Abstract: A novel concept to balance and tradeoff between
make-to-stock and make-to-order has been hybrid MTS/MTO production context. One of the most important decisions involved in
the hybrid MTS/MTO environment is determining whether a product
is manufactured to stock, to order, or hybrid MTS/MTO strategy. In this paper, a model based on analytic network process is developed to tackle the addressed decision. Since the regarded decision deals with
the uncertainty and ambiguity of data as well as experts- and
managers- linguistic judgments, the proposed model is equipped with
fuzzy sets theory. An important attribute of the model is its generality due to diverse decision factors which are elicited from the
literature and developed by the authors. Finally, the model is validated by applying to a real case study to reveal how the proposed
model can actually be implemented.
Abstract: The primary objective of the paper is to propose a new method for solving assignment problem under uncertain situation. In the classical assignment problem (AP), zpqdenotes the cost for assigning the qth job to the pth person which is deterministic in nature. Here in some uncertain situation, we have assigned a cost in the form of composite relative degree Fpq instead of and this replaced cost is in the maximization form. In this paper, it has been solved and validated by the two proposed algorithms, a new mathematical formulation of IVIF assignment problem has been presented where the cost has been considered to be an IVIFN and the membership of elements in the set can be explained by positive and negative evidences. To determine the composite relative degree of similarity of IVIFS the concept of similarity measure and the score function is used for validating the solution which is obtained by Composite relative similarity degree method. Further, hypothetical numeric illusion is conducted to clarify the method’s effectiveness and feasibility developed in the study. Finally, conclusion and suggestion for future work are also proposed.
Abstract: In conventional reliability assessment, the reliability data of system components are treated as crisp values. The collected data have some uncertainties due to errors by human beings/machines or any other sources. These uncertainty factors will limit the understanding of system component failure due to the reason of incomplete data. In these situations, we need to generalize classical methods to fuzzy environment for studying and analyzing the systems of interest. Fuzzy set theory has been proposed to handle such vagueness by generalizing the notion of membership in a set. Essentially, in a Fuzzy Set (FS) each element is associated with a point-value selected from the unit interval [0, 1], which is termed as the grade of membership in the set. A Vague Set (VS), as well as an Intuitionistic Fuzzy Set (IFS), is a further generalization of an FS. Instead of using point-based membership as in FS, interval-based membership is used in VS. The interval-based membership in VS is more expressive in capturing vagueness of data. In the present paper, vague set theory coupled with conventional Lambda-Tau method is presented for reliability analysis of repairable systems. The methodology uses Petri nets (PN) to model the system instead of fault tree because it allows efficient simultaneous generation of minimal cuts and path sets. The presented method is illustrated with the press unit of the paper mill.
Abstract: This paper focuses on creating a component model of information system under uncertainty. The paper identifies problem in current approach of component modeling and proposes fuzzy tool, which will work with vague customer requirements and propose components of the resulting component model. The proposed tool is verified on specific information system and results are shown in paper. After finding suitable sub-components of the resulting component model, the component model is visualised by tool.
Abstract: This paper addresses one of the most important issues
have been considered in hybrid MTS/MTO production environments. To cope with the problem, a mathematical programming model is
applied from a tactical point of view. The model is converted to a fuzzy goal programming model, because a degree of uncertainty is involved in hybrid MTS/MTO context. Finally, application of the
proposed model in an industrial center is reported and the results prove the validity of the model.
Abstract: In this paper, a method for deriving a group priority vector in the Fuzzy Analytic Network Process (FANP) is proposed. By introducing importance weights of multiple decision makers (DMs) based on their experiences, the Fuzzy Preferences Programming Method (FPP) is extended to a fuzzy group prioritization problem in the FANP. Additionally, fuzzy pair-wise comparison judgments are presented rather than exact numerical assessments in order to model the uncertainty and imprecision in the DMs- judgments and then transform the fuzzy group prioritization problem into a fuzzy non-linear programming optimization problem which maximize the group satisfaction. Unlike the known fuzzy prioritization techniques, the new method proposed in this paper can easily derive crisp weights from incomplete and inconsistency fuzzy set of comparison judgments and does not require additional aggregation producers. Detailed numerical examples are used to illustrate the implement of our approach and compare with the latest fuzzy prioritization method.
