Abstract: The objective of this research is to calculate the
optimal inventory lot-sizing for each supplier and minimize the total
inventory cost which includes joint purchase cost of the products,
transaction cost for the suppliers, and holding cost for remaining
inventory. Genetic algorithms (GAs) are applied to the multi-product
and multi-period inventory lot-sizing problems with supplier
selection under storage space. Also a maximum storage space for the
decision maker in each period is considered. The decision maker
needs to determine what products to order in what quantities with
which suppliers in which periods. It is assumed that demand of
multiple products is known over a planning horizon. The problem is
formulated as a mixed integer programming and is solved with the
GAs. The detailed computation results are presented.
Abstract: Quality Function Deployment (QFD) is an expounded, multi-step planning method for delivering commodity, services, and processes to customers, both external and internal to an organization. It is a way to convert between the diverse customer languages expressing demands (Voice of the Customer), and the organization-s languages expressing results that sate those demands. The policy is to establish one or more matrices that inter-relate producer and consumer reciprocal expectations. Due to its visual presence is called the “House of Quality" (HOQ). In this paper, we assumed HOQ in multi attribute decision making (MADM) pattern and through a proposed MADM method, rank technical specifications. Thereafter compute satisfaction degree of customer requirements and for it, we apply vagueness and uncertainty conditions in decision making by fuzzy set theory. This approach would propound supervised neural network (perceptron) for MADM problem solving.
Abstract: This paper presents an interval-based multi-attribute
decision making (MADM) approach in support of the decision
process with imprecise information. The proposed decision
methodology is based on the model of linear additive utility function
but extends the problem formulation with the measure of composite
utility variance. A sample study concerning with the evaluation of
electric generation expansion strategies is provided showing how the
imprecise data may affect the choice toward the best solution and
how a set of alternatives, acceptable to the decision maker (DM),
may be identified with certain confidence.
Abstract: PARADIGMA (PARticipative Approach to DIsease
Global Management) is a pilot project which aims to develop and
demonstrate an Internet based reference framework to share scientific
resources and findings in the treatment of major diseases.
PARADIGMA defines and disseminates a common methodology and
optimised protocols (Clinical Pathways) to support service functions
directed to patients and individuals on matters like prevention, posthospitalisation
support and awareness. PARADIGMA will provide a
platform of information services - user oriented and optimised
against social, cultural and technological constraints - supporting the
Health Care Global System of the Euro-Mediterranean Community
in a continuous improvement process.
Abstract: The healthcare environment is generally perceived as
being information rich yet knowledge poor. However, there is a lack
of effective analysis tools to discover hidden relationships and trends
in data. In fact, valuable knowledge can be discovered from
application of data mining techniques in healthcare system. In this
study, a proficient methodology for the extraction of significant
patterns from the Coronary Heart Disease warehouses for heart
attack prediction, which unfortunately continues to be a leading cause
of mortality in the whole world, has been presented. For this purpose,
we propose to enumerate dynamically the optimal subsets of the
reduced features of high interest by using rough sets technique
associated to dynamic programming. Therefore, we propose to
validate the classification using Random Forest (RF) decision tree to
identify the risky heart disease cases. This work is based on a large
amount of data collected from several clinical institutions based on
the medical profile of patient. Moreover, the experts- knowledge in
this field has been taken into consideration in order to define the
disease, its risk factors, and to establish significant knowledge
relationships among the medical factors. A computer-aided system is
developed for this purpose based on a population of 525 adults. The
performance of the proposed model is analyzed and evaluated based
on set of benchmark techniques applied in this classification problem.
Abstract: Decision fusion is one of hot research topics in
classification area, which aims to achieve the best possible
performance for the task at hand. In this paper, we
investigate the usefulness of this concept to improve change
detection accuracy in remote sensing. Thereby, outputs of
two fuzzy change detectors based respectively on
simultaneous and comparative analysis of multitemporal
data are fused by using fuzzy integral operators. This
method fuses the objective evidences produced by the
change detectors with respect to fuzzy measures that express
the difference of performance between them. The proposed
fusion framework is evaluated in comparison with some
ordinary fuzzy aggregation operators. Experiments carried
out on two SPOT images showed that the fuzzy integral was
the best performing. It improves the change detection
accuracy while attempting to equalize the accuracy rate in
both change and no change classes.
Abstract: In this paper, the implementation of a rule-based
intuitive reasoner is presented. The implementation included two
parts: the rule induction module and the intuitive reasoner. A large
weather database was acquired as the data source. Twelve weather
variables from those data were chosen as the “target variables"
whose values were predicted by the intuitive reasoner. A “complex"
situation was simulated by making only subsets of the data available
to the rule induction module. As a result, the rules induced were
based on incomplete information with variable levels of certainty.
