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2011
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Fuzzy Relational Dynamic System with Smooth Fuzzy Composition
Fuzzy Relational Dynamic System with Smooth Fuzzy Composition
en
en
Fuzzy relational models of functions have been developed in recent two decades which has led to fuzzy relational models of dynamic systems which we call fuzzy relational dynamic systems (FRDS). In this paper the effectiveness of smooth fuzzy relational compositions (FRC) in such dynamic models is studied after introducing a general framework for modeling of dynamic systems using FRDS, and so the smooth FRDS is developed. A modeling structure is presented in this regard as well as a related identification algorithm. Finally, the modeling capability of the proposed smooth FRDS is verified via some simulations on various benchmark problems and actual dynamic systems.
1
8
Arya
Aghili Ashtiani
Mohammad Bagher
Menhaj
Fuzzy relational dynamic system
smooth fuzzy relational composition
fuzzy relational modeling.
Article.1.pdf
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]
A Multi-objective Goal Programming Approach to a Fuzzy Transportation Problem the Case of a General Contractor Company
A Multi-objective Goal Programming Approach to a Fuzzy Transportation Problem the Case of a General Contractor Company
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en
In this paper, the author presented a transportation problem with Non-Linear constraints in which supplies and demands are trapezoidal fuzzy values and the objective function assumes multiple objectives. Then, Non-Linear constraints are linearized by defining and adding auxiliary constraints. Finally, the optimal solution of the problem is founded by solving the linear programming problem with fuzzy and crisp constraints and applying fuzzy programming technique. The method proposed to solve this problem is illustrated through numerical examples. Multi-objective goal programming methodology is used for numerical examples. The results of this research were developed and used as one of the Decision Support System models in Logistics Department of Kayson Co.
9
19
Hossein
Abdollahnejad Barough
Fuzzy Transportation Problem
Non-Linear Programming
Fuzzy Constraints
Multi-objective Goal Programming
Linear Programming.
Article.2.pdf
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]
The Progressions of Fuzzy Numbers and Their Features
The Progressions of Fuzzy Numbers and Their Features
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en
In this paper a certain progression of fuzzy numbers is introduced that called geometrical- arithmetical progression of fuzzy numbers. We study the features of this kind of progression. We also introduced the geometrical progression of fuzzy numbers and the arithmetical progression of fuzzy numbers through using geometrical-arithmetical progression. If fuzzy numbers change into crisp numbers, then geometrical- arithmetical progression of crisp numbers is obtained which is more general than geometrical and arithmetical progression and in special cases, change into them. There are some numerical examples at the end.
20
26
Ezzatallah
Baloui Jamkhaneh
Ali
Shabani
Roghayeh
Zareei Jamkhaneh
Mohamad
Khaleghi
Fuzzy number
arithmetical progression
geometrical progression
geometrical- arithmetical progression.
Article.3.pdf
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E. Pasha, A. Saiedifar, B. Asady, The percentiles of fuzzy numbers and their applications, Iran. J. Fuzzy Syst., 6 (2009), 27-44
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]
Optimal Fuzzy Synchronization of Generalized Lorenz Chaotic Systems
Optimal Fuzzy Synchronization of Generalized Lorenz Chaotic Systems
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en
In this article two identical generalized Lorenz systems have been synchronized by a fuzzy controller based on mamdani approach and stability of the proposed scheme has been established by the Lyapunov stability theorem. Controller parameters have been optimized by the genetic algorithm. Effectiveness of proposed method has been demonstrated through computer simulation.
27
36
Davood
Babaei Pourkargar
Mohammad
Shahrokhi
Fuzzy controller
Synchronization
Generalized Lorenz system
Genetic Algorithm.
Article.4.pdf
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]
Rule Extraction for Blood Donators with Fuzzy Sequential Pattern Mining
Rule Extraction for Blood Donators with Fuzzy Sequential Pattern Mining
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en
Sequential pattern mining is to discover all sub-sequences that are frequent. The classical sequential pattern mining algorithms do not allow processing of numerical data and require converting these data into a binary representation, which necessarily leads to a loss of information. Fuzzy sets are used to overcome this problem and fuzzy set based algorithms have been proposed to handle numerical data using intervals, particularly fuzzy intervals. In this paper, a fuzzy sequential pattern mining algorithm is applied to mine fuzzy sequential patterns from the Blood Transfusion Service Center data set. It helps to predict future patterns of blood donating behavior.
