Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277366
Xu Xu, Jie Lin, Dongming Xu
Supplier selection has a critical effect on the competitiveness of the entire supply chain network. It is not only a significant work in supply chain management but also a complex decision making problem which includes both qualitative and quantitative factors. Research results indicate that the supplier selection process appears to satisfy different evaluation criteria and business model in deciding the success of the supply chain. Supplier selection problem related to organization strategy and it needs more critical analysis. This paper proposes a novel approach that combines expert domain knowledge with Apriori algorithm of data mining to discover the pattern of supplier under the methodology of Domain-Driven Data Mining (D3M). Apriori algorithm of data mining with the help of Intuitionistic Fuzzy Set Theory (IFST) is employed during the process of mining. The overall patterns obtained help in deciding the final selection of suppliers. Finally, AHP is used to efficiently tackle both quantitative and qualitative decision factors involved in ranking of suppliers with the help of pattern achieved. An example searching for pattern of supplier is used to demonstrate the effective implementation procedure of proposed method. The proposed method can provide the guidelines for the decision makers to effectively select their suppliers in the current competitive business scenario.
{"title":"Mining pattern of supplier with the methodology of domain-driven data mining","authors":"Xu Xu, Jie Lin, Dongming Xu","doi":"10.1109/FUZZY.2009.5277366","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277366","url":null,"abstract":"Supplier selection has a critical effect on the competitiveness of the entire supply chain network. It is not only a significant work in supply chain management but also a complex decision making problem which includes both qualitative and quantitative factors. Research results indicate that the supplier selection process appears to satisfy different evaluation criteria and business model in deciding the success of the supply chain. Supplier selection problem related to organization strategy and it needs more critical analysis. This paper proposes a novel approach that combines expert domain knowledge with Apriori algorithm of data mining to discover the pattern of supplier under the methodology of Domain-Driven Data Mining (D3M). Apriori algorithm of data mining with the help of Intuitionistic Fuzzy Set Theory (IFST) is employed during the process of mining. The overall patterns obtained help in deciding the final selection of suppliers. Finally, AHP is used to efficiently tackle both quantitative and qualitative decision factors involved in ranking of suppliers with the help of pattern achieved. An example searching for pattern of supplier is used to demonstrate the effective implementation procedure of proposed method. The proposed method can provide the guidelines for the decision makers to effectively select their suppliers in the current competitive business scenario.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127952110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277359
T. Abdelkader, S. Naik, A. Nayak, F. Karray
In contention-based wireless networks, collisions between data packets can be reduced by introducing a random delay before each transmission. Backoff schemes are those that provide the backoff interval from which the random delay is drawn. In this paper, we propose a new scheme which calculates the backoff interval dynamically according to the network conditions. The network conditions are measured locally by each node, which supports the distributed nature of the vehicular networks. The measures are used by a fuzzy inference system to calculate the backoff interval. We compare the proposed scheme with other known schemes: the binary exponential backoff (BEB), the sensing backoff algorithm (SBA) and an optimal scheme which requires the knowledge of the number of nodes in the network (Genie). The evaluation measures are the throughput and fairness. Results show an improvement of the fuzzy-based schemes compared to the BEB and SBA, especially for large number of nodes in the network.
{"title":"Adaptive backoff scheme for contention-based vehicular networks using fuzzy logic","authors":"T. Abdelkader, S. Naik, A. Nayak, F. Karray","doi":"10.1109/FUZZY.2009.5277359","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277359","url":null,"abstract":"In contention-based wireless networks, collisions between data packets can be reduced by introducing a random delay before each transmission. Backoff schemes are those that provide the backoff interval from which the random delay is drawn. In this paper, we propose a new scheme which calculates the backoff interval dynamically according to the network conditions. The network conditions are measured locally by each node, which supports the distributed nature of the vehicular networks. The measures are used by a fuzzy inference system to calculate the backoff interval. We compare the proposed scheme with other known schemes: the binary exponential backoff (BEB), the sensing backoff algorithm (SBA) and an optimal scheme which requires the knowledge of the number of nodes in the network (Genie). The evaluation measures are the throughput and fairness. Results show an improvement of the fuzzy-based schemes compared to the BEB and SBA, especially for large number of nodes in the network.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125339761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277427
Chih-Ching Hsiao
The rough set theory is successes to deal with imprecise, incomplete or uncertain for information system. Fuzzy set and the rough set theories turned out to be particularly adequate for the analysis of various types of data, especially, when dealing with inexact, uncertain or vague knowledge. In this paper, we propose an novel algorithm, which termed as Rough-Fuzzy C-regression model (RFCRM), that define fuzzy subspaces in a fuzzy regression manner and also include Rough-set theory for TSK modeling with robust capability against outliers.
