in this study. Finally, an actual case of selecting the appropriate portfolio decision for a paper company is illustrated.
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S. Daugherty, Mary, Thadavillil Jithendranathan, and David O. Vang. "Portfolio selection using the multiple attribute decision making model." Investment Management and Financial Innovations 18, no. 2 (2021): 155–65. http://dx.doi.org/10.21511/imfi.18(2).2021.13.
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This paper uses a Multiple Attribute Decision Making (MADM) model to improve the out-of-sample performance of a naïve asset allocation model. Under certain conditions, the naïve model has out-performed other portfolio optimization models, but it also has been shown to increase the tail risk. The MADM model uses a set of attributes to rank the assets and is flexible with the attributes that can be used in the ranking process. The MADM model assigns weights to each attribute and uses these weights to rank assets in terms of their desirability for inclusion in a portfolio. Using the MADM model, assets are ranked based on the attributes, and unlike the naïve model, only the top 50 percent of assets are included in the portfolio at any point in time. This model is tested using both developed and emerging market stock indices. In the case of developed markets, the MADM model had 24.04 percent higher return and 53.66 percent less kurtosis than the naïve model. In the case of emerging markets, the MADM model return is 90.16 percent higher than the naïve model, but with almost similar kurtosis.
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Bulut Karageyik, Başak, and David C. M. Dickson. "Optimal reinsurance under multiple attribute decision making." Annals of Actuarial Science 10, no. 1 (2015): 65–86. http://dx.doi.org/10.1017/s1748499515000123.
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AbstractWe apply methods from multiple attribute decision making (MADM) to the problem of selecting an optimal reinsurance level. In particular, we apply the Technique for Order of Preference by Similarity to Ideal Solution method with Mahalanobis distance. We consider the classical risk model under a reinsurance arrangement – either excess of loss or proportional – and we consider scenarios that have the same finite time ruin probability. For each of these scenarios we calculate three quantities: released capital, expected profit and expected utility of resulting wealth. Using these inputs, we apply MADM to find optimal retention levels. We compare and contrast our findings with those when decisions are based on a single attribute.
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Robinson, P. John, and A. Saranraj. "Intuitionistic Fuzzy Gram-Schmidt Orthogonalized Artificial Neural Network for Solving MAGDM Problems." Indian Journal Of Science And Technology 17, no. 24 (2024): 2529–37. http://dx.doi.org/10.17485/ijst/v17i24.1386.
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Objectives: To propose a suitable decision-making model based on Intuitionistic Fuzzy sets (IFSs) and Gram-Schmidt orthogonalization process for Artificial Neural Network (ANN). Methods: The IFS data sets appearing in the form of matrices are aggregated using the available aggregation operators in the literature and then the collective aggregated information is processed through Gram-Schmidt orthogonalization for the revised input vectors which is then fed into the ANN algorithm following Delta Learning Rule for the next phase. The weight updation is performed through the ANN and the output is improvised. Findings: The proposed Gram-Scmidt Orthogonalization process is utilized in Intuitionistic Fuzzy Artificial Neural Network model. The Delta learning rule is utilized in the process of the Neural Network, where the Intuitionistic Fuzzy nature of the input data is transformed into a fuzzy data and then the ranking of the alternatives is done based on the weights updation through the learning phase of the ANN. Once the vector is trained out of the learning phase, it is then processed through the activation function for the final selection of the best alternative required of the Multiple Attribute Group Decision Making (MAGDM) problem posed in this work. To demonstrate the usefulness and applicability of this new method with the Gram-Schmidt process, the numerical example also adds more insight to the proposed methodology of ANN with the application of some Linear Space techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network model are based on learning rules or using some other calculations. The proposed Gram-Schmidt Orthogonalization process is used to find the orthogonal basis that are used as input training vectors in the Delta learning rule for ANN. Keywords: MAGDM, ANN, Aggregation operators, Learning Rules, Intuitionistic Fuzzy sets, Gram-Scmidt Orthogonalization
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Zolotavkin, Yevhen, Jongkil Jay Jeong, Veronika Kuchta, Maksym Slavnenko, and Robin Doss. "Improving Unlinkability of Attribute-based Authentication through Game Theory." ACM Transactions on Privacy and Security 25, no. 2 (2022): 1–36. http://dx.doi.org/10.1145/3501260.