37
43
Fatemeh
Zabihi
Mojtaba
Ramezan
Mir Mohsen
Pedram
Azizollah
Memariani
Sequence
Fuzzy Sequential Pattern Mining
Fuzzy rules.
Article.5.pdf
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F. Zabihi, M. M. Pedram, A. Memariani, Fuzzy Constrained Sequential Pattern Mining, A dissertation submitted in partial fulfillment of the requirements for the degree of Master of Industrial Engineering (Industrial Engineering) in the Tarbiat Moallem University, Tehran (2008)
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]
Fuzzy Time-delay Dynamical Systems
Fuzzy Time-delay Dynamical Systems
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This paper investigates the first order linear fuzzy time-delay dynamical systems. We use a complex number representation of the \(\alpha\)-level sets of the fuzzy time-delay system, and obtain the solution by applying a Runge-Kutta method. Several examples are considered to show the convergence and accuracy of the proposed method. We finally present some conclusions and new directions for further research in this area.
44
53
M. H.
Farahi
S.
Barati
Time-delay dynamical systems
fuzzy differential equations
fuzzy matrices
Runge-Kutta methods.
Article.6.pdf
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]
A Genetic Fuzzy Approach for Building of Marketing Intelligence Systems for Consumer Behavior Modelling
A Genetic Fuzzy Approach for Building of Marketing Intelligence Systems for Consumer Behavior Modelling
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en
In this paper we debate on the causes of dissatisfactory of academic studies of marketing models. Next, we present a more complete methodology for knowledge discovery in data bases to be used in marketing causal modelling as a decision support tool in marketing management .This methodology is based on genetic fuzzy systems, a specific hybridization of artificial intelligence methods, that is proper for the problems we offer. Marketing intelligence system is called knowledge based marketing management support systems that is an avant-garde evolution in the use of KDD methods based on intelligence systems like this in our paper. The KDD process creates some basic questions for the professionals in this case that are completely discussed and solved next .After the theoretical presentation, this methodology is experimented on a consumer modelling application in interactive computer-mediated environments.
54
64
Hamid Reza
Feili
Reyhaneh
Bijari
Sepideh
Zohoori
Genetic Fuzzy Systems
Marketing Modelling
Consumer Behavior
Knowledge Discovery
Decision Support Systems.
Article.7.pdf
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F. Akhter, D. Hobbs, Z. Maamar, A fuzzy logic-based system for assessing the level of business-to-consumer (B2C) trust in electronic commerce, Expert Syst. Appl., 28 (2005), 623-628
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M. Beynon, B. Curry, P. Morgan, Knowledge discovery in marketing: An approach through rough set theory, Eur. J. Mark., 35 (2001), 915-935
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O. Cordon, F. Herrera, F. Hoffmann, L. Magdalena, Genetic fuzzy systems: Evolutionary tuning and learning of fuzzy knowledge bases, World Scientific, Singapore (2001)
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]
A Fuzzy Optimization Model for Supply Chain Production Planning with Total Aspect of Decision Making
A Fuzzy Optimization Model for Supply Chain Production Planning with Total Aspect of Decision Making
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en
This paper models supply chain uncertainties by fuzzy sets and develops a fuzzy linear programming model for tactical supply chain planning in a multi-echelon, multi-product, multi-stage with different methods of manufacturing in each stage, multi-distribution centre and multi-period supply chain network. In this approach, the demand, process and supply uncertainties are jointly considered. The aim is to achieve the best use of the available resources and the best method of manufacturing at each stage for a product along the time horizon so that customer demands are met at a minimum cost. The fuzzy model provides the decision maker with alternative decision plans with different degrees of satisfaction.
65
80
Hamid Reza
Feili
Mojdeh
Hassanzadeh Khoshdooni
Supply Chain Management
Supply Chain Planning
Fuzzy Sets
Uncertainty Modeling.