{"title":"Robust function approximation based on fuzzy sets and rough sets","authors":"Chih-Ching Hsiao","doi":"10.1109/FUZZY.2009.5277427","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277427","url":null,"abstract":"The rough set theory is successes to deal with imprecise, incomplete or uncertain for information system. Fuzzy set and the rough set theories turned out to be particularly adequate for the analysis of various types of data, especially, when dealing with inexact, uncertain or vague knowledge. In this paper, we propose an novel algorithm, which termed as Rough-Fuzzy C-regression model (RFCRM), that define fuzzy subspaces in a fuzzy regression manner and also include Rough-set theory for TSK modeling with robust capability against outliers.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121535354","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5276884
V. Torra, Y. Narukawa
Intuitionistic Fuzzy Sets (IFS) are a generalization of fuzzy sets where the membership is an interval. That is, membership, instead of being a single value, is an interval. A large number of operations have been defined for this type of fuzzy sets, and several applications have been developed in the last years. In this paper we describe hesitant fuzzy sets. They are another generalization of fuzzy sets. Although similar in intention to IFS, some basic differences on their interpretation and on their operators exist. In this paper we review their definition, the main results and we present an extension principle, which permits to generalize existing operations on fuzzy sets to this new type of fuzzy sets. We also discuss their use in decision making.
{"title":"On hesitant fuzzy sets and decision","authors":"V. Torra, Y. Narukawa","doi":"10.1109/FUZZY.2009.5276884","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5276884","url":null,"abstract":"Intuitionistic Fuzzy Sets (IFS) are a generalization of fuzzy sets where the membership is an interval. That is, membership, instead of being a single value, is an interval. A large number of operations have been defined for this type of fuzzy sets, and several applications have been developed in the last years. In this paper we describe hesitant fuzzy sets. They are another generalization of fuzzy sets. Although similar in intention to IFS, some basic differences on their interpretation and on their operators exist. In this paper we review their definition, the main results and we present an extension principle, which permits to generalize existing operations on fuzzy sets to this new type of fuzzy sets. We also discuss their use in decision making.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122987777","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277196
Jeongje Park, Liang-Qi Wu, Jaeseok Choi, J. Cha, A. El-Keib, J. Watada
This paper proposes a fuzzy linear programming (LP)-based solution approach for the long-term multi-stages best generation mix (BGM) problem considering wind turbine generators (WTG) and solar cell generators (SCG), and CO2 emissions constraints. The proposed method uses fuzzy set theory to consider the uncertain circumstances ambiguities associated with budgets and reliability criterion level. The proposed approach provides a more flexible solution compared to a crisp robust plan. The effectiveness of the proposed approach is demonstrated by applying it to solve the multi-years best generation mix problem on the Korean power system, which contains nuclear, coal, LNG, oil, pumped-storage hydro, and WTGs and SCGs.
