Relevant bibliographies by topics / Fuzzy Multi-Attribute Decision Making

Academic literature on the topic 'Fuzzy Multi-Attribute Decision Making'

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Published: 4 June 2021
Last updated: 3 February 2022

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Journal articles on the topic "Fuzzy Multi-Attribute Decision Making"

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Xu, Zeshui, and Ronald R. Yager. "Dynamic intuitionistic fuzzy multi-attribute decision making." International Journal of Approximate Reasoning 48, no. 1 (April 2008): 246–62. http://dx.doi.org/10.1016/j.ijar.2007.08.008.

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Gong, Zengtai, and Junhu Wang. "Hesitant fuzzy graphs, hesitant fuzzy hypergraphs and fuzzy graph decisions1." Journal of Intelligent & Fuzzy Systems 40, no. 1 (January 4, 2021): 865–75. http://dx.doi.org/10.3233/jifs-201016.

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Up to now, there have been a lot of research results about multi-attribute decision making problems by fuzzy graph theory. However, there are few investigations about multi-attribute decision making problems under the background of indecisiveness. The main reason is that the difference of cognition and the complexity of thinking by decision makers, for the same question have different opinions. In this paper, we proposed a hesitant fuzzy hypergraph model based on hesitant fuzzy sets and fuzzy hypergraphs. At the same time, some basic graph operations of hesitant fuzzy hypergraphs are investigated and several equivalence relationship between hesitant fuzzy hypergraphs, hesitant fuzzy formal concept analysis and hesitant fuzzy information systems are discussed. Since granular computing can deal with multi-attribute decision-making problems well, we considered the hesitant fuzzy hypergraph model of granular computing, and established an algorithm of multi-attribute decision-making problem based on hesitant fuzzy hypergraph model. Finally an example is given to illustrate the effectiveness of the algorithm.
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Gunaratne, M., J. L. Chameaut, and A. G. Altschaefflf. "Fuzzy multi-attribute decision making in pavement management." Civil Engineering Systems 2, no. 3 (September 1985): 166–70. http://dx.doi.org/10.1080/02630258508970400.

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Li, Guangxu, Gang Kou, Changsheng Lin, Liang Xu, and Yi Liao. "Multi-attribute decision making with generalized fuzzy numbers." Journal of the Operational Research Society 66, no. 11 (November 2015): 1793–803. http://dx.doi.org/10.1057/jors.2015.1.

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Gui, Mei, Xiaoyang Ding, and Cheng Yi Zhang. "Multi-Attribute Decision Making Method Based on Intuitionistic Fuzzy Grey Sets." Advanced Materials Research 557-559 (July 2012): 2070–74. http://dx.doi.org/10.4028/www.scientific.net/amr.557-559.2070.

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In this paper, it discussed the fuzzy multi-attribute decision making problems that are the attribute weights are known and the attribute values are intuitionistic fuzzy grey sets. Intuitionistic fuzzy grey set is given, and the positive and negative ideal points are introduced, then the definition of Hamming distance formula between the intuitionistic fuzzy grey numbers is discussed. Moreover, it puts forward multi-attribute decision making method based on intuitionistic fuzzy grey set, and discusses the steps of the method. Then, the examples are given to show that the method is reasonable and effective.
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Li, D. F. "A fuzzy closeness approach to fuzzy multi-attribute decision making." Fuzzy Optimization and Decision Making 6, no. 3 (August 23, 2007): 237–54. http://dx.doi.org/10.1007/s10700-007-9010-1.

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Sun, Peng, Jun Zhang, and Hongbin Zeng. "A Hesitant Fuzzy Multi-Attribute Group Decision Making Method Based on Prospect Theory." Xibei Gongye Daxue Xuebao/Journal of Northwestern Polytechnical University 36, no. 4 (August 2018): 735–41. http://dx.doi.org/10.1051/jnwpu/20183640735.