Article.8.pdf
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]
Filteristic Soft BCK-Algebras
Filteristic Soft BCK-Algebras
en
en
We study the soft sets applied on the structure of filters of BCK-algebras. A connection between soft BCK-algebras and filteristic soft BCK-algebras is given also. Finally, important operations such as intersection, union, “AND”, “OR”, and subset operations of soft filters and filteristic soft BCK-algebras are investigated.
81
87
R.
Ameri
H.
Hedayati
E.
Ghasemian
BCK-algebras
filteristic soft BCK-algebras
BCK and BCI logics
BCI-algebras
Article.9.pdf
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H. Aktaş, N. Çağman, Soft sets and groups, Inform. Sci., 177 (2007), 2726-2735
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D. Chen, E. C. C. Tsang, D. S. Yeung, X. Wang, The parametrization reduction of soft sets and its applications, Comput. Math. Appl., 49 (2005), 757-763
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Y. B. Jun, Soft BCK/BCI-algebras, Comput. Math. Appl., 56 (2008), 1408-1413
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Y. B. Jun, C. H. Park, A pplications of soft sets in ideal theory of BCK-BCI-algebras, Inform. Sci., 178 (2008), 1466-2475
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L. A. Zadeh, Toward a generalized theory of uncertainty (GTU)--an outline, Inform. Sci., 172 (2005), 1-40
]
A New Method for Modeling System Dynamics by Fuzzy Logic Modeling of Research and Development in the National System of Innovation
A New Method for Modeling System Dynamics by Fuzzy Logic Modeling of Research and Development in the National System of Innovation
en
en
System Dynamics (SD) is an effective method for studying dynamic conditions and changes in complex systems. It has been used in domain of social, economic and human activities which deal with vague and inaccurate variables. In this paper, a new dynamic model of real world systems is designed based on the concept of system dynamic approach. Then relations among the variables in the model are defined as fuzzy if-then rules by using fuzzy logic method. For analyzing the model accurately and avoiding the extent of ambiguities, Fuzzy Inference System (FIS) will be designed. For this purpose, cycle of creation and absorption of knowledge in a National Innovation System has been analyzed via SD methodology and FIS results.
88
99
Hassan
Youssefi
Vahid Saeid
Nahaei
Javad
Nematian
System Dynamics
Fuzzy Logic
Fuzzy Inference System
National Innovation Systems
If-Then rules
Article.10.pdf
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B. Jeng, J. Chen, T. Liang, Applying data mining to learn system dynamics in a biological model, International Journal of Expert Systems with Applications, 30 (2006), 50-58
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]
Numerical Taxonomy Analysis with Trapezoidal Fuzzy Data
Numerical Taxonomy Analysis with Trapezoidal Fuzzy Data
en
en
Numerical taxonomy analysis is one of the best method of grading, classifying and
comparing countries or different regions according to their development levels and
modernity, that it can be used for different grading too. In this paper, the numerical
taxonomy method with triangular fuzzy data that has been introduced by Mr.
mohammadi and his colleagues in 2010, is expended to the method of numerical
taxonomy with trapezoidal fuzzy data. So, if alternatives values, are place in diverse
indicators of triangular fuzzy values, the output of expanded method of this paper will
be the same as the numerical taxonomy method with triangular fuzzy data that has
been introduced by Mr. mohammadi and his colleagues.
100
110
Ali
Mohammadi
Javad
Shohani
Rajabali
Borzooei
numerical taxonomy
trapezoidal fuzzy number
development level.
Article.11.pdf
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[1]
K. Zeiari, Principles and methods of regional planning, Institute of Publications of Tehran University, Tehran (2009)
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F. Moaiery, Assigning strategy of kordestan province industrial development as respect performance view, , (), -
##[3]
A. Mohammadi, J. Shohani, H. Jahanshahi, Numerical taxonomy analysis with triangular fuzzy data, 10th conference of fuzzy systems (Iran), 2010 (2010), -
##[4]
A. R. Jamali, Public topology, Payame-Nour University, Tehran (2013)
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]
Supplier Selection Based on Supplier Risk: an ANP and Fuzzy TOPSIS Approach
Supplier Selection Based on Supplier Risk: an ANP and Fuzzy TOPSIS Approach
en
en
Typically, supplier selection constitutes one of the most important stages of supply chain management and a variety of basic and hybrid MCDM approaches have been deployed to provide this problem with well-suited solutions. This paper investigates a new novel approach for this problem based on ANP and fuzzy TOPSIS methods while it takes into account the risk factor solely regarding the decision maker’s venture strategy. In addition to an ANP model that determines the effects of decision criteria, in the proposed approach, a set of 5 risk categories has been deployed to affect the decision maker’s choice by normalizing the weights of risk criteria.