{"title":"Fuzzy theory-based best generation mix considering renewable energy generators","authors":"Jeongje Park, Liang-Qi Wu, Jaeseok Choi, J. Cha, A. El-Keib, J. Watada","doi":"10.1109/FUZZY.2009.5277196","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277196","url":null,"abstract":"This paper proposes a fuzzy linear programming (LP)-based solution approach for the long-term multi-stages best generation mix (BGM) problem considering wind turbine generators (WTG) and solar cell generators (SCG), and CO2 emissions constraints. The proposed method uses fuzzy set theory to consider the uncertain circumstances ambiguities associated with budgets and reliability criterion level. The proposed approach provides a more flexible solution compared to a crisp robust plan. The effectiveness of the proposed approach is demonstrated by applying it to solve the multi-years best generation mix problem on the Korean power system, which contains nuclear, coal, LNG, oil, pumped-storage hydro, and WTGs and SCGs.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126092461","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277172
K. Ukai, Y. Ando, M. Mizukawa
In this research, we propose the robot technology (RT) Ontology for Interactive Human-Space Design and Intelligence to make robot provide appropriate services according to situations. For this, we propose to define that "Main task" as a task that the user requests and "Tsuide task" as an essential task accompanied with the "Main task" to complete it. We focus on the development of the system for identifying and defining "Tsuide task" that is changed by the intention of user and the main task. We name the proposing technique as "RT Ontology" which can be used for the infrastructure technology for structuring space information. In this paper, we describe the outline of RT Ontology and experiments.
{"title":"Investigation of user RT-service generation system design","authors":"K. Ukai, Y. Ando, M. Mizukawa","doi":"10.1109/FUZZY.2009.5277172","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277172","url":null,"abstract":"In this research, we propose the robot technology (RT) Ontology for Interactive Human-Space Design and Intelligence to make robot provide appropriate services according to situations. For this, we propose to define that \"Main task\" as a task that the user requests and \"Tsuide task\" as an essential task accompanied with the \"Main task\" to complete it. We focus on the development of the system for identifying and defining \"Tsuide task\" that is changed by the intention of user and the main task. We name the proposing technique as \"RT Ontology\" which can be used for the infrastructure technology for structuring space information. In this paper, we describe the outline of RT Ontology and experiments.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121175927","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277270
Fuyuko Ito, T. Hiroyasu, M. Miki, Hisatake Yokouchi
Recommendation methods help online users to purchase products more easily by presenting products that are likely to match their preferences. In these methods, user profiles are constructed according to past activities on the site. When a user accesses an e-commerce site, the user preferences may change during the course of web shopping. We called this a “preference shift” in this paper. However, conventional recommendation methods suppose that user profiles are static, and therefore these methods cannot follow the preference shift. Here, a novel product recommendation method is proposed, which responds to the preference shift. With use of this recommendation method, the users remain at the site longer than before. This paper discusses the detection method for finding the preference shift timing using time-series clustering. In the proposed method, the products preferred by a user are clustered and the preference shift timing is detected as the change in the clustering results.
{"title":"Detection of preference shift timing using time-series clustering","authors":"Fuyuko Ito, T. Hiroyasu, M. Miki, Hisatake Yokouchi","doi":"10.1109/FUZZY.2009.5277270","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277270","url":null,"abstract":"Recommendation methods help online users to purchase products more easily by presenting products that are likely to match their preferences. In these methods, user profiles are constructed according to past activities on the site. When a user accesses an e-commerce site, the user preferences may change during the course of web shopping. We called this a “preference shift” in this paper. However, conventional recommendation methods suppose that user profiles are static, and therefore these methods cannot follow the preference shift. Here, a novel product recommendation method is proposed, which responds to the preference shift. With use of this recommendation method, the users remain at the site longer than before. This paper discusses the detection method for finding the preference shift timing using time-series clustering. In the proposed method, the products preferred by a user are clustered and the preference shift timing is detected as the change in the clustering results.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131492212","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277357
B. Seselja, A. Tepavčević
Generalized or relational-valued fuzzy sets are mappings from a set X to a relational system S = (S, p). Representation of collections of subsets by relational-valued fuzzy sets in the cutworthy framework is presented. It is proved that for every collection F of subsets of a set X there is a relational system S = (S, p) and a fuzzy set μ : X → S, such that the collection of cuts of μ coincides with F.