Abstract: In this note the notion of interval-valued fuzzy BG-algebras (briefly, i-v fuzzy BG-algebras), the level and strong level BG-subalgebra is introduced. Then we state and prove some theorems which determine the relationship between these notions and BG-subalgebras. The images and inverse images of i-v fuzzy BG-subalgebras are defined, and how the homomorphic images and inverse images of i-v fuzzy BG-subalgebra becomes i-v fuzzy BG-algebras are studied.
Abstract: The fuzzy set theory has been applied in many fields,
such as operations research, control theory, and management
sciences, etc. In particular, an application of this theory in decision
making problems is linear programming problems with fuzzy
numbers. In this study, we present a new method for solving fuzzy
number linear programming problems, by use of linear ranking
function. In fact, our method is similar to simplex method that was
used for solving linear programming problems in crisp environment
before.
Abstract: This paper is to develop a fuzzy net present value (FNPV) method by taking vague cash flow and imprecise required rate of return into account for evaluating the value of the Build-Operate-Transfer (BOT) sport facilities. In order to clearly manifest a more realistic capital budgeting model based on the classical net present value (NPV) method, some uncertain financial elements in NPV formula will be fuzzified as triangular fuzzy numbers. Through the conscientious manipulation of fuzzy set theory, we will find that the proposed FNPV model is a more explicit extension of classical (crisp) model and could be more practicable for the financial managers to capture the essence of capital budgeting of sport facilities than non-fuzzy model.
Abstract: This paper applies fuzzy AHP to evaluate the service
quality of online auction. Service quality is a composition of various
criteria. Among them many intangible attributes are difficult to
measure. This characteristic introduces the obstacles for respondents
on reply in the survey. So as to overcome this problem, we invite
fuzzy set theory into the measurement of performance and use AHP in
obtaining criteria. We found the most concerned dimension of service
quality is Transaction Safety Mechanism and the least is Charge Item.
Other criteria such as information security, accuracy and information
are too vital.
Abstract: In this paper the concept of Q-fuzzification of ideals of Γ-semigroups has been introduced and some important properties have been investigated. A characterization of regular Γ-semigroup in terms of Q-fuzzy ideals has been obtained. Operator semigroups of a Γ-semigroup has been made to work by obtaining various relationships between Q-fuzzy ideals of a Γ-semigroup and that of its operator semigroups.
Abstract: Imperfect knowledge cannot be avoided all the time. Imperfections may have several forms; uncertainties, imprecision and incompleteness. When we look to classification of methods for the management of imperfect knowledge we see fuzzy set-based techniques. The choice of a method to process data is linked to the choice of knowledge representation, which can be numerical, symbolic, logical or semantic and it depends on the nature of the problem to be solved for example decision support, which will be mentioned in our study. Fuzzy Logic is used for its ability to manage imprecise knowledge, but it can take advantage of the ability of neural networks to learn coefficients or functions. Such an association of methods is typical of so-called soft computing. In this study a new method was used for the management of imprecision for collected knowledge which related to economic analysis of construction industry in Turkey. Because of sudden changes occurring in economic factors decrease competition strength of construction companies. The better evaluation of these changes in economical factors in view of construction industry will made positive influence on company-s decisions which are dealing construction.
Abstract: In this paper, The T-G-action topology on a set acted
on by a fuzzy T-neighborhood (T-neighborhood, for short) group is
defined as a final T-neighborhood topology with respect to a set of
maps. We mainly prove that this topology is a T-regular Tneighborhood
topology.
Abstract: Morphological operators transform the original image
into another image through the interaction with the other image of
certain shape and size which is known as the structure element.
Mathematical morphology provides a systematic approach to analyze
the geometric characteristics of signals or images, and has been
applied widely too many applications such as edge detection,
objection segmentation, noise suppression and so on. Fuzzy
Mathematical Morphology aims to extend the binary morphological
operators to grey-level images. In order to define the basic
morphological operations such as fuzzy erosion, dilation, opening
and closing, a general method based upon fuzzy implication and
inclusion grade operators is introduced. The fuzzy morphological
operations extend the ordinary morphological operations by using
fuzzy sets where for fuzzy sets, the union operation is replaced by a
maximum operation, and the intersection operation is replaced by a
minimum operation.
In this work, it consists of two articles. In the first one, fuzzy set
theory, fuzzy Mathematical morphology which is based on fuzzy
logic and fuzzy set theory; fuzzy Mathematical operations and their
properties will be studied in details. As a second part, the application
of fuzziness in Mathematical morphology in practical work such as
image processing will be discussed with the illustration problems.