111
121
A.
Shemshadi
M.
Toreihi
H.
Shirazi
M. J.
Tarokh
Supplier selection
Supplier risk
Analytic network process
Fuzzy TOPSIS.
Article.12.pdf
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[1]
A. Amid, S. H. Ghodsypour, C. O'Brien, Fuzzy multiobjective linear model for supplier selection in a supply chain, Int. J. Prod. Econ., 104 (2006), 394-407
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AMR , AMR Research Report on Major Supply Chain Risk Factors, AMR Research Inc., (2007)
##[3]
L. De Boer, E. Labro, P. Morlacchi, A review of methods supporting supplier selection, European Journal of Purchasing & Supply Management, 7 (2001), 75-89
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F. E. Boran, S. Genç, M. Kurt, D. Akay, A multi-criteria intuitionistic fuzzy group decision making for supplier selection with TOPSIS method, Expert Syst. Appl., 36 (2009), 11363-11368
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F. T. S. Chan, N. Kumar, Global supplier development considering risk factors using fuzzy extended AHP based approach, The International Journal of Management Science, 35 (2007), 417-431
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B. Change, H. F. Hung, C. C. Lo, Supplier Selection Using Rough Set Theory, Proceedings of the IEEE IEEM, 2007 (2007), 1461-1465
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Z. H. Che, H. S. Wang, Supplier selection and supply quantity allocation of common and non-common parts with multiple criteria under multiple products, Computers & Industrial Engineering, 55 (2008), 110-133
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K. Chen, Z. Xuan, X. Shang, Selection of Suppliers based on BP Neutral Networks and Grey Correlation Analysis, International Joint Conf. on Artificial Intelligence, 2009 (2009), 268-271
##[11]
E. W. L. Cheng, H. Li, Application of ANP in process models: An example of strategic partnering, Building and Environment, 42 (2007), 278-287
##[12]
M. Dağdeviren, S. Yavuz, N. Kılınç, Weapon selection using the AHP and TOPSIS methods under fuzzy environment, Expert Syst. Appl., 36 (2009), 8143-8151
##[13]
A. Foroughi, M. Albin, M. Kocakulah, Perspectives on Global Supply Chain Supply-Side Risk Management, Proceedings of PICMET, 2006 (2006), 2732-2740
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R. S. Gaonkar, N. Viswanadham, Analytical framework for the Management of Risk in Supply Chains, IEEE Transactions on Automation Science and Engineering, 4 (2007), 265-273
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C. Gencer, D. Gürpinar, Analytic network process in supplier selection: A case study in an electronic firm, Applied mathematical modeling, 31 (2007), 2475-2486
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A. F. Guneri, A. Yucel, G. Ayyildiz, An integrated fuzzy-lp approach for a supplier selection problem in supply chain management, Expert Syst. Appl., 36 (2009), 9223-9228
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J. Hallikas, I. Karvonen, U. Pulkkinen, V. M. Virolainen, Risk management processes in supplier networks, Int. J. Prod. Econ., 90 (2004), 47-58
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J. Hallikas, K. Puumalainen, T. Vesterinen, V. M. Virolainen, Risk-based classification of supplier relationships, Journal of Purchasing and Supply Management, 11 (2005), 72-82
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C. Harland, R. Brenchley, H. Walker, Risk in supply networks, Journal of Purchasing & Supply Management, 9 (2003), 51-62
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W. Ho, X. Xu, P. K. Dey, Multi-criteria decision making approaches for supplier evaluation and selection: A literature review, Eur. J. Oper. Res., 202 (2010), 16-24
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Z. Huixia, Y. Tao, Supplier Selection Model based on the Grey System Theory, The International Conf. on Risk Management & Engineering Management, 2008 (2008), 100-104
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M. H. Hwang, H. Rau, Development of a Supplier Selection Approach from the Viewpoint of the Entire Supply Chain, Proceedings of the Seventh International Conference on Machine Learning and Cybernetics, 2008 (2008), 3938-3945
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K. Zhao, X. Yu, D. Wang, Study on CBR Supplier Selection System Based On Data Mining for Oil Enterprises, International Symposium on Information Engineering and Electronic Commerce, 2009 (2009), 555-559
