Abstract: Several researchers have proposed methods about
combination of Genetic Algorithm (GA) and Fuzzy Logic (the use of
GA to obtain fuzzy rules and application of fuzzy logic in
optimization of GA). In this paper, we suggest a new method in
which fuzzy decision making is used to improve the performance of
genetic algorithm. In the suggested method, we determine the alleles
that enhance the fitness of chromosomes and try to insert them to the
next generation.
In this algorithm we try to present an innovative vaccination in the
process of reproduction in genetic algorithm, with considering the
trade off between exploration and exploitation.
Abstract: The decision of information technology (IT) outsourcing requires close attention to the evaluation of supplier selection process because the selection decision involves conflicting multiple criteria and is replete with complex decision making problems. Selecting the most appropriate suppliers is considered an important strategic decision that may impact the performance of outsourcing engagements. The objective of this paper is to aid decision makers to evaluate and assess possible IT outsourcing suppliers. An axiomatic design based fuzzy group decision making is adopted to evaluate supplier alternatives. Finally, a case study is given to demonstrate the potential of the methodology. KeywordsIT outsourcing, Supplier selection, Multi-criteria decision making, Axiomatic design, Fuzzy logic.
Abstract: Ranking of fuzzy numbers play an important role in
decision making, optimization, forecasting etc. Fuzzy numbers must
be ranked before an action is taken by a decision maker. In this
paper, with the help of several counter examples it is proved that
ranking method proposed by Chen and Chen (Expert Systems with
Applications 36 (2009) 6833-6842) is incorrect. The main aim of this
paper is to propose a new approach for the ranking of generalized
trapezoidal fuzzy numbers. The main advantage of the proposed
approach is that the proposed approach provide the correct ordering
of generalized and normal trapezoidal fuzzy numbers and also the
proposed approach is very simple and easy to apply in the real life
problems. It is shown that proposed ranking function satisfies all
the reasonable properties of fuzzy quantities proposed by Wang and
Kerre (Fuzzy Sets and Systems 118 (2001) 375-385).
Abstract: The decision to recruit manpower in an organization
requires clear identification of the criteria (attributes) that distinguish
successful from unsuccessful performance. The choice of appropriate
attributes or criteria in different levels of hierarchy in an organization
is a multi-criteria decision problem and therefore multi-criteria
decision making (MCDM) techniques can be used for prioritization
of such attributes. Analytic Hierarchy Process (AHP) is one such
technique that is widely used for deciding among the complex criteria
structure in different levels. In real applications, conventional AHP
still cannot reflect the human thinking style as precise data
concerning human attributes are quite hard to be extracted. Fuzzy
logic offers a systematic base in dealing with situations, which are
ambiguous or not well defined. This study aims at defining a
methodology to improve the quality of prioritization of an
employee-s performance measurement attributes under fuzziness. To
do so, a methodology based on the Extent Fuzzy Analytic Hierarchy
Process is proposed. Within the model, four main attributes such as
Subject knowledge and achievements, Research aptitude, Personal
qualities and strengths and Management skills with their subattributes
are defined. The two approaches conventional AHP
approach and the Extent Fuzzy Analytic Hierarchy Process approach
have been compared on the same hierarchy structure and criteria set.
Abstract: In this paper, the potential use of an exponential
hidden Markov model to model a hidden pavement deterioration
process, i.e. one that is not directly measurable, is investigated. It is
assumed that the evolution of the physical condition, which is the
hidden process, and the evolution of the values of pavement distress
indicators, can be adequately described using discrete condition states
and modeled as a Markov processes. It is also assumed that condition
data can be collected by visual inspections over time and represented
continuously using an exponential distribution. The advantage of
using such a model in decision making process is illustrated through
an empirical study using real world data.
Abstract: Effective employee selection is a critical component
of a successful organization. Many important criteria for personnel
selection such as decision-making ability, adaptability, ambition, and
self-organization are naturally vague and imprecise to evaluate. The
rough sets theory (RST) as a new mathematical approach to
vagueness and uncertainty is a very well suited tool to deal with
qualitative data and various decision problems. This paper provides
conceptual, descriptive, and simulation results, concentrating chiefly
on human resources and personnel selection factors. The current
research derives certain decision rules which are able to facilitate
personnel selection and identifies several significant features based
on an empirical study conducted in an IT company in Iran.
Abstract: The values of managers and employees in organizations are phenomena that have captured the interest of researchers at large. Despite this attention, there continues to be a lack of agreement on what values are and how they influence individuals, or how they are constituted in individuals- mind. In this article content-based approach is presented as alternative reference frame for exploring values. In content-based approach human thinking in different contexts is set at the focal point. Differences in valuations can be explained through the information contents of mental representations. In addition to the information contents, attention is devoted to those cognitive processes through which mental representations of values are constructed. Such informational contents are in decisive role for understanding human behavior. By applying content-based analysis to an examination of values as mental representations, it is possible to reach a deeper to the motivational foundation of behaviors, such as decision making in organizational procedures, through understanding the structure and meanings of specific values at play.