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This article first formalizes the problem of unlinkable attribute-based authentication in the system where each user possesses multiple assertions and uses them interchangeably. Currently, there are no recommendations for optimal usage of assertions in such authentication systems. To mitigate this issue, we use conditional entropy to measure the uncertainty for a Relying Party who attempts to link observed assertions with user labels. Conditional entropy is the function of usage statistics for all assertions in the system. Personal decisions made by the users about the usage of assertions contribute to these statistics. This collective effect from all the users impacts the unlinkability of authentication and must be studied using game theory. We specify several instances of the game where context information that is provided to the users differs. Through game theory and based on conditional entropy, we demonstrate how each user optimizes usage for the personal set of assertions. In the experiment, we substantiate the advantage of the proposed rational decision-making approaches: Unlinkability that we obtain under Nash equilibrium is higher than in the system where users authenticate using their assertions at random. We finally propose an algorithm that calculates equilibrium and assists users with the selection of assertions. This manifests that described techniques can be executed in realistic settings. This does not require modification of existing authentication protocols and can be implemented in platform-independent identity agents. As a use case, we describe how our technique can be used in Digital Credential Wallets: We suggest that unlinkability of authentication can be improved for Verifiable Credentials.
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Litake, Shilpa R., and Prachi Mukherji. "Integration of Fuzzy Logic and ABC Algorithm for Optimized Network Selection in Heterogeneous Wireless Environment." International Journal of Sensors, Wireless Communications and Control 10, no. 2 (2020): 248–61. http://dx.doi.org/10.2174/2210327909666190401205928.
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Background & Objective: Various Radio Access Technologies are integrated in the next generation of Heterogeneous Wireless Networks. The coexistence of various kinds of wireless access networks ensures high service quality (QoS) for the users. Seamless vertical handover plays a significant role in providing ubiquitous access to users. The ability to select the optimal access network out of available access networks decides the comprehensive performance of the system. A novel scheme: Handoff Urgency Estimator and Target Access Network Selector using Artificial Bee Colony algorithm (HUETANSABC) for deciding the necessity of handover and selection of the best network is proposed in this paper. The objective of the proposed work is to choose the most promising access network out of available coexisting networks for enhancing user experience. Fuzzy logic provides reliable results even when the input parameters are random in nature and can not be defined precisely. Artificial Bee Colony is an effective method for searching and optimization. Proposed system combines best of fuzzy logic and ABC algorithm for timely initiation of vertical handover. To gather the required information for handover, services provided by IEEE 802.21 standard are utilized. Proposed integration of fuzzy logic and the ABC algorithm has resulted in a decreasing number of unnecessary handovers. Methods: The effect of varying context parameters is analyzed using Fuzzy Inference System to estimate the urgency of handover. The optimization of a target access network selection process is achieved using meta-heuristic method. Results: Simulation results on MATLAB indicate that the proposed system performs better than ABC and Multiple Attribute Decision Making (MADM) techniques.
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Bo, Chunxin, Xiaohong Zhang, and Songtao Shao. "Non-Dual Multi-Granulation Neutrosophic Rough Set with Applications." Symmetry 11, no. 7 (2019): 910. http://dx.doi.org/10.3390/sym11070910.
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Multi-attribute decision-making (MADM) is a part of management decision-making and an important branch of the modern decision theory and method. MADM focuses on the decision problem of discrete and finite decision schemes. Uncertain MADM is an extension and development of classical multi-attribute decision making theory. When the attribute value of MADM is shown by neutrosophic number, that is, the attribute value is complex data and needs three values to express, it is called the MADM problem in which the attribute values are neutrosophic numbers. However, in practical MADM problems, to minimize errors in individual decision making, we need to consider the ideas of many people and synthesize their opinions. Therefore, it is of great significance to study the method of attribute information aggregation. In this paper, we proposed a new theory—non-dual multi-granulation neutrosophic rough set (MS)—to aggregate multiple attribute information and solve a multi-attribute group decision-making (MGDM) problem where the attribute values are neutrosophic numbers. First, we defined two kinds of non-dual MS models, intersection-type MS and union-type MS. Additionally, their properties are studied. Then the relationships between MS, non-dual MS, neutrosophic rough set (NRS) based on neutrosophic intersection (union) relationship, and NRS based on neutrosophic transitive closure relation of union relationship are outlined, and a figure is given to show them directly. Finally, the definition of non-dual MS on two universes is given and we use it to solve a MGDM problem with a neutrosophic number as the attribute value.
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HASHEMKHANI ZOLFANI, Sarfaraz, Reza MAKNOON, and Edmundas Kazimieras ZAVADSKAS. "AN INTRODUCTION TO PROSPECTIVE MULTIPLE ATTRIBUTE DECISION MAKING (PMADM)." Technological and Economic Development of Economy 22, no. 2 (2016): 309–26. http://dx.doi.org/10.3846/20294913.2016.1150363.