The certainty level was modeled by a metric called "Strength of
Belief", which was assigned to each rule or datum as ancillary
information about the confidence in its accuracy. Two techniques
were employed to induce rules from the data subsets: decision tree
and multi-polynomial regression, respectively for the discrete and the
continuous type of target variables. The intuitive reasoner was tested
for its ability to use the induced rules to predict the classes of the
discrete target variables and the values of the continuous target
variables. The intuitive reasoner implemented two types of
reasoning: fast and broad where, by analogy to human thought, the
former corresponds to fast decision making and the latter to deeper
contemplation. . For reference, a weather data analysis approach
which had been applied on similar tasks was adopted to analyze the
complete database and create predictive models for the same 12
target variables. The values predicted by the intuitive reasoner and
the reference approach were compared with actual data. The intuitive
reasoner reached near-100% accuracy for two continuous target
variables. For the discrete target variables, the intuitive reasoner
predicted at least 70% as accurately as the reference reasoner. Since
the intuitive reasoner operated on rules derived from only about 10%
of the total data, it demonstrated the potential advantages in dealing
with sparse data sets as compared with conventional methods.
Abstract: In this paper I have developed a system for evaluating
the degree of fear emotion that the intelligent agent-based system
may feel when it encounters to a persecuting event. In this paper I
want to describe behaviors of emotional agents using human
behavior in terms of the way their emotional states evolve over time.
I have implemented a fuzzy inference system using Java
environment. As the inputs of this system, I have considered three
parameters related on human fear emotion. The system outputs can
be used in agent decision making process or choosing a person for
team working systems by combination the intensity of fear to other
emotion intensities.
Abstract: Knowledge is attributed to human whose problemsolving
behavior is subjective and complex. In today-s knowledge
economy, the need to manage knowledge produced by a community
of actors cannot be overemphasized. This is due to the fact that
actors possess some level of tacit knowledge which is generally
difficult to articulate. Problem-solving requires searching and sharing
of knowledge among a group of actors in a particular context.
Knowledge expressed within the context of a problem resolution
must be capitalized for future reuse. In this paper, an approach that
permits dynamic capitalization of relevant and reliable actors-
knowledge in solving decision problem following Economic
Intelligence process is proposed. Knowledge annotation method and
temporal attributes are used for handling the complexity in the
communication among actors and in contextualizing expressed
knowledge. A prototype is built to demonstrate the functionalities of
a collaborative Knowledge Management system based on this
approach. It is tested with sample cases and the result showed that
dynamic capitalization leads to knowledge validation hence
increasing reliability of captured knowledge for reuse. The system
can be adapted to various domains.
Abstract: The paper proposes a new concept in developing
collaborative design system. The concept framework involves
applying simulation of supply chain management to collaborative
design called – 'SCM–Based Design Tool'. The system is developed
particularly to support design activities and to integrate all facilities
together. The system is aimed to increase design productivity and
creativity. Therefore, designers and customers can collaborate by the
system since conceptual design. JAG: Jewelry Art Generator based
on artificial intelligence techniques is integrated into the system.
Moreover, the proposed system can support users as decision tool
and data propagation. The system covers since raw material supply
until product delivery. Data management and sharing information are
visually supported to designers and customers via user interface. The
system is developed on Web–assisted product development
environment. The prototype system is presented for Thai jewelry
industry as a system prototype demonstration, but applicable for
other industry.
Abstract: In this paper, we present user pattern learning
algorithm based MDSS (Medical Decision support system) under
ubiquitous. Most of researches are focus on hardware system, hospital
management and whole concept of ubiquitous environment even
though it is hard to implement. Our objective of this paper is to design
a MDSS framework. It helps to patient for medical treatment and
prevention of the high risk patient (COPD, heart disease, Diabetes).
This framework consist database, CAD (Computer Aided diagnosis
support system) and CAP (computer aided user vital sign prediction
system). It can be applied to develop user pattern learning algorithm
based MDSS for homecare and silver town service. Especially this
CAD has wise decision making competency. It compares current vital
sign with user-s normal condition pattern data. In addition, the CAP
computes user vital sign prediction using past data of the patient. The
novel approach is using neural network method, wireless vital sign
acquisition devices and personal computer DB system. An intelligent
agent based MDSS will help elder people and high risk patients to
prevent sudden death and disease, the physician to get the online
access to patients- data, the plan of medication service priority (e.g.
emergency case).
Abstract: With the implied volatility as an important factor in
financial decision-making, in particular in option pricing valuation,
and also the given fact that the pricing biases of Leland option pricing
models and the implied volatility structure for the options are related,
this study considers examining the implied adjusted volatility smile
patterns and term structures in the S&P/ASX 200 index options using
the different Leland option pricing models. The examination of the
implied adjusted volatility smiles and term structures in the
Australian index options market covers the global financial crisis in
the mid-2007. The implied adjusted volatility was found to escalate
approximately triple the rate prior the crisis.
