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Musikasuwan, Salang. "Novel fuzzy techniques for modelling human decision making." Thesis, University of Nottingham, 2013. http://eprints.nottingham.ac.uk/13161/.
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Standard (type-1) fuzzy sets were introduced to resemble human reasoning in its use of approximate information and uncertainty to generate decisions. Since knowledge can be expressed in a more natural by using fuzzy sets, many decision problems can be greatly simplified. However, standard type-1 fuzzy sets have limitations when it comes to modelling human decision making. In many applications involving the modelling of human decision making (expert systems) the more traditional membership functions do not provide a wide enough choice for the system developer. They are therefore missing an opportunity to produce simpler or better systems. The use of complex non-convex membership functions in the context of human decision making systems were investigated. It was demonstrated that non-convex membership functions are plausible, reasonable membership functions in the sense originally intended by Zadeh. All humans, including ‘experts’, exhibit variation in their decision making. To date, it has been an implicit assumption that expert systems, including fuzzy expert systems, should not exhibit such variation. Type-2 fuzzy sets feature membership functions that are themselves fuzzy sets. While type-2 fuzzy sets capture uncertainty by introducing a range of membership values associated with each value of the base variable, but they do not capture the notion of variability. To overcome this limitation of type-2 fuzzy sets, Garibaldi previously proposed the term ‘non-deterministic fuzzy reasoning’ in which variability is introduced into the membership functions of a fuzzy system through the use of random alterations to the parameters. In this thesis, this notion is extended and formalised through the introduction of a notion termed a non-stationary fuzzy set. The concept of random perturbations that can be used for generating these non-stationary fuzzy sets is proposed. The footprint of variation (FOV) is introduced to describe the area covering the range from the minimum to the maximum fuzzy sets which comprise the non-stationary fuzzy sets (this is similar to the footprint of uncertainty of type-2 sets). Basic operators, i.e. union, intersection and complement, for non-stationary fuzzy sets are also proposed. Proofs of properties of non-stationary fuzzy sets to satisfy the set theoretic laws are also given in this thesis. It can be observed that, firstly, a non-stationary fuzzy set is a collection of type-1 fuzzy sets in which there is an explicit, defined, relationship between the fuzzy sets. Specifically, each of the instantiations (individual type-1 sets) is derived by a perturbation of (making a small change to) a single underlying membership function. Secondly, a non-stationary fuzzy set does not have secondary membership functions, and secondary membership grades. Hence, there is no ‘direct’ equivalent to the embedded type-2 sets of a type-2 fuzzy sets. Lastly, the non-stationary inference process is quite different from type-2 inference, in that non-stationary inference is just a repeated type-1 inference. Several case studies have been carried out in this research. Experiments have been carried out to investigate the use of non-stationary fuzzy sets, and the relationship between non-stationary and interval type-2 fuzzy sets. The results from these experiments are compared with results produced by type-2 fuzzy systems. As an aside, experiments were carried out to investigate the effect of the number of tunable parameters on performance in type-1 and type-2 fuzzy systems. It was demonstrated that more tunable parameters can improve the performance of a non-singleton type-1 fuzzy system to be as good as or better than the equivalent type-2 fuzzy system. Taken as a whole, the techniques presented in this thesis represent a valuable addition to the tools available to a model designer for constructing fuzzy models of human decision making.
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Chuang, Poon-Hwei. "Fuzzy mathematical programming in civil engineering systems." Thesis, Imperial College London, 1985. http://hdl.handle.net/10044/1/7802.
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Foroughi, Behzad. "Decision-making in manufacturing systems, an IPA/fuzzy approach." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ38629.pdf.
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Siddique, Muhammad. "Fuzzy decision making using max-min and MMR methods." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2009. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-3042.
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Fuzzy logic is based on the theory of fuzzy sets, where an object’s membership of a set is gradual rather than just member or not a member. Fuzzy logic uses the whole interval of real numbers between zero (False) and one (True) to develop a logic as a basis for rules of inference. Particularly the fuzzified version of the modus ponens rule of inference enables computers to make decisions using fuzzy reasoning rather than exact. We study decision making problem under uncertainty. we analyze Max-Min method and Minimization of regret method originally developed by Savage and further developed by Yager. We generalize The MMR method by creating the parameterized family of minimum regret methods by using the ordered weighted averaging OWA operators.
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Jaffal, Hussein, and Cheng Tao. "Multiple Attributes Group Decision Making by Type-2 Fuzzy Sets and Systems." Thesis, Blekinge Tekniska Högskola, Sektionen för ingenjörsvetenskap, 2011. http://urn.kb.se/resolve?urn=urn:nbn:se:bth-2659.
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We are living in a world full of uncertainty and ambiguity. We usually ask ourselves questions that we are uncertain about their answers. Is it going to rain tomorrow? What will be the exchange rate of euro next month? Why, where and how should I invest? Type-1 Fuzzy sets are characterized by the membership function whose value for a given element x is said to be the grade of membership having a value in the interval [0, 1]. In addition, type-1 fuzzy sets have limited capabilities to deal with uncertainty. In our thesis, we study another concept of a fuzzy description of uncertainty which is called Type-2 fuzzy sets. According to this concept, for any given element x, we can’t speak of an unambiguously specified value of the membership function. Moreover, Type-2 fuzzy sets constitute a powerful tool for handling uncertainty. The aim of our thesis is to examine the potential of the Type-2 fuzzy sets especially in decision making. So, we present basic definitions concerning Type-2 fuzzy sets, and operations on these sets are to be discussed too. Then, Type-2 fuzzy relations and methods of transformation of Type-2 fuzzy sets will be introduced. Also, the theory of Type-2 Fuzzy sets will serve for the construction of the fuzzy inference system. Finally, we utilize interval type-2 fuzzy sets in the application of Multiple Attributes Group Decision Making which is called TOPSIS.
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Strasser, Mark. "The development of a fuzzy decision-support system for dairy cattle culling decisions." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1997. http://www.collectionscanada.ca/obj/s4/f2/dsk2/ftp01/MQ29794.pdf.
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Levy, Bat-Sheva. "Fuzzy logic, a model to explain students' mathematical decision-making." Thesis, National Library of Canada = Bibliothèque nationale du Canada, 1999. http://www.collectionscanada.ca/obj/s4/f2/dsk1/tape7/PQDD_0026/MQ51391.pdf.