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In recent years futures science has received a great deal of attention and has gained worldwide credibility in the science community as the science of tomorrows. The countless applications of futures studies in various fields have been a major breakthrough for mankind. Undoubtedly, decision making is one of the most significant aspects of shaping the future and an integral part of any credible future research. Multiple Criteria Decision Making (MCDM) in general and Multiple Attribute Decision Making in particular (MADM), are among the most remarkable subparts of the decision making process. The most recent model developed using the MADM method is the Dynamic MADM. The model does not specifically concentrate on the future actions and approaches and remains to be fully explored. This research presents a new concept and a new approach in the MADM field which is called the Prospective Multiple Attribute Decision Making (PMADM). The PMADM model can very well cover the DMADM concept but instead chooses to focus on future topics. The study also introduces two new approaches. The first research aims to elaborate the basis of this model and then evolves to deal with the future limiters as they potentially pop up and change the course of future actions. The new model based on future limiters is separated and categorized into two sections; one of which is looked upon without the probabilities rate and the other one with the probabilities rate. This approach is deemed priceless due to its major applicability in the ranking of the MADM methods such as: TOPSIS, VIKOR, COPRAS, ARAS, WASPAS and etc. Finally, a case study with the various applications of PMADM model in WASPAS methodology is put forth and illustrated.
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ZHOU, LIHUA, WEIYI LIU, and LIZHEN WANG. "STATIC STRATEGIC GAME APPROACH FOR MULTIPLE ATTRIBUTE DECISION MAKING PROBLEMS WITHOUT WEIGHT INFORMATION." International Journal on Artificial Intelligence Tools 20, no. 03 (2011): 577–88. http://dx.doi.org/10.1142/s0218213011000292.
Full textAbstract:
In multiple attribute decision making (MADM) problems, it is usual that no single alternative works best for all performance attributes, so it is difficult to select the best from among multiple available alternatives, especially in the situation that the attribute weights are completely unknown. This research propose a game theory-based approach (GMADM) which incorporates static strategic game theory into MADM problems to derive the attribute weights, and then utilize the weight arithmetic average (WAA) operator to aggregate the attribute values corresponding to each alternative and rank alternatives by means of aggregated information. In GMADM, each attribute is regarded as a player taking part in the game, and the player's strategy is to select a value from interval [0,1] to assign corresponding attribute weight, and the player's utility is defined as the agreement between the ranking of alternatives determined by the aggregated information and the one determined by the attribute values. When the game is in equilibrium status, the strategy profile is the best attribute weights which make each player have good utilities. Moreover, the equilibrium solution of game and the resolution method for the MADM problem without weight information have also been developed. Finally, the result of proposed approach for a practical MADM problem and its comparisons with one of other methods are given.
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Amanathulla, Sk, G. Muhiuddin, D. Al-Kadi, and M. Pal. "Multiple Attribute Decision-Making Problem Using Picture Fuzzy Graph." Mathematical Problems in Engineering 2021 (October 21, 2021): 1–16. http://dx.doi.org/10.1155/2021/9937828.
Full textAbstract:
In a picture fuzzy environment, almost all multiple attribute decision-making ( MADM ) methods have been discussed a type of problem in which there is no relationship among the attributes. Although the relationship among the attributes should be considered in the actual applications, so we need to pay attention to that important issue. This article applied graph theory to the picture fuzzy set ( PFS ) and obtained a new method, MADM , to solve complicated problems under a picture fuzzy environment. The developed method can capture the relationship among the attributes that cannot be handled well by any existing methods. This study introduces union, intersection, sum, Cartesian product, the composition of picture fuzzy graphs ( PFG s), and their important properties. Finally, by considering the importance of relationships among attributes in the determination process, two algorithms, based on PFG , have developed to solve complicated problems using picture fuzzy information. Also, two numerical examples have introduced to explain how to deal with the MADM problem under picture fuzzy environment.
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LI, DENG-FENG. "RELATIVE RATIO METHOD FOR MULTIPLE ATTRIBUTE DECISION MAKING PROBLEMS." International Journal of Information Technology & Decision Making 08, no. 02 (2009): 289–311. http://dx.doi.org/10.1142/s0219622009003405.