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To solve a multi-attribute group decision-making problem, since its attribute weight is completely unknown, based on the prospect theory, a hesitant fuzzy multi-attribute group decision making method is proposed. According to the single-attribute-preference function provided by a decision maker, an improved entropy weight method is proposed to calculate the attribute weight. The hesitant fuzzy decision-making matrix is transformed into the prospect decision-making matrix which utilizes positive and negative ideal solutions as reference points. Then alternative schemes are ranked according to the ratio of profit to loss. Finally, a numerical example is provided to verify the effectiveness and feasibility of the proposed method.
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Chuu *, S. J. "Fuzzy multi-attribute decision-making for evaluating manufacturing flexibility." Production Planning & Control 16, no. 3 (April 2005): 323–35. http://dx.doi.org/10.1080/09537280500063236.

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Gu, Xiangbai, and Qunxiong Zhu. "Fuzzy multi-attribute decision-making method based on eigenvector of fuzzy attribute evaluation space." Decision Support Systems 41, no. 2 (January 2006): 400–410. http://dx.doi.org/10.1016/j.dss.2004.08.001.

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SU, ZHI-XIN. "A HYBRID FUZZY APPROACH TO FUZZY MULTI-ATTRIBUTE GROUP DECISION-MAKING." International Journal of Information Technology & Decision Making 10, no. 04 (July 2011): 695–711. http://dx.doi.org/10.1142/s021962201100452x.

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The paper investigates fuzzy multi-attribute group decision-making (FMAGDM) problems. The important weights of the attributes and the ratings of the alternatives with respect to each attribute provided by multiple decision-makers are described by the linguistic variables expressed in triangular fuzzy numbers or trapezoidal fuzzy numbers. A hybrid fuzzy approach is proposed, which assesses each alternative in terms of distance measure calculated by a modified VIKOR method as well as similarity measure calculated by a modified gray relational analysis (GRA) method, to the positive ideal alternative and the negative ideal alternative. A new relative closeness coefficient is established to rank alternatives by aggregating the distance and the similarity measures. Two numerical examples for reverse logistics applications are presented to illustrate the proposed method.
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Dissertations / Theses on the topic "Fuzzy Multi-Attribute Decision Making"

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Kaleem, Faisal. "VHITS: Vertical Handoff Initiation and Target Selection in a Heterogeneous Wireless Network." FIU Digital Commons, 2012. http://digitalcommons.fiu.edu/etd/602.

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Global connectivity, for anyone, at anyplace, at anytime, to provide high-speed, high-quality, and reliable communication channels for mobile devices, is now becoming a reality. The credit mainly goes to the recent technological advances in wireless communications comprised of a wide range of technologies, services, and applications to fulfill the particular needs of end-users in different deployment scenarios (Wi-Fi, WiMAX, and 3G/4G cellular systems). In such a heterogeneous wireless environment, one of the key ingredients to provide efficient ubiquitous computing with guaranteed quality and continuity of service is the design of intelligent handoff algorithms. Traditional single-metric handoff decision algorithms, such as Received Signal Strength (RSS) based, are not efficient and intelligent enough to minimize the number of unnecessary handoffs, decision delays, and call-dropping and/or blocking probabilities. This research presented a novel approach for the design and implementation of a multi-criteria vertical handoff algorithm for heterogeneous wireless networks. Several parallel Fuzzy Logic Controllers were utilized in combination with different types of ranking algorithms and metric weighting schemes to implement two major modules: the first module estimated the necessity of handoff, and the other module was developed to select the best network as the target of handoff. Simulations based on different traffic classes, utilizing various types of wireless networks were carried out by implementing a wireless test-bed inspired by the concept of Rudimentary Network Emulator (RUNE). Simulation results indicated that the proposed scheme provided better performance in terms of minimizing the unnecessary handoffs, call dropping, and call blocking and handoff blocking probabilities. When subjected to Conversational traffic and compared against the RSS-based reference algorithm, the proposed scheme, utilizing the FTOPSIS ranking algorithm, was able to reduce the average outage probability of MSs moving with high speeds by 17%, new call blocking probability by 22%, the handoff blocking probability by 16%, and the average handoff rate by 40%. The significant reduction in the resulted handoff rate provides MS with efficient power consumption, and more available battery life. These percentages indicated a higher probability of guaranteed session continuity and quality of the currently utilized service, resulting in higher user satisfaction levels.
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Naim, Nur Syibrah Muhamad. "A type-2 fuzzy logic approach for multi-criteria group decision making." Thesis, University of Essex, 2014. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.635990.