Abstract: In Lebanon, public construction projects are awarded
to the contractor submitting the lowest bid price based on a
competitive bidding process. The contractor has to make a strategic
decision in choosing the appropriate bid price that will offer a
satisfactory profit with a greater probability to win. A simulation
model for bid price decision making based on the lowest bid price
evaluation is developed. The model, built using Crystal Ball decisionengineering
software, considers two main factors affecting the
bidding process: the number of qualified bidders and the size of the
project. The validity of the model is tested on twelve separate
projects. The study also shows how to use the model to conduct risk
analysis and help any specific contractor to decide on his bid price
with associated certainty level in a scientific method.
Abstract: Need for an appropriate system of evaluating students-
educational developments is a key problem to achieve the predefined
educational goals. Intensity of the related papers in the last years; that
tries to proof or disproof the necessity and adequacy of the students
assessment; is the corroborator of this matter. Some of these studies
tried to increase the precision of determining question weights in
scientific examinations. But in all of them there has been an attempt
to adjust the initial question weights while the accuracy and precision
of those initial question weights are still under question. Thus In
order to increase the precision of the assessment process of students-
educational development, the present study tries to propose a new
method for determining the initial question weights by considering
the factors of questions like: difficulty, importance and complexity;
and implementing a combined method of PROMETHEE and fuzzy
analytic network process using a data mining approach to improve
the model-s inputs. The result of the implemented case study proves
the development of performance and precision of the proposed
model.
Abstract: Environmental decision making, particularly about
hazardous waste management, is inherently exposed to a high
potential conflict, principally because of the trade-off between sociopolitical,
environmental, health and economic factors. The need to
plan complex contexts has led to an increasing request for decision
analytic techniques as support for the decision process. In this work,
alternative systems of asbestos-containing waste management
(ACW) in Puglia (Southern Italy) were explored by a multi-criteria
decision analysis. In particular, through Analytic Hierarchy Process
five alternatives management have been compared and ranked
according to their performance and efficiency, taking into account
environmental, health and socio-economic aspects. A separated
valuation has been performed for different temporal scale. For short
period results showed a narrow deviation between the disposal
alternatives “mono-material landfill in public quarry" and “dedicate
cells in existing landfill", with the best performance of the first one.
While for long period “treatment plant to eliminate hazard from
asbestos-containing waste" was prevalent, although high energy
demand required to achieve the change of crystalline structure. A
comparison with results from a participative approach in valuation
process might be considered as future development of method
application to ACW management.
Abstract: This article combines two techniques: data
envelopment analysis (DEA) and Factor analysis (FA) to data
reduction in decision making units (DMU). Data envelopment
analysis (DEA), a popular linear programming technique is useful to
rate comparatively operational efficiency of decision making units
(DMU) based on their deterministic (not necessarily stochastic)
input–output data and factor analysis techniques, have been proposed
as data reduction and classification technique, which can be applied
in data envelopment analysis (DEA) technique for reduction input –
output data. Numerical results reveal that the new approach shows a
good consistency in ranking with DEA.
Abstract: As a vital activity for companies, new product
development (NPD) is also a very risky process due to the high
uncertainty degree encountered at every development stage and the
inevitable dependence on how previous steps are successfully
accomplished. Hence, there is an apparent need to evaluate new
product initiatives systematically and make accurate decisions under
uncertainty. Another major concern is the time pressure to launch a
significant number of new products to preserve and increase the
competitive power of the company. In this work, we propose an
integrated decision-making framework based on neural networks and
fuzzy logic to make appropriate decisions and accelerate the
evaluation process. We are especially interested in the two initial
stages where new product ideas are selected (go/no go decision) and
the implementation order of the corresponding projects are
determined. We show that this two-staged intelligent approach allows
practitioners to roughly and quickly separate good and bad product
ideas by making use of previous experiences, and then, analyze a
more shortened list rigorously.
Abstract: Classification is an interesting problem in functional
data analysis (FDA), because many science and application problems
end up with classification problems, such as recognition, prediction,
control, decision making, management, etc. As the high dimension
and high correlation in functional data (FD), it is a key problem to
extract features from FD whereas keeping its global characters, which
relates to the classification efficiency and precision to heavens. In this
paper, a novel automatic method which combined Genetic Algorithm
(GA) and classification algorithm to extract classification features is
proposed. In this method, the optimal features and classification model
are approached via evolutional study step by step. It is proved by
theory analysis and experiment test that this method has advantages in
improving classification efficiency, precision and robustness whereas
using less features and the dimension of extracted classification
features can be controlled.
Abstract: This paper presents the development of a Bayesian
belief network classifier for prediction of graft status and survival
period in renal transplantation using the patient profile information
prior to the transplantation. The objective was to explore feasibility
of developing a decision making tool for identifying the most suitable
recipient among the candidate pool members. The dataset was
compiled from the University of Toledo Medical Center Hospital
patients as reported to the United Network Organ Sharing, and had
1228 patient records for the period covering 1987 through 2009. The
Bayes net classifiers were developed using the Weka machine
learning software workbench. Two separate classifiers were induced
from the data set, one to predict the status of the graft as either failed
or living, and a second classifier to predict the graft survival period.