Full textAbstract:
The multiple attribute decision making (MADM) is an important research field in decision science and operations research. Recently, several commonly used methods such as the TOPSIS and the VIKOR were proposed to solve the MADM problems. The TOPSIS and VIKOR are based on aggregating functions representing closeness to the ideal, which originated in the compromise programming method. The aim of this paper is to develop a new methodology called the relative ratio (RR) for the MADM problems. In this RR method, a compromise solution/alternative is determined based on the concept that the chosen alternative should be as close to the ideal solution as possible and as far away from the negative-ideal solution as possible simultaneously. The computation principle and procedure of the RR method are described in detail in this paper. Moreover comparisons of the RR method with the TOPSIS as well as the VIKOR are made theoretically and illustrated with a numerical example.
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Ahsan, Moh, Devi Permatasari, and Retno Marsitin. "TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION UNTUK MEMILIH JURUSAN BAGI SISWA SMA BERDASARKAN KECERDASAN GANDA." Kurawal - Jurnal Teknologi, Informasi dan Industri 1, no. 2 (2018): 107–13. http://dx.doi.org/10.33479/kurawal.2018.1.2.107-113.
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Setiap manusia mempunyai lebih dari satu kecerdasan ganda, seperti logika-matematika, visual-spasial, interpersonal, intrapersonal, linguistik, musikal, naturalis, dan kinestestis (Howard Gardner dari Harvard University). Banyak cara, untuk melihat kecerdasan seseorang diantaranya melalui angka, musik, gambar, kata-kata, kegiatan fisik atau sosial-emosional. Banyak hasil riset mengenai kecerdasan siswa, mengharap kepada orang tua dan wali murid agar selalu memberikan banyak pengaraha, stimulasi dan pengalaman kepada siswa.Pendidikan menjadi point penting untuk orang tua dalam meningkatkan kecerdasan anaknya, dimana dalam lembaga pendidikan anak sebagai peserta didik atau siswa dapat diarahkan kepada bakat, minat dan potensi yang dimilikinya. Jika anak tidak tersentuh oleh dunia pendidikan maka potensi kecerdasan yang dimiliki seorang anak tidak akan berkembang dengan maksimal.Pemilihan jurusan sejak awal bisa mempermudah siswa dalam memilih bidang ilmu atau jurusan yang akan ditekuninya di akademik, sekolah tinggi atau di Universitas kelak yang tentunya akan mengarah kepada masa depan dan karirnya. Sejak diberlakukannya kurikulum 2013 dimana sistem peminatan sudah dimulai dan dilakukan dari kelas Sepuluh atau kelas satu. Hal ini tentunya membutuhkan peran guru Bimbingan Konseling (BK) untuk mengarahkan anak ke bakat, minat dan potensi akademiknya. Pilihan tersebut terdiri dari Ilmu Pengetahuan Alam (IPA), Ilmu Pengetahuan Sosial (IPS) , dan BAHASA (KEMEDIKBUD 2012).Technique for Order Preference by Similiarty to Ideal Solution (TOPSIS) adalah diantara salah satu metode yang didalam MADM (Multiple-Attribute Decesion Making) dan merupakan salah satu pemodelan decision support system (SPK) digunakan untuk pengambilan sebuah keputusan dalam menetapkan kriteria yang terbaik dari beberapa alternatif berdasarkan beberapa kriteria yang telah ditentukan sebelumnya.
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Ahsan, Moh, Devi Permatasari, and Retno Marsitin. "TECHNIQUE FOR ORDER PREFERENCE BY SIMILARITY TO IDEAL SOLUTION UNTUK MEMILIH JURUSAN BAGI SISWA SMA BERDASARKAN KECERDASAN GANDA." Kurawal - Jurnal Teknologi, Informasi dan Industri 1, no. 2 (2018): 107–13. http://dx.doi.org/10.33479/kurawal.v1i2.128.