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Multi-Criteria Group Decision Making (MCGDM) is a decision tool which is able to find a unique agreement from a group of decision makers (DMs) by evaluating various conflicting criteria. However, the current multi-criteria decision making with a group of DMs (MCGDM) techniques do not effectively deal with the large number of possibilities that cause disagreement between different judgements and the variety of ideas and opinions among the decision makers which lead to high_uncertainty levels. There is a growing interest to investigate techniques to handle the faced uncertainties in many decision making applications. Studies in fuzzy decision making have grown rapidly in the utilisation of extended fuzzy set theories (i.e., Type-2 Fuzzy Sets, Intuitionistic Fuzzy Sets, Hesitant Fuzzy Sets, Vague Sets, Interval-valued Fuzzy Sets; etc.) to evaluate the faced uncertainties.
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Ku, Khalif Ku Muhammad Naim. "Generalised hybrid fuzzy multi criteria decision making based on intuitive multiple centroid defuzzification." Thesis, University of Portsmouth, 2016. https://researchportal.port.ac.uk/portal/en/theses/generalised-hybrid-fuzzy-multi-criteria-decision-making-based-on-intuitive-multiple-centroid-defuzzification(84549646-118e-45d7-9868-29019128b482).html.

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The concept of fuzzy multi criteria decision making process has received significant attention from research community due to its successful applications for human based decision making problems under fuzzy environment. It complements the decision makers to evaluate their subjective judgements under situations that are vague, imprecise, random and uncertain in nature. Inspired by such real applications, in this research study, the theoretical foundation of a hybrid fuzzy multi criteria decision making model based on new centroid defuzzification method is proposed. The proposed model tackles some issues that may be associated with the selection problems of the multi criteria decision making such as deriving decision criteria important weights, ranking various alternatives, suitable combination of fuzzy multi criteria decision making techniques and proper defuzzification method used. In developing the hybrid model, two multi criteria decision making techniques are integrated which are; 1) consistent fuzzy preference relations and; 2) fuzzy technique for order of preference by similarity to ideal solution. It is also incorporated together with new defuzzification method namely intuitive multiple centroid. In the view of evidence outlined in this study, the proposed model serves as a generic multi criteria decision making procedure, particularly when fuzzy sets are involved in the decision process. The two major contributions from this study are that: 1) The intuitive multiple centroid defuzzification capable to cater all possible representations of fuzzy sets reasonably and consistent with human intuition or judgment. 2) The generalised hybrid fuzzy multiple decision making model using intuitive multiple centroid gives better computation to evaluate criteria and alternatives in decision making problems under different uncertain environment. Furthermore, an empirical validation of the proposed model is investigated through conducting a case study of staff recruitment in MESSRS SAPRUDIN, IDRIS & CO, Malaysia. In this case study, a group of three decision makers, and four finalist of candidates are selected to take part of this case study. Their involvement achieved the first objective of the case study. At the end of the case study, a sensitivity analysis is conducted to indicate the robustness and the consistency of the results obtained. It is concluded that the proposed model is indeed beneficial under different environment.
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Soo, Houng Y. "Towards the Development of a Decision Support System for Emergency Vehicle Preemption and Transit Signal Priority Investment Planning." Diss., Virginia Tech, 2004. http://hdl.handle.net/10919/27204.