Abstract: This paper explores the effectiveness of machine
learning techniques in detecting firms that issue fraudulent financial
statements (FFS) and deals with the identification of factors
associated to FFS. To this end, a number of experiments have been
conducted using representative learning algorithms, which were
trained using a data set of 164 fraud and non-fraud Greek firms in the
recent period 2001-2002. The decision of which particular method to
choose is a complicated problem. A good alternative to choosing
only one method is to create a hybrid forecasting system
incorporating a number of possible solution methods as components
(an ensemble of classifiers). For this purpose, we have implemented
a hybrid decision support system that combines the representative
algorithms using a stacking variant methodology and achieves better
performance than any examined simple and ensemble method. To
sum up, this study indicates that the investigation of financial
information can be used in the identification of FFS and underline the
importance of financial ratios.
Abstract: Chronic conditions carry with them strong emotions
and often lead to charged relationships between patients and their
health providers and, by extension, patients and health researchers.
Persons are both autonomous and relational and a purely cognitive
model of autonomy neglects the social and relational basis of chronic
illness. Ensuring genuine informed consent in research requires a
thorough understanding of how participants perceive a study and
their reasons for participation. Surveys may not capture the
complexities of reasoning that underlies study participation.
Contradictory reasons for participation, for instance an initial claim
of altruism as rationale and a subsequent claim of personal benefit
(therapeutic misconception), affect the quality of informed consent.
Individuals apply principles through the filter of personal values and
lived experience. Authentic autonomy, and hence authentic consent
to research, occurs within the context of patients- unique life
narratives and illness experiences.
Abstract: Based on a non-linear single track model which
describes the dynamics of vehicle, an optimal path planning strategy
is developed. Real time optimization is used to generate reference
control values to allow leading the vehicle alongside a calculated lane
which is optimal for different objectives such as energy consumption,
run time, safety or comfort characteristics. Strict mathematic
formulation of the autonomous driving allows taking decision on
undefined situation such as lane change or obstacle avoidance. Based
on position of the vehicle, lane situation and obstacle position, the
optimization problem is reformulated in real-time to avoid the
obstacle and any car crash.
Abstract: Most Decision Support Systems (DSS) for waste
management (WM) constructed are not widely marketed and lack
practical applications. This is due to the number of variables and
complexity of the mathematical models which include the
assumptions and constraints required in decision making. The
approach made by many researchers in DSS modelling is to isolate a
few key factors that have a significant influence to the DSS. This
segmented approach does not provide a thorough understanding of
the complex relationships of the many elements involved. The
various elements in constructing the DSS must be integrated and
optimized in order to produce a viable model that is marketable and
has practical application. The DSS model used in assisting decision
makers should be integrated with GIS, able to give robust prediction
despite the inherent uncertainties of waste generation and the plethora
of waste characteristics, and gives optimal allocation of waste stream
for recycling, incineration, landfill and composting.
Abstract: Mathematical programming has been applied to various
problems. For many actual problems, the assumption that the parameters
involved are deterministic known data is often unjustified. In
such cases, these data contain uncertainty and are thus represented
as random variables, since they represent information about the
future. Decision-making under uncertainty involves potential risk.
Stochastic programming is a commonly used method for optimization
under uncertainty. A stochastic programming problem with recourse
is referred to as a two-stage stochastic problem. In this study, we
consider a stochastic programming problem with simple integer
recourse in which the value of the recourse variable is restricted to a
multiple of a nonnegative integer. The algorithm of a dynamic slope
scaling procedure for solving this problem is developed by using a
property of the expected recourse function. Numerical experiments
demonstrate that the proposed algorithm is quite efficient. The
stochastic programming model defined in this paper is quite useful
for a variety of design and operational problems.
Abstract: Many exist studies always use Markov decision
processes (MDPs) in modeling optimal route choice in
stochastic, time-varying networks. However, taking many
variable traffic data and transforming them into optimal route
decision is a computational challenge by employing MDPs in
real transportation networks. In this paper we model finite
horizon MDPs using directed hypergraphs. It is shown that the
problem of route choice in stochastic, time-varying networks
can be formulated as a minimum cost hyperpath problem, and
it also can be solved in linear time. We finally demonstrate the
significant computational advantages of the introduced
methods.
Abstract: This paper deals with the application of a well-known neural network technique, multilayer back-propagation (BP) neural network, in financial data mining. A modified neural network forecasting model is presented, and an intelligent mining system is developed. The system can forecast the buying and selling signs according to the prediction of future trends to stock market, and provide decision-making for stock investors. The simulation result of seven years to Shanghai Composite Index shows that the return achieved by this mining system is about three times as large as that achieved by the buy and hold strategy, so it is advantageous to apply neural networks to forecast financial time series, the different investors could benefit from it.
Abstract: General requirements for knowledge representation in
the form of logic rules, applicable to design and control of industrial
processes, are formulated. Characteristic behavior of decision trees
(DTs) and rough sets theory (RST) in rules extraction from recorded
data is discussed and illustrated with simple examples. The
significance of the models- drawbacks was evaluated, using
simulated and industrial data sets. It is concluded that performance of
DTs may be considerably poorer in several important aspects,
compared to RST, particularly when not only a characterization of a
problem is required, but also detailed and precise rules are needed,
according to actual, specific problems to be solved.