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Oderanti, Festus Oluseyi. "Fuzzy decision making system and the dynamics of business games." Thesis, Heriot-Watt University, 2011. http://hdl.handle.net/10399/2446.
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Effective and efficient strategic decision making is the backbone for the success of a business organisation among its competitors in a particular industry. The results of these decision making processes determine whether the business will continue to survive or not. In this thesis, fuzzy logic (FL) concepts and game theory are being used to model strategic decision making processes in business organisations. We generally modelled competition by business organisations in industries as games where each business organization is a player. A player formulates his own decisions by making strategic moves based on uncertain information he has gained about the opponents. This information relates to prevailing market demand, cost of production, marketing, consolidation efforts and other business variables. This uncertain information is being modelled using the concept of fuzzy logic. In this thesis, simulation experiments were run and results obtained in six different settings. The first experiment addresses the payoff of the fuzzy player in a typical duopoly system. The second analyses payoff in an n-player game which was used to model a perfect market competition with many players. It is an extension of the two-player game of a duopoly market which we considered in the first experiment. The third experiment used and analysed real data of companies in a case study. Here, we chose the competition between Coca-cola and PepsiCo companies who are major players in the beverage industry. Data were extracted from their published financial statements to validate our experiment. In the fourth experiment, we modelled competition in business networks with uncertain information and varying level of connectivity. We varied the level of interconnections (connectivity) among business units in the business networks and investigated how missing links affect the payoffs of players on the networks. We used the fifth experiment to model business competition as games on boards with possible constraints or restrictions and varying level of connectivity on the boards. We also investigated this for games with uncertain information. We varied the level of interconnections (connectivity) among the nodes on the boards and investigated how these a ect the payoffs of players that played on the boards. We principally used these experiments to investigate how the level of availability of vital infrastructures (such as road networks) in a particular location or region affects profitability of businesses in that particular region. The sixth experiment contains simulations in which we introduced the fuzzy game approach to wage negotiation in managing employers and employees (unions) relationships. The scheme proposes how employers and employees (unions) can successfully manage the deadlocks that usually accompany wage negotiations. In all cases, fuzzy rules are constructed that symbolise various rules and strategic variables that firms take into consideration before taken decisions. The models also include learning procedures that enable the agents to optimize these fuzzy rules and their decision processes. This is the main contribution of the thesis: a set of fuzzy models that include learning, and can be used to improve decision making in business.
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Hinojosa, William. "Probabilistic fuzzy logic framework in reinforcement learning for decision making." Thesis, University of Salford, 2010. http://usir.salford.ac.uk/26716/.
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This dissertation focuses on the problem of uncertainty handling during learning by agents dealing in stochastic environments by means of reinforcement learning. Most previous investigations in reinforcement learning have proposed algorithms to deal with the learning performance issues but neglecting the uncertainty present in stochastic environments. Reinforcement learning is a valuable learning method when a system requires a selection of actions whose consequences emerge over long periods for which input-output data are not available. In most combinations of fuzzy systems with reinforcement learning, the environment is considered deterministic. However, for many cases, the consequence of an action may be uncertain or stochastic in nature. This work proposes a novel reinforcement learning approach combined with the universal function approximation capability of fuzzy systems within a probabilistic fuzzy logic theory framework, where the information from the environment is not interpreted in a deterministic way as in classic approaches but rather, in a statistical way that considers a probability distribution of long term consequences. The generalized probabilistic fuzzy reinforcement learning (GPFRL) method, presented in this dissertation, is a modified version of the actor-critic learning architecture where the learning is enhanced by the introduction of a probability measure into the learning structure where an incremental gradient descent weight- updating algorithm provides convergence. XXIABSTRACT Experiments were performed on simulated and real environments based on a travel planning spoken dialogue system. Experimental results provided evidence to support the following claims: first, the GPFRL have shown a robust performance when used in control optimization tasks. Second, its learning speed outperforms most of other similar methods. Third, GPFRL agents are feasible and promising for the design of adaptive behaviour robotics systems.
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Chen, Zhifeng. "Consensus in group decision making under linguistic assessments." Diss., Manhattan, Kan. : Kansas State University, 2005. http://hdl.handle.net/2097/68.
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Pavuluri, Manoj Kumar. "Fuzzy decision tree classification for high-resolution satellite imagery /." free to MU campus, to others for purchase, 2003. http://wwwlib.umi.com/cr/mo/fullcit?p1418056.
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Sasikala, K. R. "Fuzzy reasoning with geographic information system : an aid to decision-making." Thesis, University of Surrey, 1997. http://epubs.surrey.ac.uk/1002/.
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Kishk, Mohammed El-Said. "An integrated fuzzy approach to whole life costing based decision making." Thesis, Robert Gordon University, 2001. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.369051.
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Mitchell, Sophia. "A Cascading Fuzzy Logic Approach for Decision Making in Dynamic Applications." University of Cincinnati / OhioLINK, 2016. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1448037866.
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Wang, Ming-hua. "A knowledge-based system approach for project management decision-making support." Thesis, University of Warwick, 1997. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.340476.
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Wagholikar, Amol S., and N/A. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Griffith University. School of Information and Communication Technology, 2007. http://www4.gu.edu.au:8080/adt-root/public/adt-QGU20071214.152324.
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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.
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Wagholikar, Amol S. "Acquisition of Fuzzy Measures in Multicriteria Decision Making Using Similarity-based Reasoning." Thesis, Griffith University, 2007. http://hdl.handle.net/10072/365403.