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Advances in microprocessor and communications technologies are making it possible to deploy advanced traffic signal controllers capable of integrating emergency vehicle preemption and transit priority operations. However, investment planning for such an integrated system is not a trivial task. Investment planning for such a system requires a holistic approach that considers institutional, technical and financial issues from a systems perspective. Two distinct service providers, fire and rescue providers and transit operators, with separate operational functions, objectives, resources and constituents are involved. Performance parameters for the integrated system are not well defined and performance data are often imprecise in nature. Transportation planners and managers interested in deploying integrated emergency vehicle preemption and traffic priority systems do not have an evaluation approach or a common set of performance metrics to make an informed decision. There is a need for a simple structured analytical approach and tools to assess the impacts of an integrated emergency vehicle preemption and transit priority system as part of investment decision making processes. This need could be met with the assistance of a decision support system (DSS) developed to provide planners and managers a simple and intuitive analytical approach to assist in making investment decisions regarding emergency vehicle preemption and transit signal priority. This dissertation has two research goals: (1) to develop a decision support system framework to assess the impacts of advanced traffic signal control systems capable of integrating emergency vehicle preemption and transit signal priority operations for investment planning purposes; and (2) to develop selected analytical tools for incorporation into the decision support system framework. These analytical tools will employ fuzzy sets theory concepts, as well as cost and accident reduction factors. As part of this research, analytical tools to assess impacts on operating cost for transit and fire and rescue providers have been developed. In addition, an analytical tool was developed and employs fuzzy multi-attribute decision making methods to rank alternative transit priority strategies. These analytical tools are proposed for incorporation into the design of a decision support system in the future.
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Yaakob, Abdul Malek Bin. "Multi criteria decision making methodology for fuzzy rule based systems and networks using TOPSIS." Thesis, University of Portsmouth, 2017. https://researchportal.port.ac.uk/portal/en/theses/multi-criteria-decision-making-methodology-for-fuzzy-rule-based-systems-and-networks-using-topsis(8efb475a-4f98-4d5c-846d-f01b01328dda).html.

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Fuzzy systems and networks are vital within the armoury of fuzzy tools and applicable to real life decision making environments. Three types of fuzzy systems introduced in literatures which are systems with single rule base, systems with multiple rule bases and system with networked rule bases. This research introduces novel extension of the Technique of Ordering of Preference by Similarity to Ideal Solution (TOPSIS) methods and uses fuzzy systems and networks to solve multi-criteria decision making problems where both benefit and cost are presented as subsystems. In conjunction, the implementation of fuzzy sets type-1, type-2 and Z-number of proposed approaches is also presented. Furthermore, literatures have observed that tracking the performance of criteria is crucial by controlling the estimation of uncertainty of the criteria. Thus, the decision maker evaluates the performance of each alternative and further observes the performance for both benefit and cost criteria. This research improves significantly the transparency of the TOPSIS methods while ensuring higher effectiveness in comparison to established approaches. Ensuring the practicality and the effectiveness of proposed methods in a realistic scenario, the problem of ranking traded stock is studied. This case study is conducted based on stocks traded in a developing financial market such as Kuala Lumpur Stock Exchange. The ranking based on proposed methods is validated comparatively using performance indicators such as Spearman Rho correlation, Kendall Tau correlation, Root Mean Square Errror and Average Absolute Distance by assuming ranking based on return on investment as a benchmarking. Based on the case study, the proposed methods outperform the established TOPSIS methods in term of average rank position.
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Madi, Elissa Nadia. "An improved uncertainty in multi-criteria decision making model based on type-2 fuzzy TOPSIS." Thesis, University of Nottingham, 2018. http://eprints.nottingham.ac.uk/55394/.