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A. H. I. Lee , A fuzzy supplier selection model with the consideration of benefits, opportunities, costs and risks, Expert Syst. Appl., 36 (2009), 2879-2893
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C. C. Lee, C. Ou-Yang, A neural networks approach for forecasting the supplier’s bid prices in supplier selection negotiation process, Expert Syst. Appl., 36 (2009), 2961-2970
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R. R. Levary, Using the analytic hierarchy process to rank foreign suppliers based on supplier risks, Computers & Industrial Engineering, 55 (2008), 535-542
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C. H. Li, Y. H. Sun, Y. W. Du, An ANP with Benefits, Opportunities, Costs and Risks for Selecting Suppliers, 4th International Conference on Wireless Communications, Networking and Mobile Computing, 2008 (2008), 1-4
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S. Önüt, S. S. Kara, E. Işik, Long term supplier selection using a combined fuzzy MCDM approach: A case study for a telecommunication company, Expert Syst. Appl., 36 (2009), 3887-3895
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A. Shemshadi, J. Soroor. M. J. Tarokh, Implementing a Multi-Agent System for the Real-time Coordination of a Typical Supply Chain Based on the JADE Technology, 3rd IEEE SMC International Conference on System of Systems Engineering (SoSE'08), 2008 (2008), 1-6
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C. Tang, B. Tomlin, The power of flexibility for mitigating supply chain risks, Int. J. Prod. Econ., 116 (2008), 12-27
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J. H. Thun, D. Hoenig, An empirical analysis of supply chain risk management in the German automotive industry, Int. J. Prod. Econ., 131 (2009), 242-249
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]
Single Machine Scheduling with Fuzzy Preemption Penalties
Single Machine Scheduling with Fuzzy Preemption Penalties
en
en
In preemptive scheduling problems, processing of a job can be temporarily interrupted, and resumed at a later time. Conventionally, in the literature on preemptive scheduling, preempted jobs can simply be resumed from the point at which preemption occurred or restart from the beginning. However, this situation may not always be true in practice. It is likely that, in some cases, an imprecise or fuzzy part of jobs processing must be repeated, i.e., a fuzzy time penalty must be incurred. In this paper, we consider the single-machine scheduling problem of minimizing the total flow time subject to job release dates and fuzzy preemption penalties. We present a heuristic algorithm and validate it using some numerical examples.
122
129
Mehdi
Heydari
Emran
Mohammadi
Scheduling
Fuzzy preemption penalty
Membership function
Flow time
Article.13.pdf
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[1]
H. F. Ting, A near optimal scheduler for on-demand data broadcasts, In Italian Conference on Algorithms and Complexity, 2006 (2006), 163-174
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F. Zheng, S. P. Y. Fung, W. T. Chan, F. Y. L. Chin, C. K. Poon, P. W. H. Wong, Improved on-line broadcast scheduling with deadlines, Proc.of 12th International Computing and Combinatorics Conference, 2006 (2006), 320-329
##[3]
M. Heydari, S. J. Sadjadi, E. Mohammadi, Minimizing total flow time subject to preemption penalties in online scheduling, Int. J. Adv. Manuf. Technol., 47 (2009), 227-236
##[4]
Z. Liu, T. C. E. Cheng, Minimizing total completion time subject to job release dates and preemption penalties, J. Sched., 7 (2004), 313-327
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, The concept of a linguistic variable and its application approximate reasoning, Information Sci., 8 (1975), 199-249
]
\((\bar{\varepsilon},\overline{\varepsilon\wedge q_k})\)-fuzzy Subalgebras in BCK/BCI-algebras
\((\bar{\varepsilon},\overline{\varepsilon\wedge q_k})\)-fuzzy Subalgebras in BCK/BCI-algebras
en
en
In this paper, the notion of not quasi-coincidence \(\bar{q}\) of a
fuzzy point with a fuzzy set is considered. We introduce the
notion of \((\bar{\varepsilon},\overline{\varepsilon\wedge q_k})\)-fuzzy \((\bar{\varepsilon},\overline{ q_k})\)-fuzzy subalgebra in a
BCK/BCI-algebra X and several properties are investigated.