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Continuous development has been occurring in the area of decision support systems. Modern systems focus on applying decision models that can provide intelligent support to the decision maker. These systems focus on modelling the human reasoning process in situations requiring decision. This task may be achieved by using an appropriate decision model. Multicriteria decision making (MCDM) is a common decision making approach. This research investigates and seeks a way to resolve various issues associated with the application of this model. MCDM is a formal and systematic decision making approach that evaluates a given set of alternatives against a given set of criteria. The global evaluation of alternatives is determined through the process of aggregation. It is well established that the aggregation process should consider the importance of criteria while determining the overall worth of an alternative. The importance of individual criteria and of sub-sets of the criteria affects the global evaluation. The aggregation also needs to consider the importance of the sub-set of criteria. Most decision problems involve dependent criteria and the interaction between the criteria needs to be modelled. Traditional aggregation approaches, such as weighted average, do not model the interaction between the criteria. Non-additive measures such as fuzzy measures model the interaction between the criteria. However, determination of non-additive measures in a practical application is problematic. Various approaches have been proposed to resolve the difficulty in acquisition of fuzzy measures. These approaches mainly propose use of past precedents. This research extends this notion and proposes an approach based on similarity-based reasoning. Solutions to the past problems can be used to solve the new decision problems. This is the central idea behind the proposed methodology. The methodology itself applies the theory of reasoning by analogy for solving MCDM problems. This methodology uses a repository of cases of past decision problems. This case base is used to determine the fuzzy measures for the new decision problem. This work also analyses various similarity measures. The illustration of the proposed methodology in a case-based decision support system shows that interactive models are suitable tools for determining fuzzy measures in a given decision problem. This research makes an important contribution by proposing a similarity-based approach for acquisition of fuzzy measures.<br>Thesis (PhD Doctorate)<br>Doctor of Philosophy (PhD)<br>School of Information and Communication Technology<br>Full Text
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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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Samantra, Chitrasen. "Decision-making in fuzzy environment." Thesis, 2012. http://ethesis.nitrkl.ac.in/3956/1/chitrasen_samantra(210me2272)production_engineering.pdf.
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Decision-making is a logical human judgment process for identifying and choosing alternatives based on the values and preferences of the decision maker that mostly applied in the managerial level of the concerned department of the organization/ supply chain. Recently, decision-making has gained immense popularity in industries because of their global competitiveness and to survive successfully in respective marketplace.Therefore, decision-making plays a vital role especially in purchase department for reducing material costs, minimizing production time as well as improving the quality of product or service. But, in today’s real life problems, decision-makers generally face lot of confusions, ambiguity due to the involvement of uncertainty and subjectivity in complex evaluating criterions of alternatives. To deal such kind of vagueness in human thought the title ‘Decision-Making in Fuzzy Environment’ has focused into the emerging area of research associated with decision sciences. Multiple and conflicting objectives such as ‘minimize cost’ and ‘maximize quality of service’ are the real stuff of the decision-makers’ daily concerns. Keeping this in mind, this thesis introduces innovative decision aid methodologies for an evaluation cum selection policy analysis, based on theory of multi criteria decision-making tools and fuzzy set theory.
In the supplier selection policy, emphasis is placed on compromise solution towards the selection of best supplier among a set of alternative candidate suppliers. The nature of supplier selection process is a complex multi-attribute group decision making (MAGDM) problem which deals with both quantitative and qualitative factors may be conflicting in nature as well as contain incomplete and uncertain information. Therefore, an application of VIKOR method combined with fuzzy logic has been reported as an efficient approach to support decision-making in supplier selection problems. This dissertation also proposes an integrated model for industrial robot selection considering both objective and subjective criteria’s. The concept of Interval-Valued Fuzzy Numbers (IVFNs) combined with VIKOR method has been adapted in this analysis.
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Alas, José Ernesto. "Organizational decision making : the Fuzzy Front End." Thesis, 2011. http://hdl.handle.net/2152/ETD-UT-2011-12-4593.
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Decision-makers have many defined and widely accepted tools in place to manage projects and programs. However, can the same be said for the very early stages of projects? This research investigates what researchers are now referring to as the Fuzzy Front End of Innovation, which is defined as the territory leading up to organizational-level absorption and commercialization of the innovation process.
Despite all of the actions in establishing new operational efficiencies and project management guidelines to improve New Product Development (NPD), a formalized model does not exist for the screening and filtering of the most exceptional opportunities. The ALAS Fuzzy Front End of Innovation Process Model is proposed to help manage the innovation process. This model is based on an in-depth literature review and respondent interview data.
As a secondary topic this thesis will look to understand and propose the organizational structure required to support pre-phase Fuzzy Front End activities, governance and management’s role. This will not be a discussion on organizational types within development or engineering organizations (i.e.: matrix, product, platform organizational structures) but rather from the findings propose a structure that helps define who the key stake holders are in approving or rejecting development efforts.<br>text
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Hung, Chih-Ching, and 洪志菁. "Fuzzy Multiattribute Decision Making Model：Methods and Applications." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/30582911980576850125.
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碩士<br>國立成功大學<br>製造工程研究所碩博士班<br>92<br>Multiattribute decision-making methods are clear and simultaneously considering many decision goals. It helps the decision makers to find the best alternative under the constrained resources and conflict goals. It is difficult to solve by using mathematical programming. In most situations, the information provided by decision-maker is uncertainty to achieve level. It is not a crisp value. We must use fuzzy concept in order to overcome the uncertainty problem.
This research use fuzzy multiple attribute decision making methods to develop a systematic, objective, and considering multiple alternatives assessment. Our goal is discuss the theory of fuzzy multiple attribute decision making methods and using two cases to verify the methods. Max-min, Triangular Fuzzy Technique for Order Preference by Similarity to Ideal Solution, Fuzzy and Technique for Order Preference by Similarity to Ideal Solution, Trapezoidal Fuzzy Decision Matrix are used to find the best alternative. Finally, the essay takes the real example of semi-conductor sealing plant to illustrate the method and the result proves its best alternative.
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Chiou, Chu-Han, and 邱楚涵. "Fuzzy Multiattribute Decision Making and Fuzzy Multiattribute Group Decision Making Based on Intuitionistic Fuzzy Sets, Particle Swarm Optimization Techniques and Evidential Reasoning Methodology." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/38146881272371392299.
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碩士<br>國立臺灣科技大學<br>資訊工程系<br>102<br>Fuzzy multiattribute decision making and fuzzy multiattribute group decision making are important research topics. There may be situations that the weights of attributes of fuzzy multiattribute decision making problems and fuzzy multiattribute group decision making problems are incomplete. In recent years, some methods have been presented to deal with fuzzy multiattribute decision making problems and fuzzy multiattribute group decision making problems with incomplete certain information on the weights of attributes. In this thesis, we propose a new fuzzy multiattribute decision making method for dealing with fuzzy multiattribute decision making problems with incomplete certain information on the weights of attributes based on interval-valued intuitionistic fuzzy sets, particle swarm optimization techniques and the evidential reasoning methodology. The proposed fuzzy multiattribute decision making method can overcome the drawbacks of the existing fuzzy multiattribute decision making methods. Moreover, we also propose a new fuzzy multiattribute group decision making method for dealing with fuzzy multiattribute group decision making problems based on intuitionistic fuzzy sets and the evidential reasoning methodology. The proposed fuzzy multiattribute group decision making method can overcome the drawbacks of the existing fuzzy multiattribute group decision making methods.