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This thesis presents a detailed study about one of the Multiple Criteria Decision Making (MCDM) models, namely Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), based on fuzzy set theory (FST) by focusing on improving modelling uncertain information provided by a group of decision makers (DMs). An exploration of issues and limitations in current models of standard TOPSIS and fuzzy TOPSIS were made. Despite many variations of type-1 fuzzy TOPSIS (T1-TOPSIS) model, none of the studies explaining the details of the key stages of standard TOPSIS (non-fuzzy) and T1-TOPSIS are based on a step-wise procedure. A detailed study was conducted which involve the process of identifying the limitations of standard TOPSIS and T1-TOPSIS. Based on this, a novel contribution on the comparison between these two models in systematic stepwise procedure was given. This study successfully identified and discussed the limitations, issues and challenges which have not been investigated sufficiently in the context of T1-TOPSIS model. Based on this exploration, further investigation of multiple variants of the extension of the fuzzy TOPSIS model for solving the MCDM problem was made with the primary aim of detailing the steps involved. One challenge that has risen is that it is not straightforward to differentiate between the multiple variants of TOPSIS existing today. A systematic comparison was made between standard T1-TOPSIS model with the recently extended model to show the differences between both models and to provide context for their respective strengths and limitations both in the complexity of application and expressiveness of results. Based on the resulting comparison, the differences in the steps implemented by these two Fuzzy TOPSIS models were highlighted throughout the worked example. Furthermore, this task highlights the ability of both models to handle different levels of uncertainty. Following the exploration of issues and limitations of the current model, as well as a comparative study, a novel extension of type-2 fuzzy TOPSIS model is proposed in this thesis which suggests providing an interval-valued output to reflect the uncertainties and to model subjective information. The proposed model enables to uniquely captures input uncertainty (i.e., decision-makers' preferences) in the decision-making outputs and provide a direct mapping of uncertainty in the inputs to outputs. By keeping the output values in interval form, the proposed model reduces the loss of information and maximises the potential benefit of using Interval Type-2 Fuzzy Sets (IT2 FSs). To demonstrate the MCDM problems when a various level of uncertainty is introduced, a novel experimental method was proposed in this study. The primary aim is to explore the use of IT2 FSs in handling uncertainty based on the TOPSIS model. This experiment was conducted to show how the variation of uncertainty levels in the input affects the final outputs. An implementation of the proposed model to two different case studies was conducted to evaluate the proposed model. The proposed model for the first time generates an interval-valued output. As intervals can, for example, exhibit partial overlap, a novel extended measure is proposed to compare the resulting interval-valued output from various cases (i.e., overlapping and non-overlapping) of the interval with considering uncertainty.
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Tahvili, Sahar. "Solving complex maintenance planning optimization problems using stochastic simulation and multi-criteria fuzzy decision making." Thesis, SICS, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:ri:diva-24416.

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Dukyil, Abdulsalam Saleh. "Artificial intelligence and multiple criteria decision making approach for a cost-effective RFID-enabled tracking management system." Thesis, Brunel University, 2018. http://bura.brunel.ac.uk/handle/2438/17128.

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The implementation of RFID technology has been subject to ever-increasing popularity in relation to the traceability of items as one of the most advance technologies. Implementing such a technology leads to an increase in the visibility management of products. Notwithstanding this, RFID communication performance is potentially greatly affected by interference between the RFID devices. It is also subject to auxiliary costs in investment that should be considered. Hence, seeking a cost-effective design with a desired communication performance for RFID-enabled systems has become a key factor in order to be competitive in today‟s markets. This study introduce a cost and performance-effective design for a proposed RFID-enabled passport tracking system through the development of a multi-objective model that takes in account economic, operation and social criteria. The developed model is aimed at solving the design problem by (i) allocating the optimal numbers of related facilities that should be established and (ii) obtaining trade-offs among three objectives: minimising implementation and operational costs; minimising RFID reader interference; and maximising the social impact measured in the number of created jobs. To come closer to the actual design in terms of considering the uncertain parameters, a fuzzy multi-objective model was developed. To solve the multi-objective optimization problem model, two solution methods were used respectively (epsilon constrain and linear programming) to select the best Pareto solution and a decision-making method was developed to select the final trade-off solution. Moreover, this research aims to provide a user-friendly decision making tool for selecting the best vendor from a group which submitted their tenders for implementing a proposed RFID- based passport tracking system. In addition to that a real case study was applied to examine the applicability of the developed model and the proposed solution methods. The research findings indicate that the developed model is capable of presenting a design for an RFID- enabled passport tracking system. Also, the developed decision-making tool can easily be used to solve similar vendor selection problem. Research findings demonstrate that the proposed RFID-enabled monitoring system for the passport tracking system is economically feasible. The study concludes that the developed mathematical models and optimization approaches can be a useful decision-maker for tackling a number of design and optimization problems for RFID system using artificial intelligence mathematical algorithm based techniques.
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Hu, Yi-Chung, and 胡宜中. "Multi-Attribute Decision Making Using Fuzzy Knowledge Discovery." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/94188804622234490269.