Abstract: Intuitionistic fuzzy sets as proposed by Atanassov,
have gained much attention from past and latter researchers for
applications in various fields. Similarity measures between
intuitionistic fuzzy sets were developed afterwards. However, it does
not cater the conflicting behavior of each element evaluated. We
therefore made some modification to the similarity measure of IFS
by considering conflicting concept to the model. In this paper, we
concentrate on Zhang and Fu-s similarity measures for IFSs and
some examples are given to validate these similarity measures. A
simple modification to Zhang and Fu-s similarity measures of IFSs
was proposed to find the best result according to the use of degree of
indeterminacy. Finally, we mark up with the application to real
decision making problems.
Abstract: Sensors possess several properties of physical
measures. Whether devices that convert a sensed signal into an
electrical signal, chemical sensors and biosensors, thus all these
sensors can be considered as an interface between the physical and
electrical equipment. The problem is the analysis of the multitudes of
saved settings as input variables. However, they do not all have the
same level of influence on the outputs. In order to identify the most
sensitive parameters, those that can guide users in gathering
information on the ground and in the process of model calibration
and sensitivity analysis for the effect of each change made.
Mathematical models used for processing become very complex.
In this paper a fuzzy rule-based system is proposed as a solution
for this problem. The system collects the available signals
information from sensors. Moreover, the system allows the study of
the influence of the various factors that take part in the decision
system. Since its inception fuzzy set theory has been regarded as a
formalism suitable to deal with the imprecision intrinsic to many
problems. At the same time, fuzzy sets allow to use symbolic models.
In this study an example was applied for resolving variety of
physiological parameters that define human health state. The
application system was done for medical diagnosis help. The inputs
are the signals expressed the cardiovascular system parameters, blood
pressure, Respiratory system paramsystem was done, it will be able
to predict the state of patient according any input values.
Abstract: Due to the deregulation of the Electric Supply
Industry and the resulting emergence of electricity market, the
volumes of power purchases are on the rise all over the world. In a
bid to meet the customer-s demand in a reliable and yet economic
manner, utilities purchase power from the energy market over and
above its own production. This paper aims at developing an optimal
power purchase model with two objectives viz economy and
environment ,taking various functional operating constraints such as
branch flow limits, load bus voltage magnitudes limits, unit capacity
constraints and security constraints into consideration.The price of
purchased power being an uncertain variable is modeled using fuzzy
logic. DEMO (Differential Evolution For Multi-objective
Optimization) is used to obtain the pareto-optimal solution set of the
multi-objective problem formulated. Fuzzy set theory has been
employed to extract the best compromise non-dominated solution.
The results obtained on IEEE 30 bus system are presented and
compared with that of NSGAII.
Abstract: This article outlines conceptualization and
implementation of an intelligent system capable of extracting
knowledge from databases. Use of hybridized features of both the
Rough and Fuzzy Set theory render the developed system flexibility
in dealing with discreet as well as continuous datasets. A raw data set
provided to the system, is initially transformed in a computer legible
format followed by pruning of the data set. The refined data set is
then processed through various Rough Set operators which enable
discovery of parameter relationships and interdependencies. The
discovered knowledge is automatically transformed into a rule base
expressed in Fuzzy terms. Two exemplary cancer repository datasets
(for Breast and Lung Cancer) have been used to test and implement
the proposed framework.
Abstract: Quality control charts indicate out of control
conditions if any nonrandom pattern of the points is observed or any
point is plotted beyond the control limits. Nonrandom patterns of
Shewhart control charts are tested with sensitizing rules. When the
processes are defined with fuzzy set theory, traditional sensitizing
rules are insufficient for defining all out of control conditions. This is
due to the fact that fuzzy numbers increase the number of out of
control conditions. The purpose of the study is to develop a set of
fuzzy sensitizing rules, which increase the flexibility and sensitivity
of fuzzy control charts. Fuzzy sensitizing rules simplify the
identification of out of control situations that results in a decrease in
the calculation time and number of evaluations in fuzzy control chart
approach.
Abstract: In this article, by using fuzzy AHP and TOPSIS
technique we propose a new method for project selection problem.
After reviewing four common methods of comparing alternatives
investment (net present value, rate of return, benefit cost analysis
and payback period) we use them as criteria in AHP tree. In this
methodology by utilizing improved Analytical Hierarchy Process
by Fuzzy set theory, first we try to calculate weight of each
criterion. Then by implementing TOPSIS algorithm, assessment of
projects has been done. Obtained results have been tested in a
numerical example.