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呂國忠. "Fuzzy Decision Making Analysis -- Evaluating Weapon Systems Using Ranking Fuzzy Number." Thesis, 1998. http://ndltd.ncl.edu.tw/handle/23131979699426093959.
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碩士<br>國防管理學院<br>資源管理研究所<br>86<br>In this paper, we modify Chen''s evaluating model and Hwang''s relative distance method to propose a new algorithm. That is, we develop a general and new method for evaluating weapon system, which use nine scales concepts of AHP and combine the triangular fuzzy numbers to represent fuzzy performance of the corresponding to each attribute, and establish fuzzy judgement matrix by total score of performance. Then take fuzzy number 1, 3, 5, 7, 9 to denote the weight for each attribute. Last, use ranking fuzzy numbers to evaluating the optimal alternative. For practical apphcation, we structure a practical example of evaluating anti-armor weapon system to illustrate our proposed method.
The main results of this research are listed in the following:
1. Use fuzzy numbers to represent fuzzy performance of the corresponding to each attribute, and modify Chen''s evaluating model for his shortcoming is by crisp integer to denote sub attribute judgment number.
2. Take Saaty''s 1-9 scale as evaluating range, it abase the mistake of decision making for Chen''s evaluating model is applied in extremely judgment problem.
3. Re-define Hwang''s maximal fuzzy number γmax and minimal fuzzy number γmin, which can decrease the argument for taking β1, and β2 values.
4. By illustrating example, the weight dimension of each attribute has determinant effect for evaluating alternatives.
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Tan, Yuh Jan, and 湯玉珍. "Fuzzy Theory Apply on Stock Investment Decision Making." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/10764648727356405143.
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Chen, Chi-Le, and 陳錡樂. "Computation Coordination Based on Fuzzy Group Decision Making." Thesis, 2006. http://ndltd.ncl.edu.tw/handle/04552221420976169755.
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碩士<br>國立交通大學<br>資訊管理研究所<br>94<br>As demands of data processing and computation are increasing in the booming Internet, distributed-computing environment is gaining more attention than centric-computing one. Among many proposed dictributed-computing architecture, CPS is a lightweight, Web-services-based computing sharing architecture. It is applicable to implement on trusty network in an enterprise by Web-services and Business Process Execution Language (BPEL) for providing a visualized developing environment and workflow management. However, a real-time distributed computing architecture like CPS needs to handle computing resources properly to prevent resources wasting and unbalancing. In order to have an optimized computing result, the way to coordinate computing resources will be a key factor.
Therefore, a fuzzy group decision-making module is proposed in this paper to add on CPS to provide real-time computation coordination and quality of service. This module increases computing performance efficiently and shows stability in the variable sharing environment. In this research, the module has been applied to analyze the performance of digital watermark by filter bank selection and image scrambling. As the result of this study, the performance can be improved in the aspect of speedup, efficiency and process time.
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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<br>國立交通大學<br>資訊管理所<br>91<br>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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王昱傑. "Fuzzy TOPSIS for multi-criteria group decision-making." Thesis, 2003. http://ndltd.ncl.edu.tw/handle/88465474014242170013.
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Shieh, Ren Jye, and 謝仁傑. "Overlapping and Rank Order in Fuzzy Decision Making." Thesis, 1993. http://ndltd.ncl.edu.tw/handle/78443400863977044967.
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碩士<br>國立中山大學<br>資訊管理研究所<br>81<br>Much of decision making in the real world takes place in an
environment in which the goals, the constraints, and the
consequences of possible actions are not known precisely.
Fuzziness,in the sense of Zadeh [z1],means a lack of precise
boundaries for some considered subsets of a given universe.
Since its inception in 1965, a number of researches have
investigated the use of fuzzy sets in decision making. When
fuzzy data are incorporated into the Multiple Attribute
Decision Making problem, the final ratings are no longer crisp
numbers; they are fuzzy numbers. When the final ratings are
fuzzy, it is very difficult to distinguish the best
possible course of action from the mediocre ones, or even
the worst one. In these situations the decision maker need a
measure of uncertainty to decide whether it is wise to
proceed with the best choice or more information is needed
to remove some or all of the uncertainty. Although there are
so many methods for ranking or comparing fuzzy numbers,
whatever method is chosen, the ranking can be questioned
whenever a significant overlap between fuzzy numbers exists.
This paper proposes a method for measuring the overlapping
between fuzzy numbers. Then, the overlap index is extended
for comparing fuzzy subsets and a preference index is
defined. And, an approach to quantify the extent of
possible rank order is deduced so that the decision maker can
choose the favorable rank order. Finally, such overlap index is
discussed for estimating the possibility of rank reversal and
the extent of consensus in group decision.
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劉芝帆. "Fuzzy Multiple Criteria Decision Making and its Applications in the Decision Making for Tourist City." Thesis, 2016. http://ndltd.ncl.edu.tw/handle/4sd4gb.
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Lee, Li-Wei, and 李立偉. "New Methods for Fuzzy Decision Making and Fuzzy Multiple Attributes Group Decision Making Based on Interval Type-2 Fuzzy Sets and Likelihood-Based Comparison Relations." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/32652460477341269634.