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博士
國立交通大學
資訊管理所
91
Most organizations have large databases that contain a wealth of potentially accessible information. Through data mining techniques, many interesting patterns or useful rules hidden in data will be discovered. On the other hand, soft computing techniques have expanded enormously over the past few years. Fuzzy sets are one critical component of soft computing, and are further used to generate fuzzy knowledge representations in this dissertation. The reason is that we consider that fuzzy knowledge representations described by the natural language are well suited for the subject thinking of human subjects and will help to increase the flexibility for users in making decisions. Additionally, the comprehensibility of fuzzy representation by human users is also a criterion in designing a fuzzy system. The simple fuzzy partition methods are thus preferable. The main aim of this dissertation is to develop novel fuzzy data mining techniques to find comprehensible and potentially useful fuzzy knowledge based on the simple fuzzy partition method; then those fuzzy knowledge, including fuzzy association rules, fuzzy sequential patterns and frequent patterns, are further applied to solve various multi-attribute decision problems by using soft computing tools. The feasibility of using fuzzy association rules in multi-attribute classification problems is specially explored. Subsequently, novel methods are further proposed by soft computing techniques to cope with two significant multi-attribute decision problems that include competence set expansion and assessment of weights of product attributes in individual purchase behaviors. Since some compound skills can be added to the needed competence set for helping to acquire all single skills, potentially useful compound skills are extracted from single skills. For classification problems, we employ genetic algorithms to automatically find fuzzy if-then rules from training patterns. In addition, the acquisition of a compact fuzzy rule set with high classification accuracy rate is taken into account in the fitness function. For classification generalization ability, the simulation results from the iris data and the appendicitis data demonstrate that proposed learning algorithm performs well in comparison with other fuzzy or non-fuzzy classification methods. For competence set expansion, two issues with possible solutions are discussed. First, the fuzzy knowledge can be treated as a needed competence set that should be acquired by decision makers; then, that needed competence set with minimum learning cost is expanded by the minimum spanning table method proposed by Feng and Yu (1998). Next, since it seems that it is not easy to measure learning costs by time or money, the other method is to obtain learning costs between any two single skills by using the grey relational grade. The learning cost from one compound skill to another single skill is further obtained by using a trained multi-layer neural network. As for the assessment of individual weights of product attributes, the focus is to assess weights or degrees of consumers’ attentiveness of product attributes for various frequent purchase behaviors. By using frequent purchase behaviors discovered from transaction databases, and evaluations of product attributes through questionnaire, each product can be transformed into a piece of input training data for a single-layer perceptron (SLP). After training SLP, the weights of products’ attributes in each frequent purchase behavior can be found from connection weights of SLP. Through numerical examples or simulation results, we illuminate that individual proposed methods can effectively use fuzzy knowledge to provide useful information to support multiple attributes decision making. Additionally, a new clustering technique, named the grey self-organizing feature maps (GSOFM), is proposed by incorporating the grey relations into the well-known self-organizing feature maps. From the simulation results, we can see that the best result of the GSOFM applied for analysis of the iris data outperforms those of other known unsupervised neural network models. Furthermore, the GSOFM can effectively solve the traveling salesman problems.
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張弘紋. "The Construction of Fuzzy Multi-Attribute Group Decision-Making Method." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/51267572187654160991.

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Books on the topic "Fuzzy Multi-Attribute Decision Making"

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Chen, Shu-Jen, and Ching-Lai Hwang. Fuzzy Multiple Attribute Decision Making. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-46768-4.

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Hao, Zhinan, Zeshui Xu, and Hua Zhao. Several Intuitionistic Fuzzy Multi-Attribute Decision Making Methods and Their Applications. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-3891-9.

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Kahraman, Cengiz, ed. Fuzzy Multi-Criteria Decision Making. Boston, MA: Springer US, 2008. http://dx.doi.org/10.1007/978-0-387-76813-7.

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1929-, Hwang C. L., and Hwang Frank P, eds. Fuzzy multiple attribute decision making: Methods and applications. Berlin: Springer-Verlag, 1992.

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Jih-Jeng, Huang, ed. Multiple attribute decision making: Methods and appliations. Boca Raton, FL: CRC Press, 2011.

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Lee, E. Stanley, and Hsu-shih Shih. Fuzzy and Multi-Level Decision Making. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0683-8.

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Julien, Benoît. A fuzzy interactive screening model for multiple attribute decision-making. Ottawa: National Library of Canada, 1990.

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Kahraman, Cengiz, and İrem Otay, eds. Fuzzy Multi-criteria Decision-Making Using Neutrosophic Sets. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00045-5.