The classifier for graft status prediction performed very well with a
prediction accuracy of 97.8% and true positive values of 0.967 and
0.988 for the living and failed classes, respectively. The second
classifier to predict the graft survival period yielded a prediction
accuracy of 68.2% and a true positive rate of 0.85 for the class
representing those instances with kidneys failing during the first year
following transplantation. Simulation results indicated that it is
feasible to develop a successful Bayesian belief network classifier for
prediction of graft status, but not the graft survival period, using the
information in UNOS database.
Abstract: Globalization and therefore increasing tight competition among companies, have resulted to increase the importance of making well-timed decision. Devising and employing effective strategies, that are flexible and adaptive to changing market, stand a greater chance of being effective in the long-term. In other side, a clear focus on managing the entire product lifecycle has emerged as critical areas for investment. Therefore, applying wellorganized tools to employ past experience in new case, helps to make proper and managerial decisions. Case based reasoning (CBR) is based on a means of solving a new problem by using or adapting solutions to old problems. In this paper, an adapted CBR model with k-nearest neighbor (K-NN) is employed to provide suggestions for better decision making which are adopted for a given product in the middle of life phase. The set of solutions are weighted by CBR in the principle of group decision making. Wrapper approach of genetic algorithm is employed to generate optimal feature subsets. The dataset of the department store, including various products which are collected among two years, have been used. K-fold approach is used to evaluate the classification accuracy rate. Empirical results are compared with classical case based reasoning algorithm which has no special process for feature selection, CBR-PCA algorithm based on filter approach feature selection, and Artificial Neural Network. The results indicate that the predictive performance of the model, compare with two CBR algorithms, in specific case is more effective.
Abstract: The paper presents the method developed to assess
rating points of objects with qualitative indexes. The novelty of the
method lies in the fact that the authors use linguistic scales that allow
to formalize the values of the indexes with the help of fuzzy sets. As
a result it is possible to operate correctly with dissimilar indexes on
the unified basis and to get stable final results. The obtained rating
points are used in decision making based on fuzzy expert opinions.
Abstract: Multiple criteria decision making (MCDM) is an approach to ranking the solutions and finding the best one when two or more solutions are provided. In this study, MCDM approach is proposed to select the most suitable scheduling rule of robotic flexible assembly cells (RFACs). Two MCDM approaches, Analytic Hierarchy Process (AHP) and Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) are proposed for solving the scheduling rule selection problem. The AHP method is employed to determine the weights of the evaluation criteria, while the TOPSIS method is employed to obtain final ranking order of scheduling rules. Four criteria are used to evaluate the scheduling rules. Also, four scheduling policies of RFAC are examined to choose the most appropriate one for this purpose. A numerical example illustrates applications of the suggested methodology. The results show that the methodology is practical and works in RFAC settings.
Abstract: S-Curves are commonly used in technology forecasting. They show the paths of product performance in relation to time or investment in R&D. It is a useful tool to describe the inflection points and the limit of improvement of a technology. Companies use this information to base their innovation strategies.
However inadequate use and some limitations of this technique lead
to problems in decision making. In this paper first technology
forecasting and its importance for company level strategies will be
discussed. Secondly the S-Curve and its place among other
forecasting techniques will be introduced. Thirdly its use in
technology forecasting will be discussed based on its advantages,
disadvantages and limitations. Finally an application of S-curve on
3D TV technology using patent data will also be presented and the
results will be discussed.
Abstract: We study the possibility of using geometric operators
in the selection of human resources. We develop three new methods
that use the ordered weighted geometric (OWG) operator in different
indexes used for the selection of human resources. The objective of
these models is to manipulate the neutrality of the old methods so the
decision maker is able to select human resources according to his
particular attitude. In order to develop these models, first a short
revision of the OWG operator is developed. Second, we briefly
explain the general process for the selection of human resources.
Then, we develop the three new indexes. They will use the OWG
operator in the Hamming distance, in the adequacy coefficient and in
the index of maximum and minimum level. Finally, an illustrative
example about the new approach is given.
Abstract: The next generation wireless systems, especially the
cognitive radio networks aim at utilizing network resources more
efficiently. They share a wide range of available spectrum in an
opportunistic manner. In this paper, we propose a quality
management model for short-term sub-lease of unutilized spectrum
bands to different service providers. We built our model on
competitive secondary market architecture. To establish the
necessary conditions for convergent behavior, we utilize techniques
from game theory. Our proposed model is based on potential game
approach that is suitable for systems with dynamic decision making.
The Nash equilibrium point tells the spectrum holders the ideal price
values where profit is maximized at the highest level of customer
satisfaction. Our numerical results show that the price decisions of
the network providers depend on the price and QoS of their own
bands as well as the prices and QoS levels of their opponents- bands.