Full textAbstract:
博士<br>國立臺灣科技大學<br>資訊工程系<br>99<br>Fuzzy multiple attributes group decision making is an important research topic. In this dissertation, we present five new methods for fuzzy decision making and fuzzy multiple attributes group decision making based on interval type-2 fuzzy sets and likelihood-based comparison relations. In the first method of this dissertation, we present a new fuzzy decision making method based on likelihood-based comparison relations. First, we introduce the concepts of likelihood-based comparison relations for intervals. Then, we propose the concept of likelihood-based comparison relations for type-1 fuzzy sets and interval type-2 fuzzy sets. Then, we present a new method to rank fuzzy sets by using fuzzy targets based on the proposed likelihood-based comparison relations for fuzzy sets. Finally, we present a new fuzzy decision making method based on the proposed likelihood-based comparison relations for fuzzy sets and the proposed fuzzy ranking method. The proposed fuzzy decision making method has the advantage that the evaluated values can either be represented by crisp values, intervals, type-1 fuzzy sets or interval type-2 fuzzy sets. It can overcome the drawbacks of the existing methods due to the fact that the existing methods can not deal with the ranking of interval type-2 fuzzy sets for fuzzy decision making and can not distinguish the ranking order between the alternatives in some situations. In the second method of this dissertation, we present a new method for fuzzy multiple attributes group decision making based on the ranking values and the arithmetic operations of interval type-2 fuzzy sets. First, we present the arithmetic operations between interval type-2 fuzzy sets. Then, we present a new fuzzy ranking method to calculate the ranking values of interval type-2 fuzzy sets. We also make a comparison of the ranking values of the proposed method with the existing methods. Based on the proposed fuzzy ranking method and the proposed arithmetic operations between interval type-2 fuzzy sets, we present a new method to handle fuzzy multiple attributes group decision making problems. The proposed method provides us with a useful way to handle fuzzy multiple attributes group decision making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 fuzzy sets rather than traditional type-1 fuzzy sets to represent the evaluating values and the weights of attributes. In the third method of this dissertation, we present a new interval type-2 TOPSIS method to handle fuzzy multiple attributes group decision making problems based on interval type-2 fuzzy sets. We present a new fuzzy ranking method to calculate the ranking values of interval type-2 fuzzy sets. We also use some examples to illustrate the fuzzy multiple attributes group decision making process of the proposed method. The proposed method provides us with a useful way to handle fuzzy multiple attributes group decision making problems in a more flexible and more intelligent manner due to the fact that it uses interval type-2 fuzzy sets rather than traditional type-2 fuzzy sets to represent the evaluating values and the weights of the attributes. In the fourth method of this dissertation, we present a new method for fuzzy multiple criteria hierarchical group decision making based on arithmetic operations and fuzzy preference relations of interval type-2 fuzzy sets. Because the time complexity of the proposed method is O(nk), where n is the number of criteria and k is the number of decision-makers, it is more efficient than the existing methods. Moreover, the proposed method can overcome the drawback of the existing method due to the fact that it can handle evaluating values represented by nonnormal interval type-2 fuzzy sets. The proposed method provides us with a useful way to handle fuzzy multiple criteria hierarchical group decision making problems. In the fifth method of this dissertation, we present a new method for interval linguistic labels aggregation and consensus measure for autocratic decision making using group recommendations based on the likelihood-based comparison relations of interval linguistic labels and the proposed Interval Linguistic Labels Ordered Weighted Average (ILLOWA) operator. First, we propose the concepts of likelihood-based comparison relations of interval linguistic labels. Then, propose the ILLOWA operator to aggregate interval linguistic labels. Based on the likelihood-based comparison relations of interval linguistic labels and the proposed ILLOWA operator, we propose a new method for interval linguistic labels aggregation and consensus measure for autocratic decision making using group recommendations. The proposed method can overcome the drawbacks of existing methods. It provides us with a useful way for interval linguistic labels aggregation and consensus measure for autocratic decision making using group recommendations.
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Al-Mutairi, Mubarak. "Preference Uncertainty and Trust in Decision Making." Thesis, 2007. http://hdl.handle.net/10012/2754.
Full textAbstract:
A fuzzy approach for handling uncertain preferences is developed within the paradigm of the Graph Model for Conflict Resolution and new advances in trust modeling and assessment are put forward for permitting decision makers (DMs) to decide with whom to cooperate and trust in order to move from a potential resolution to a more preferred one that is not attainable on an individual basis. The applicability and the usefulness of the fuzzy preference and trust research for giving an enhanced strategic understanding about a dispute and its possible resolution are demonstrated by employing a realworld environmental conflict as well as two generic games that represent a wide range of real life encounters dealing with trust and cooperation dilemmas.
The introduction of the uncertain preference representation extends the applicability of the Graph Model for Conflict Resolution to handle conflicts with missing or incomplete preference information. Assessing the presence of trust will help to compensate for the missing information and bridge the gap between a desired outcome and a feared betrayal. These advances in the areas of uncertain preferences and trust have potential applications in engineering decision making, electronic commerce, multiagent systems, international trade and many other areas where conflict is present.
In order to model a conflict, it is assumed that the decision makers, options, and the preferences of the decision makers over possible states are known. However, it is often the case that the preferences are not known for certain. This could be due to lack of information, impreciseness, or misinformation intentionally supplied by a competitor. Fuzzy logic is applied to handle this type of information. In particular, it allows a decision maker to express preferences using linguistic terms rather than exact values. It also makes use of data intervals rather than crisp values which could accommodate minor shifts in values without drastically changing the overall results. The four solution concepts of Nash, general metarationality, symmetric metarationality, and sequential stability for determining stability and potential resolutions to a conflict, are extended to accommodate the new fuzzy preference representation. The newly proposed solution concepts are designed to work for two and more than two decision maker cases. Hypothetical and real life conflicts are used to demonstrate the applicability of this newly proposed procedure.
Upon reaching a conflict resolution, it might be in the best interests of some of the decision makers to cooperate and form a coalition to move from the current resolution to a better one that is not achievable on an individual basis. This may require moving to an intermediate state or states which may be less preferred by some of the coalition members while being more preferred by others compared to the original or the final state. When the move is irreversible, which is the case in most real life situations, this requires the existence of a minimum level of trust to remove any fears of betrayal. The development of trust modeling and assessment techniques, allows decision makers to decide with whom to cooperate and trust. Illustrative examples are developed to show how this modeling works in practice.
The new theoretical developments presented in this research enhance the applicability of the Graph Model for Conflict Resolution. The proposed trust modeling allows a reasonable way of analyzing and predicting the formation of coalitions in conflict analysis and cooperative game theory. It also opens doors for further research and developments in trust modeling in areas such as electronic commerce and multiagent systems.
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Chang, Cheng-Wen, and 張正文. "FUZZY MULTIPLE ATTRIBUTE DECISION MAKING ANALYSIS─EVALUATE WEAPON SYSTEMS USING A SIMPE GROUP DECISION MAKING METHOD." Thesis, 2000. http://ndltd.ncl.edu.tw/handle/29876496561015782396.