Specially, we show that under certain conditions an
\((\bar{\varepsilon},\overline{\varepsilon\wedge q_k})\) -fuzzy subalgebra can be expressed such that
consist of a union of two proper non-equivalent \((\bar{\varepsilon},\overline{\varepsilon\wedge q_k})\) -
fuzzy subalgebras.
130
140
Reza
Ameri
Hossein
Hedayati
Morteza
Norouzi
BCK/BCI-algebra
\((\bar{\varepsilon}،\bar{ q_k})\)-fuzzy subalgebra
\((\bar{\varepsilon}،\overline{\varepsilon\wedge q_k})\)-fuzzy subalgebra
\((\overline{\varepsilon\wedge q_k})\)-level subalgebra.
Article.14.pdf
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An Intelligent System for Parking Trailer in Presence of Fixed and Moving Obstacles Using Reinforcement Learning and Fuzzy Logic
An Intelligent System for Parking Trailer in Presence of Fixed and Moving Obstacles Using Reinforcement Learning and Fuzzy Logic
en
en
In examples of reinforcement learning where state space is continuous, it seems impossible to use reference tables to store value-action .In these problems a method is required for value estimation for each state-action pair .The inputs to this estimation system are (characteristics of)
state variables which reflect the status of agent in the environment .The system can be either linear of nonlinear .For each member in set of actions of an agent, there exists an estimation system which determines state value for the action .
On the other hand, in most real world problems, just as the state space is continuous, so is the action space for an agent .In these cases, fuzzy systems may provide a useful solution in selection of final action from action space .In this paper we intend to combine reinforcement learning algorithm with fuzzified actions and state space along with a linear estimation system into an intelligent systems for parking Trailers in cases where both state and action spaces are continuous .Finally, the successful performance of the proposed algorithm is shown through simulations on trailer parking problem .
141
149
M.
Sharafi
A.
Zare
A. V.
Kamyad
Reinforcement Learning
Fuzzy Systems
Trailer Parking Problem
SARSA Algorithm.
Article.15.pdf
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]
Introducing a New Method to Expand TOPSIS Decision Making Model to Fuzzy TOPSIS
Introducing a New Method to Expand TOPSIS Decision Making Model to Fuzzy TOPSIS
en
en
Fuzzy TOPSIS is one of the various models of multiple attributes decision making with
fuzzy values that so far diverse models have been introduced for it. In this paper,
according to these models, a new method is presented for fuzzy TOPSIS with
triangular fuzzy data. So, it has better and more accurate outputs in comparison with
previous methods. At last, we solve a fuzzy multiple attributes decision making
problem to demonstrate the proposed method.
150
159
Ali
Mohammadi
Abolfazl
Mohammadi
Hossain
Aryaeefar
fuzzy TOPSIS
fuzzy number
linguistic variables
triangular fuzzy number.
Article.16.pdf
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[1]
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]
New Perspective to ERP Critical Success Factors Priorities and Causal Relations under Fuzzy Environment
New Perspective to ERP Critical Success Factors Priorities and Causal Relations under Fuzzy Environment
en
en
Enterprise Resource Planning (ERP), systems are high technical cross-functional information systems that designed to improve organizational performance and competitiveness by streamlining business processes and eliminating duplication of works and data. Regarding the fact that ERP systems have a tremendous advantage for organizations but the implementation of an ERP is not straightforward and it involves significant risks. Several studies have conducted to identify the critical success factors (CSFs) in the ERP implementation process. However, most of those studies are lacking in systematic efforts to classify and evaluating CSFs. This study is motivated by a lack of theoretically research in the classification and evaluating CSFs by considering the causal relationship among CSFs that are affected the successful implementation of ERP systems. To achieve this aim Decision Making Trial and Evaluation Laboratory (DEMATEL) and Analytical Network Process (ANP) is applied. The proposed methodology implemented in the biggest refrigerator production company in Iran.