{"title":"Representation by cuts in the framework of relational valued fuzzy sets","authors":"B. Seselja, A. Tepavčević","doi":"10.1109/FUZZY.2009.5277357","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277357","url":null,"abstract":"Generalized or relational-valued fuzzy sets are mappings from a set X to a relational system S = (S, p). Representation of collections of subsets by relational-valued fuzzy sets in the cutworthy framework is presented. It is proved that for every collection F of subsets of a set X there is a relational system S = (S, p) and a fuzzy set μ : X → S, such that the collection of cuts of μ coincides with F.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133728058","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277238
T. Lin
Granular computing (GrC) is a recent label, roughly speaking, jointly coined by Lin and Zadeh in 1996 to denote an emerging technology that is based on the computing/mathematical theory of an ancient concept of granulation. In this paper, we present the rdquofinalrdquo GrC model that simplify the earlier version.
{"title":"The ”Final” model of granular computing","authors":"T. Lin","doi":"10.1109/FUZZY.2009.5277238","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277238","url":null,"abstract":"Granular computing (GrC) is a recent label, roughly speaking, jointly coined by Lin and Zadeh in 1996 to denote an emerging technology that is based on the computing/mathematical theory of an ancient concept of granulation. In this paper, we present the rdquofinalrdquo GrC model that simplify the earlier version.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133375906","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2009-10-02DOI: 10.1109/FUZZY.2009.5277370
H. Ishibuchi, Yusuke Nakashima, Y. Nojima
Recently evolutionary multiobjective optimization (EMO) algorithms have been actively used for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems where EMO algorithms are used to search for a number of non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. The main advantage of the use of EMO algorithms for fuzzy system design over single-objective optimizers is that multiple alternative fuzzy rule-based systems with different accuracy-interpretability tradeoffs are obtained by their single run. The decision maker can choose a single fuzzy rule-based system according to their preference. There still exist several important issues to be discussed in this research area such as the definition of interpretability, the formulation of interpretability measures, the visualization of tradeoff relations, and the interpretability of the explanation of fuzzy reasoning results. In this paper, we discuss the ability of EMO algorithms as multiobjective optimizers to search for Pareto optimal or near Pareto optimal fuzzy rule-based systems. More specifically, we examine whether EMO algorithms can find non-dominated fuzzy rule-based systems that approximate the entire Pareto fronts of multiobjective fuzzy system design problems.
{"title":"Search ability of evolutionary multiobjective optimization algorithms for multiobjective fuzzy genetics-based machine learning","authors":"H. Ishibuchi, Yusuke Nakashima, Y. Nojima","doi":"10.1109/FUZZY.2009.5277370","DOIUrl":"https://doi.org/10.1109/FUZZY.2009.5277370","url":null,"abstract":"Recently evolutionary multiobjective optimization (EMO) algorithms have been actively used for the design of accurate and interpretable fuzzy rule-based systems. This research area is often referred to as multiobjective genetic fuzzy systems where EMO algorithms are used to search for a number of non-dominated fuzzy rule-based systems with respect to their accuracy and interpretability. The main advantage of the use of EMO algorithms for fuzzy system design over single-objective optimizers is that multiple alternative fuzzy rule-based systems with different accuracy-interpretability tradeoffs are obtained by their single run. The decision maker can choose a single fuzzy rule-based system according to their preference. There still exist several important issues to be discussed in this research area such as the definition of interpretability, the formulation of interpretability measures, the visualization of tradeoff relations, and the interpretability of the explanation of fuzzy reasoning results. In this paper, we discuss the ability of EMO algorithms as multiobjective optimizers to search for Pareto optimal or near Pareto optimal fuzzy rule-based systems. More specifically, we examine whether EMO algorithms can find non-dominated fuzzy rule-based systems that approximate the entire Pareto fronts of multiobjective fuzzy system design problems.","PeriodicalId":117895,"journal":{"name":"2009 IEEE International Conference on Fuzzy Systems","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2009-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133797751","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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