Full textAbstract:
碩士<br>國防管理學院<br>資源管理研究所<br>88<br>ABSTRACT
The purpose of this paper is to propose a simple and easy fuzzy group decision-making method that could be applied by decision-makers in evaluating weapon system. It is hard to give a precise number to present the attribute value through human’s thoughts and experience in the process of evaluating weapon system. Due to the complicacy and the lacking of information from system, the differences of attributes only can be described by linguistic terms often. Thus, the expert’s opinions are described by linguistic terms in this paper, which can be expressed and numbered by trapezoidal fuzzy number. To make the consensus of the experts consistent, we utilize fuzzy Delphi method to adjust the fuzzy rating of every expert to achieve the consistent condition. For aggregating many experts’ opinions, we take the operation of fuzzy number to get the mean of fuzzy rating, and the mean of weight, . The model of evaluating weapon system is a kind of multiple alternative and multiple attribute method. We can obtain the experts’ fuzzy rating of attributes relative to alternatives by means of fuzzy Delphi method. The fuzzy decision matrix is constructed by means of fuzzy rating, . We can derive the aggregated fuzzy numbers by multiplying the fuzzy decision matrix corresponding fuzzy attribute weight . The final results become ranking fuzzy number problem. We also propose an easy ranking procedure to rank aggregated fuzzy numbers, here. Finally, for practical application, we structure a practical example of evaluating self-propelled anti—aircraft guns weapon system to illustrate our proposed method.
The primary results of this research are listed in the following：
1.In the Fuzzy Multiple Attribute Decision Making Analysis, we propose a simple and easy fuzzy group decision-making method which can be applied by decision-makers in evaluating weapon system.
2.To make the consensus of the experts consistent, we utilize fuzzy Delphi method to adjust the fuzzy rating of every expert to achieve the consensus condition.
3.We also propose an easy ranking procedure to rank aggregated fuzzy numbers.
Keywords: 1. Military Application 2. Fuzzy Number 3. Ranking Fuzzy Number 4. Fuzzy Multiple Attribute Decision Making 5. Decision-Making
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Chou, Chi-Chun, and 鄒治群. "Internet Simulation of Making Decision on Buying Houses through Fuzzy Decision Policy." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/06472447032444920065.
Full textAbstract:
碩士<br>大葉大學<br>電機工程學系<br>99<br>Fuzzy theory is information about human minds to make quantitative decisions. We need a correct mathematical model to achieve traditional control, but we almost spent a lot of time to create the mathematical model. Fuzzy theory simplifies problems by thinking like human beings. It also achieves the same goals of traditional control.
Fuzzy decision making is in a lot of information to find the rules more closely tied to human thinkings and decision-making. Buying a house has many items to be considered such as, price, house pattern, story, house size, parking, traffic, shopping, distance to school, distance to work, etc. The increasing items downgrade a person's ability to judge. So what is be the best or better decision we can make? According to the real time characteristic of internet, abundant information can be gathered together, and fuzzy decision making can help make the best choice for our reference. This thesis shows of internet simulation of decision making on buying houses through fuzzy decision policy.
Key Words: fuzzy decision, fuzzy theory, buying house, internet
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Patel, Pretesh Bhoola. "A forecasting of indices and corresponding investment decision making application." Thesis, 2007. http://hdl.handle.net/10539/2191.
Full textAbstract:
Student Number : 9702018F -
MSc(Eng) Dissertation -
School of Electrical and Information Engineering -
Faculty of Engineering and the Built Environment<br>Due to the volatile nature of the world economies, investing is crucial in ensuring an individual is prepared for future
financial necessities. This research proposes an application, which employs computational intelligent methods that could
assist investors in making financial decisions. This system consists of 2 components. The Forecasting Component (FC) is
employed to predict the closing index price performance. Based on these predictions, the Stock Quantity Selection
Component (SQSC) recommends the investor to purchase stocks, hold the current investment position or sell stocks in
possession. The development of the FC module involved the creation of Multi-Layer Perceptron (MLP) as well as Radial
Basis Function (RBF) neural network classifiers. TCategorizes that these networks classify are based on a profitable trading
strategy that outperforms the long-term “Buy and hold” trading strategy. The Dow Jones Industrial Average, Johannesburg
Stock Exchange (JSE) All Share, Nasdaq 100 and the Nikkei 225 Stock Average indices are considered. TIt has been
determined that the MLP neural network architecture is particularly suited in the prediction of closing index price
performance. Accuracies of 72%, 68%, 69% and 64% were obtained for the prediction of closing price performance of the
Dow Jones Industrial Average, JSE All Share, Nasdaq 100 and Nikkei 225 Stock Average indices, respectively. TThree
designs of the Stock Quantity Selection Component were implemented and compared in terms of their complexity as well as
scalability. TComplexity is defined as the number of classifiers employed by the design. Scalability is defined as the ability of
the design to accommodate the classification of additional investment recommendations. TDesigns that utilized 1, 4 and 16
classifiers, respectively, were developed. These designs were implemented using MLP neural networks, RBF neural
networks, Fuzzy Inference Systems as well as Adaptive Neuro-Fuzzy Inference Systems. The design that employed 4
classifiers achieved low complexity and high scalability. As a result, this design is most appropriate for the application of
concern. It has also been determined that the neural network architecture as well as the Fuzzy Inference System
implementation of this design performed equally well.
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橋山, 智訓, and Tomonori HASHIYAMA. "A Study on Fuzzy Models of Decision Making Process." Thesis, 1996. http://hdl.handle.net/2237/15678.
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Hui-ChiChuang and 莊惠棋. "A Fuzzy Decision-Making Approach to Cause-Effect Modeling." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/5v294g.
Full textAbstract:
碩士<br>國立成功大學<br>資訊管理研究所<br>101<br>It is not a simple task to depict cause and effect relations in a complex systems, especially they are always dynamic and constantly changing. However, this task is very critical to achieve optimum and balanced status in any situation. Nowadays, there are a lot of tools for the expression and analysis of the relationship of causes and effects in these systems. These methods or tools have been developed to assist this, and they have been used widely and effectively in various fields.
This study utilizes fuzzy cognitive maps (FCMs) to represent the cause and effect relationships in a complex system. Since fuzzy cognitive maps have some limitations, such as using fixed weights when the system changes, examining direct influences only, and so on, we use Hebbian learning, fuzzy transitive closure and a convergence method to overcome these. We get much better performance from adjusting the weight matrix using this approach. Furthermore, we illustrate the use of this method with real medical data, and predict the probability of getting a stroke using fuzzy cognitive maps. It is anticipated that this can provide extra information to doctors or patients with regard to the health status of the latter.
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Tu, Chien-Ping, and 杜建平. "Intuitionictic Fuzzy VIKOR Approach for Multi-Criteria Decision Making." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/53192838584504022575.