160
170
Mohsen Sadegh
Amalnick
Ayyub
Ansarinejad
Sina-Miri
Nargesi
Shakib
Taheri
ERP Critical Success Factors
Fuzzy DEMATEL
Fuzzy ANP
CFCS Defuzzification Method.
Article.17.pdf
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[1]
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V. Morabito, S. Pace, P. Previtali, ERP Marketing and Italian SMEs, European Management Journal, 23 (2005), 590-598
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]
The Study of Airline Service Quality in the Qeshm Free Zone by Fuzzy Logic
The Study of Airline Service Quality in the Qeshm Free Zone by Fuzzy Logic
en
en
This paper applies the fuzzy set theory for evaluating service quality of three airlines are active in Qeshm free zone in Iran via customer survey. Service quality is a composite of various attributes among them many intangible attributes are difficult to measure. So we invite fuzzy set theory to reflect the inherent subjectiveness and resolve the ambiguity of concepts that are associated with human beings'subjective judgments vaguely measured with linguistic terms. By applying AHP in obtaining criteria weight and TOPSIS in ranking, we find the relative ranking position of each airline and provide an adequate alternative to performance evaluation of airline services which usually involve subjective judgments of qualitative attributes.
171
183
Nahid
Moones Toosi
Reza
Ahmadi Kohanali
AHP
TOPSIS
Airline
Service quality
Article.18.pdf
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Fuzzy Cost Analysis in a Fuzzy Transportation System a Study of the Supply Chain Management in a General Contractor Company
Fuzzy Cost Analysis in a Fuzzy Transportation System a Study of the Supply Chain Management in a General Contractor Company
en
en
Transportation models play an important role in logistics and supply chain management for reducing cost and improving services. In this paper, the author presented a fuzzy transportation problem, in which the cost coefficients and supply and demand quantities are fuzzy numbers. The problem is solved in two stages. First, calculating the maximum satisfactory level and achieving balances between fuzzy supplies and demands. Second, the problem is solved by considering the unit of transportation costs and optimal solutions which are connected with fuzzy quantities’ satisfactory level are founded. The author used two different satisfactory levels for the problem: The transportation costs breaking points \((\gamma_p)\) and the values that have violated positive condition of optimal solutions in the intervals of \([\gamma_{p-1},\gamma_p]\). A new method is proposed in this paper to find optimal solutions. The proposed method is then illustrated through a numerical example.
184
194
Hossein
Abdollahnejad Barough
Fuzzy Transportation Problem
Supply Chain Management
Fuzzy Cost Analysis
Linear Programming.
Article.19.pdf
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An Appraisal and Ranking of the Effective Factors on Performance of Branches of Melli Bank of Iran in Mazandaran Province by Using of Ahp-fuzzy Technique
An Appraisal and Ranking of the Effective Factors on Performance of Branches of Melli Bank of Iran in Mazandaran Province by Using of Ahp-fuzzy Technique
en
en
The purpose of this research is appraisal and ranking of effective factors on performance of branches of melli bank of Iran in mazandaran province by using of AHP technique. as for literature review of organizational performance, needed data was collected by use of instruments with documents, interviews and questionnaire, with non experimental research methodology, by considerations experts' viewpoint in group AHP technique for priority. the results of this research to imply between 4 criteria, asset, management, personnel, customers and and between 27 selector switch, rate of asset absorption, profitability, decrease the delayed demanding, expertise, trust and respect to the personnel, have highest priority relation to other alternatives.
195
207
A.
Sorayaei
S. A.
Sajjadi Amiri
S. M.
Sajjadi Amiri
appraisal
ranking
performance
branches of Melli Bank of Iran
FUZZY-Analytical Hierarchy Process (AHP-FUZZY).
Article.20.pdf
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