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Rao, R. Venkata. Decision Making in Manufacturing Environment Using Graph Theory and Fuzzy Multiple Attribute Decision Making Methods. London: Springer London, 2013. http://dx.doi.org/10.1007/978-1-4471-4375-8.

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Cheng, Chi-Bin, Hsu-Shih Shih, and E. Stanley Lee. Fuzzy and Multi-Level Decision Making: Soft Computing Approaches. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-92525-7.

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Book chapters on the topic "Fuzzy Multi-Attribute Decision Making"

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Xu, Zeshui, and Xiaoqiang Cai. "Dynamic Intuitionistic Fuzzy Multi-Attribute Decision Making." In Intuitionistic Fuzzy Information Aggregation, 259–83. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3_6.

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Basiura, Beata, Jerzy Duda, Bartłomiej Gaweł, Janusz Opiła, Tomasz Pełech-Pilichowski, Bogdan Rębiasz, and Iwona Skalna. "Multi-attribute Decision Making Process and Its Application." In Advances in Fuzzy Decision Making, 55–72. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26494-3_4.

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Xu, Zeshui. "Interactive Intuitionistic Fuzzy Multi-Attribute Decision Making." In Intuitionistic Preference Modeling and Interactive Decision Making, 195–223. Berlin, Heidelberg: Springer Berlin Heidelberg, 2013. http://dx.doi.org/10.1007/978-3-642-28403-8_2.

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Lee, E. Stanley, and Hsu-shih Shih. "Fuzzy Decision Making." In Fuzzy and Multi-Level Decision Making, 97–116. London: Springer London, 2001. http://dx.doi.org/10.1007/978-1-4471-0683-8_5.

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Ohlan, Anshu, and Ramphul Ohlan. "Intuitionistic Fuzzy Exponential Divergence and Multi-attribute Decision-Making." In Generalizations of Fuzzy Information Measures, 123–42. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-45928-8_7.

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Hauke, Wolfgang. "Fuzzy Multiple Attribute Decision Making (Fuzzy-MADM)." In Fuzzy-Modelle in der Unternehmensplanung, 115–34. Heidelberg: Physica-Verlag HD, 1998. http://dx.doi.org/10.1007/978-3-642-58992-8_7.

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Xu, Zeshui, and Xiaoqiang Cai. "Projection Model-Based Approaches to Intuitionistic Fuzzy Multi-Attribute Decision Making." In Intuitionistic Fuzzy Information Aggregation, 249–58. Berlin, Heidelberg: Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-29584-3_5.

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Chen, Shu-Jen, and Ching-Lai Hwang. "Fuzzy Multiple Attribute Decision Making Methods." In Lecture Notes in Economics and Mathematical Systems, 289–486. Berlin, Heidelberg: Springer Berlin Heidelberg, 1992. http://dx.doi.org/10.1007/978-3-642-46768-4_5.

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Zhang, Guangquan, Jie Lu, and Ya Gao. "Fuzzy Bi-level Decision Making." In Multi-Level Decision Making, 175–205. Berlin, Heidelberg: Springer Berlin Heidelberg, 2015. http://dx.doi.org/10.1007/978-3-662-46059-7_7.

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Thakkar, Jitesh J. "Fuzzy Integral and Grey Relation." In Multi-Criteria Decision Making, 161–89. Singapore: Springer Singapore, 2021. http://dx.doi.org/10.1007/978-981-33-4745-8_10.

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Conference papers on the topic "Fuzzy Multi-Attribute Decision Making"

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Wang, Min. "Fuzzy Random Multi-attribute Decision Making Method." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.123.

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Shivakumar, Uppala, Vadlamani Ravi, and G. R. Gangadharan. "Ranking cloud services using fuzzy multi-attribute decision making." In 2013 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2013. http://dx.doi.org/10.1109/fuzz-ieee.2013.6622319.

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Nagata, Kiyoshi, Michio Amagasa, and Hiroo Hirose. "Multi-attribute decision making based on fuzzy outranking." In 2012 IEEE 13th International Symposium on Computational Intelligence and Informatics (CINTI). IEEE, 2012. http://dx.doi.org/10.1109/cinti.2012.6496754.