Full textAbstract:
碩士<br>國防大學管理學院<br>運籌管理學系<br>100<br>When decision-makers face of the guidelines conflicting decision-making, multi-criteria decision-making methods (Multiple-Criteria Decision Making, of MCDM) has been the best tool to be used as the analysis. VIKOR France in 1998 was Opricovic proposed is a multi-criteria decision making to optimize one of the compromise solution, the concept of the Department of proximity distance is the ideal solution for the sorting of the program. However, VIKOR method in the calculation formula, likely to cause too enlarge objections to the scoring, you can not get a reasonable sort results. Decision makers given the assessed value of the program and guidelines, there are also ambiguous subjective semantics, intuitionistic fuzzy Atanassov published in 1986 by adding the hesitation degree of semantic expression than fuzzy sets have a better ability to express. Therefore, this study will be modified missing for VIKOR law, and tried to solve the decision-makers in the decision-making process in the fuzzy uncertainty, combined with intuitive fuzzy construct a quality hybrid multi-criteria decision making approach in order to obtain a more reasonable and realistic sort the results .
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LIN, QIN-JIN, and 林清進. "A framework of multicriterion decision making using fuzzy preferences." Thesis, 1992. http://ndltd.ncl.edu.tw/handle/52602581428514965381.
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Jou, Jyh-Rong, and 周志榮. "Fuzzy Sets Theory on Decision Making for Product Development." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/83805156939230545451.
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Chang, Ching-wen, and 張瀞文. "Multi-Criteria Decision Making Based on Fuzzy TOPSIS Model." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/77303652881998390293.
Full textAbstract:
碩士<br>南華大學<br>資訊管理學系碩士班<br>98<br>Multi-criteria decision-making includes multi-criteria and program evaluations. This method can help decision-makers to make decision based on actual situations, providing decision-makers with an optimal program. Software project cost for decision-makers, which includes multi-estimate criteria and multi-program. Therefore, evaluating software cost is classified as a multi-criteria decision-making problem. This study uses the software project cost evaluation case for a study case. The contributions of this study are as follows, 1) Use the decision tree J48 to filter attributes. We can find the potential interaction between attributes, determine how attributes affect the final evaluation, and point out the important attribute. 2) The study proposes a Fuzzy Q-uantiles Method to fuzzfy attributes. It can make the results more reasonable. 3) Use the Fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model, including (a) using TOPSIS and Fuzzy theory to identify high-risk costs, (b) using information fusion technology into weight calculations. 4) Use a Constructive Cost Model database to verify. The database is a public database; it’s including 63projects and 16 attributes.Analytical results show that the fuzzy TOPSIS model can solve the above problem and help decision-makers evaluate software cost effectively.
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Liang, Jia-Ling, and 梁佳玲. "Fuzzy Multi-Criteria Decision Making Methods on Various Applications." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/13686281939778988844.
Full textAbstract:
博士<br>義守大學<br>資訊工程學系博士班<br>99<br>Decision or selection making is a vital part of daily life; of which the major concern is that almost all issues requiring decisions have multiple, often conflicting, criteria. In reality, there is no avoidance of the coexistence of qualitative and quantitative data, and the data are often full of complex, fuzziness and uncertainty. Due to this, it is difficult to make a proper selection or decision based on individual subjective judgment. With closer cooperation between the decision-makers and schemers, the actual demands of problems can be realized.
Therefore, the thesis proposes multi-criteria decision making methods and fuzzy multi-criteria decision making methods to evaluate decision problems in various applications. The methodologies in the thesis include Analytic Hierarchy Process (AHP), Technique for Order Preference by Similarity to Ideal Solution (TOPSIS), Preference Ranking Organization METHod for Enrichment Evaluation (PROMETHEE), Consistent Fuzzy Preference Relations, Incomplete Linguistic Preference Relations and Fuzzy VlseKriterijumska Optimizacija I Kompromisno Resenje (Fuzzy VIKOR). The methodologies have be analyzed and compared each other with two empirical cases in web portals and healthcare organizations and several authenticated examples.
These methodologies can properly mediate the conflicts and contradictions during the decision-making processes. The thesis proposes an objective evaluation suggestions to slove the traditional decision-making problems effectively, such as: the lack of flexibility, computational complexity and inconsistent. The results provide useful suggestions for both researchers and decision-makers in every fields.
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Lin, Chih Ting, and 林致廷. "Group Decision Making Based on Interval-Valued Fuzzy Numbers." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/43647343520455128979.
Full textAbstract:
碩士<br>長庚大學<br>工商管理學系<br>99<br>Abstract
Decision-making cause uncertain meaning in language is almost different from the real situation. Zadeh (1965) First proposed fuzzy theory to measure vague comments. In the complicated decision environment, single decision-maker is too subjective, We need experts comments with different professional backgrounds, group decision-making, consensus combined experts is much closer than real decision result, the purpose of this study presented a new group decision-making method based on interval-valued fuzzy numbers.
The study focused on experts’ consensus, it based on TOPSIS used interval-valued fuzzy numbers proposed by Ashtiani et al. (2009), using similarity measures between interval-valued fuzzy numbers proposed by Chen and Chen (2009) to measuring distance of horizontal-axis, and similarity between X-axis and Y-axis, effectively handling information filtering problems based on interval-valued fuzzy numbers, revising the similarity measures method in Ashtiani et al. (2009), and interpreting by a numerical case.
Today employees are important assets of companies and enterprises, especially Hi-tect R&D personnel, human recruiting of pre-work can not be ignored. In empirical study, asking the directors’perspective in the department of human resource, the provider of wireless broadband soluations in Hisnchu，using seven criteria to comment overall performance of the engineers from various recruitment channels, proofing the practicality using the method of this study.
The results of the priority order, internal recommendation, alternative R&D, and internet human resource agency. With the weight of parameter to represent the importance of the directors, and comparing the valued of the six sets, there is no difference after changing the values, it means the directors’ comments is in agreement. The result shows the directors tend to choose the interviewee recommended by consultants, it also echoed the effectiveness of industry-university cooperation in recent years.
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Yang, Ming-wey, and 楊明煒. "New Methods for Fuzzy Multiple Attributes Group Decision Making Based on Ranking Interval Type-2 Fuzzy Sets and Multicriteria Fuzzy Decision Making Based on Ranking Interval-Valued Intuitionistic Fuzzy Values." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/86948971640696340099.