Full textAbstract:
Setiap manusia mempunyai lebih dari satu kecerdasan ganda, seperti logika-matematika, visual-spasial, interpersonal, intrapersonal, linguistik, musikal, naturalis, dan kinestestis (Howard Gardner dari Harvard University). Banyak cara, untuk melihat kecerdasan seseorang diantaranya melalui angka, musik, gambar, kata-kata, kegiatan fisik atau sosial-emosional. Banyak hasil riset mengenai kecerdasan siswa, mengharap kepada orang tua dan wali murid agar selalu memberikan banyak pengaraha, stimulasi dan pengalaman kepada siswa.Pendidikan menjadi point penting untuk orang tua dalam meningkatkan kecerdasan anaknya, dimana dalam lembaga pendidikan anak sebagai peserta didik atau siswa dapat diarahkan kepada bakat, minat dan potensi yang dimilikinya. Jika anak tidak tersentuh oleh dunia pendidikan maka potensi kecerdasan yang dimiliki seorang anak tidak akan berkembang dengan maksimal.Pemilihan jurusan sejak awal bisa mempermudah siswa dalam memilih bidang ilmu atau jurusan yang akan ditekuninya di akademik, sekolah tinggi atau di Universitas kelak yang tentunya akan mengarah kepada masa depan dan karirnya. Sejak diberlakukannya kurikulum 2013 dimana sistem peminatan sudah dimulai dan dilakukan dari kelas Sepuluh atau kelas satu. Hal ini tentunya membutuhkan peran guru Bimbingan Konseling (BK) untuk mengarahkan anak ke bakat, minat dan potensi akademiknya. Pilihan tersebut terdiri dari Ilmu Pengetahuan Alam (IPA), Ilmu Pengetahuan Sosial (IPS) , dan BAHASA (KEMEDIKBUD 2012).Technique for Order Preference by Similiarty to Ideal Solution (TOPSIS) adalah diantara salah satu metode yang didalam MADM (Multiple-Attribute Decesion Making) dan merupakan salah satu pemodelan decision support system (SPK) digunakan untuk pengambilan sebuah keputusan dalam menetapkan kriteria yang terbaik dari beberapa alternatif berdasarkan beberapa kriteria yang telah ditentukan sebelumnya.
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Robinson, P. John, and P. Wilson Arul Prakash. "Application of Improved Class of Weighted Averaging Operators in Intuitionistic Fuzzy Ann Guided MAGDM E-Commerce Problems." Indian Journal Of Science And Technology 17, no. 29 (2024): 3026–36. http://dx.doi.org/10.17485/ijst/v17i29.1872.
Full textAbstract:
Objectives: To propose a novel and improvised Artificial Neural Network (ANN) technique to solve Multiple Attribute Group Decision Making (MAGDM) problems and an improved class of Aggregation operators for combining Intuitionistic Fuzzy Set (IFS) matrices for the ANN algorithm. Methods: A novel class of improved aggregation operators namely the Improved Intuitionistic Fuzzy Weighted Arithmetic Averaging (IM-IFWAA) operator and the Improved Intuitionistic Fuzzy Ordered Weighted Averaging (IM-IFOWA) operator are proposed in this work. The proposed improved class of aggregation operators will aggregate the IFS matrix data sets appearing in the form of matrices and then the revised input vectors which is then fed into the ANN algorithm following Delta, Perceptron, and Hebb Learning Rule for the next phase. Findings: Aggregating the Intuitionistic Fuzzy set information or data in today’s digital world is a tedious task in the field of MAGDM problem-solving. In this work, two new classes of operators for aggregating the data sets are proposed namely the IM-IFWAA operator and the IM-IFOWA operator which will improve the performance of the ANN. Necessary theorems for the proposed operators are proved to provide consistency of the same. The input created using the aforementioned operators is then fed into the novel ANN algorithm for further computations. Varieties of learning rules are then engaged for ranking of the best alternative for the decision problem. The developed theory is supported by a numerical example which is computed using all the proposed techniques. Novelty: Most of the research done on Intuitionistic Fuzzy Artificial Neural Network models are based on learning rules or using some other calculations. The proposed methods of using novel and improvised aggregation operators for ANN are used to find the inputs for ANN where varieties of learning rules for ANN are employed for effective decision analysis. Keywords: ANN, Learning Rules, MAGDM, Intuitionistic Fuzzy Sets, Weighted Aggregation Operator
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Ding, Tao, Liang Liang, Min Yang, and Huaqing Wu. "Multiple Attribute Decision Making Based on Cross-Evaluation with Uncertain Decision Parameters." Mathematical Problems in Engineering 2016 (2016): 1–10. http://dx.doi.org/10.1155/2016/4313247.
Full textAbstract:
Multiple attribute decision making (MADM) problem is one of the most common and popular research fields in the theory of decision science. A variety of methods have been proposed to deal with such problems. Nevertheless, many of them assumed that attribute weights are determined by different types of additional preference information which will result in subjective decision making. In order to solve such problems, in this paper, we propose a novel MADM approach based on cross-evaluation with uncertain parameters. Specifically, the proposed approach assumes that all attribute weights are uncertain. It can overcome the drawback in prior research that the alternatives’ ranking may be determined by a single attribute with an overestimated weight. In addition, the proposed method can also balance the mean and deviation of each alternative’s cross-evaluation score to guarantee the stability of evaluation. Then, this method is extended to a more generalized situation where the attribute values are also uncertain. Finally, we illustrate the applicability of the proposed method by revisiting two reported studies and by a case study on the selection of community service companies in the city of Hefei in China.