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Wang, Min. "Fuzzy Multi-attribute Decision Making Under Interval Number." In Fourth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD 2007). IEEE, 2007. http://dx.doi.org/10.1109/fskd.2007.331.

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Wang, Cheng, Huanbin Liu, and Ping Li. "Novel Method of Hybrid Multi-Attribute Decision Making." In 2008 Fifth International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2008. http://dx.doi.org/10.1109/fskd.2008.358.

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Qiu, Yuming, Ping Ge, and Joseph F. Junker. "Varying Weights in Multi-Attribute Decision Making." In ASME 2008 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2008. http://dx.doi.org/10.1115/detc2008-49350.

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Extreme cases that contain either extremely high or pretty low preference attribute(s) are investigated for multi-attribute decision making problems. Normal cases occur most of the time, and many existing methods have been developed to support the decision making in such scenarios. Extreme cases are possible in real applications, and they are usually present intriguing scenarios because of the potential fuzzy and varying decision criteria. To capture this phenomenon, varying weights are introduced to simulate the change pattern concerning relative importance of attributes, and a uniform framework has been developed to support the decision making mathematically for extreme cases. A real application from Industrial Assessment Center at Oregon State University is used to demonstrate the proposed method, and the result shows its capability of capturing a decision maker’s flexible decision altitudes, and indicates its advantage over existing constant weight methods.
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Lawry, Jonathan, and Hongmei He. "Linguistic Attribute Hierarchies for Multiple-Attribute Decision Making." In 2007 IEEE International Fuzzy Systems Conference. IEEE, 2007. http://dx.doi.org/10.1109/fuzzy.2007.4295602.

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Wang, Weize, and Xinwang Liu. "Multi-attribute decision making models under interval type-2 fuzzy environment." In 2011 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE). IEEE, 2011. http://dx.doi.org/10.1109/fuzzy.2011.6007366.

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Tarmudi, Zamali, and Mohd Lazim Abdullah. "Linguistic aggregation method for fuzzy multi-attribute decision-making." In 2013 International Conference on Fuzzy Theory and Its Applications (iFUZZY). IEEE, 2013. http://dx.doi.org/10.1109/ifuzzy.2013.6825460.

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Li, Junhong, Jian Li, Chao You, and Mingjuan Dong. "Multi-attribute decision making method with intuitionistic fuzzy sets." In 2012 9th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD). IEEE, 2012. http://dx.doi.org/10.1109/fskd.2012.6234072.

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Reports on the topic "Fuzzy Multi-Attribute Decision Making"

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Karam, Sofia, Morteza Nagahi, Vidanelage Dayarathna, Junfeng Ma, Raed Jaradat, and Michael Hamilton. Integrating systems thinking skills with multi-criteria decision-making technology to recruit employee candidates. Engineer Research and Development Center (U.S.), June 2021. http://dx.doi.org/10.21079/11681/41026.

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The emergence of modern complex systems is often exacerbated by a proliferation of information and complication of technologies. Because current complex systems challenges can limit an organization's ability to efficiently handle socio-technical systems, it is essential to provide methods and techniques that count on individuals' systems skills. When selecting future employees, companies must constantly refresh their recruitment methods in order to find capable candidates with the required level of systemic skills who are better fit for their organization's requirements and objectives. The purpose of this study is to use systems thinking skills as a supplemental selection tool when recruiting prospective employees. To the best of our knowledge, there is no prior research that studied the use of systems thinking skills for recruiting purposes. The proposed framework offers an established tool to HRM professionals for assessing and screening of prospective employees of an organization based on their level of systems thinking skills while controlling uncertainties of complex decision-making environment with the fuzzy linguistic approach. This framework works as an expert system to find the most appropriate candidate for the organization to enhance the human capital for the organization.
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Sperry, Richard. Multi-Perspective Technology Assessment to Improve Decision Making: A Novel Approach Using Fuzzy Cognitive Mapping for a Large-Scale Transmission Line Upgrade. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.1821.

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Fowke, Robert. Performance Measures for Managerial Decision Making: Performance Measurement Synergies in Multi-Attribute Performance Measurement Systems. Portland State University Library, January 2000. http://dx.doi.org/10.15760/etd.164.

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