Full textAbstract:
碩士<br>國立臺灣科技大學<br>資訊工程系<br>99<br>In recent years, some researchers proposed fuzzy multiple attributes group decision making methods based on ranking interval type-2 fuzzy sets. In this thesis, we present a new method for fuzzy multiple attributes group decision making based on ranking interval type-2 fuzzy sets. First, we present a new method for ranking interval type-2 fuzzy sets. Then, we present a new method for multiple attributes group decision making based on the proposed ranking method of interval type-2 fuzzy sets. In this thesis, we also present a new method for multicriteria fuzzy decision making based on ranking interval-valued intuitionistic fuzzy sets, where interval-valued intuitionistic fuzzy values are used to represent evaluating values of the decision-maker with respect to alternatives. First, we propose a new method for ranking interval-valued intuitionistic fuzzy values. Based on the proposed fuzzy ranking method of interval-valued intuitionistic fuzzy values, we propose a new method for multicriteria fuzzy decision making. The methods presented in this thesis provide us useful ways for dealing with fuzzy multiple attributes group decision making problems and multicriteria fuzzy decision making problems.
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CHI, HA THI XUAN, and HA THI XUAN CHI. "IMPROVED APPROACHES FOR RANKING GENERALIZED FUZZY NUMBERS AND FUZZY MUTIL-CRITERIA DECISION MAKING." Thesis, 2014. http://ndltd.ncl.edu.tw/handle/5qfyy3.
Full textAbstract:
博士<br>國立臺灣科技大學<br>工業管理系<br>102<br>Ranking fuzzy numbers, a significant component in decision making process, supports a decision maker in selecting the optimal solution. Althoung there are many existing ranking methods for fuzzy numbers, most of them suffer from some shortcomings. To overcome these shortcomings, this study proposes a new ranking approach for both normal and generalized fuzzy numbers that ensures full consideration of all information of fuzzy numbers. The proposed approach integrates the concept of centroid point, the left and the right (LR) areas between fuzzy numbers, height of a fuzzy number and the degree of decision maker’s optimism. Several numerical examples are presented to illustrate the efficiency and superiority of the proposed.
To reduce uncertainty in decision making and avoid loss of information, this study also proposed a new fuzzy multi-criteria decision making (MCDM) approach based on the proposed ranking method for generalized fuzzy numbers. The applicability of the proposed fuzzy MCMD model is illustrated through a case study.
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Lien-Chen-Chuin and 連振權. "Fuzzy Multiple Attribute Decision Making By Fuzzy Cognitive Maps- For Example Weapon System Evaluation." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/83176626438359525531.
Full textAbstract:
碩士<br>國防管理學院<br>國防決策科學研究所<br>92<br>ABSTRACT
Many human’s decisions are made under uncertainty and risky situations. Policymakers deal with problems including multiple and estimative attributes. However, these attributes often conflict with each other. Therefore, when we meet multiple attribute decision making problems, we have to decide the evaluating criterion first. This study will use the Fuzzy Cognitive Maps to decide whether the criterion is dependent, then use it to obtain the strategic plan. We’ll use the proposed method in this paper to solve the problem. If the criterions are independent, we can use the Fuzzy Multiple Attribute Decision Making to calculate the result, this study will use Analytic Hierarchy Process (AHP) to compute the result. Furthermore, we integrated the marshal of every scheme for the policymaker’s reference. Then, we evaluate the tanks of the three different countries we used for the case study in this paper.
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Tsay, Kurn Liang, and 蔡坤良. "Application of Fuzzy Multiple Attribute Decision Making on River Conservation." Thesis, 1996. http://ndltd.ncl.edu.tw/handle/hqcwx6.
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Chang, Hsin-i., and 張欣儀. "Simulating Strategies in Handling Heterogeneous Group Fuzzy Decision-making Process." Thesis, 2004. http://ndltd.ncl.edu.tw/handle/gt7rv3.
Full textAbstract:
碩士<br>朝陽科技大學<br>工業工程與管理系碩士班<br>92<br>Although the objectives in the firms are obvious to all employees, the gaps of professional background, positions and the indefinite acknowledgement of the issue will produce different opinions toward making decision. When the experts’ opinions are not considered with the same intensity, it is known as a heterogeneous group decision-making problem. In many situations, making decisions depend on numerous factors and therefore given the limitations of human ability, it is too complicate to deal with. The nature of evaluation criteria and the subjectiveness of decision makers often create a certain degree of uncertainty and fuzziness on the decision problem and its analysis process. Therefore, how to apply appropriate theories to deal with this type of group decision-making problem has become an important research issue.
In the study we apply a fuzzy multi-criteria decision-making model to heterogeneous group decision marking problems. Furthermore, simulation is used to analyze the coordinator’s strategies in handling the decision-making process to ensure the quality and effectiveness of decision-making, that is, to achieve the consensus of the alternative rapidly, shorten the duration of the meeting, and reduce the possibility of conflicts.
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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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Lin, Wen-Bin, and 林文彬. "Fuzzy decision making approach in water resource planning and management." Thesis, 1994. http://ndltd.ncl.edu.tw/handle/25631794326396587251.
Full textAbstract:
碩士<br>淡江大學<br>水資源及環境工程所<br>82<br>The goal of this study focuses on the application of fuzzy
theory applied to water resource planning to deal with fuzzy
in- formation which is ignored by traditional method. Two cases
are practiced to illustrate it's applicable to wa- ter resource
planning. And the result's tend to be conservative, but through
the modification present here, the deficiency could to overcome
successfully.
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Huang, Shiao-Hui, and 黃曉惠. "Applying Fuzzy Multi-Criteria Decision Making For Evaluating Action Websites." Thesis, 2009. http://ndltd.ncl.edu.tw/handle/30832586035040584608.
Full textAbstract:
碩士<br>元智大學<br>資訊管理學系<br>97<br>Recently there are two major incidents that effect Taiwan’s economy. One is Non Typical Influenza, which keep people away from the street. The other incident was the effect of Global Financial Crisis, which cut down everyone’s income, and turns to the Internet, by saving other travel expenses. Also due to the new habit for customer shopping, a lot more had gone and truth Online Shopping, therefore the transaction from online shopping had become more commonly.
This paper was written by the reason of in search to establish an objective, evaluable Online Shopping Customer Service Evaluation System. First of all this paper collected related documentations from local and foreign, established basic evaluation theory, and then by using Internet Questionnaires, to analyze the authentic, effectiveness of the results, then it used TOPSIS method to evaluate the overall online shopping customer service systems.