Abstract: The Informational Infrastructures of small and medium-sized manufacturing enterprises are relatively poor, there are serious shortages of capitals which can be invested in informatization construction, computer hardware and software resources, and human resources. To address the informatization issue in small and medium-sized manufacturing enterprises, and enable them to the application of advanced management thinking and enhance their competitiveness, the paper establish a manufacturing-oriented small and medium-sized enterprises informatization platform based on the ASP business intelligence technology, which effectively improves the scientificity of enterprises decision and management informatization.
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: This paper argues that a product development exercise
involves in addition to the conventional stages, several decisions
regarding other aspects. These aspects should be addressed
simultaneously in order to develop a product that responds to the
customer needs and that helps realize objectives of the stakeholders
in terms of profitability, market share and the like. We present a
framework that encompasses these different development
dimensions. The framework shows that a product development
methodology such as the Quality Function Deployment (QFD) is the
basic tool which allows definition of the target specifications of a
new product. Creativity is the first dimension that enables the
development exercise to live and end successfully. A number of
group processes need to be followed by the development team in
order to ensure enough creativity and innovation. Secondly,
packaging is considered to be an important extension of the product.
Branding strategies, quality and standardization requirements,
identification technologies, design technologies, production
technologies and costing and pricing are also integral parts to the
development exercise. These dimensions constitute the proposed
framework. The paper also presents a mathematical model used to
calculate the design targets based on the target costing principle. The
framework is used to study a case of a new product development in
the telecommunications services sector.
Abstract: In this paper, all variables are supposed to be integer
and positive. In this modern method, objective function is assumed to
be maximized or minimized but constraints are always explained like
less or equal to. In this method, choosing a dual combination of ideal
nonequivalent and omitting one of variables. With continuing this
act, finally, having one nonequivalent with (n-m+1) unknown
quantities in which final nonequivalent, m is counter for constraints,
n is counter for variables of decision.
Abstract: CIM is the standard formalism for modeling management
information developed by the Distributed Management Task
Force (DMTF) in the context of its WBEM proposal, designed to
provide a conceptual view of the managed environment. In this
paper, we propose the inclusion of formal knowledge representation
techniques, based on Description Logics (DLs) and the Web Ontology
Language (OWL), in CIM-based conceptual modeling, and then we
examine the benefits of such a decision. The proposal is specified
as a CIM metamodel level mapping to a highly expressive subset
of DLs capable of capturing all the semantics of the models. The
paper shows how the proposed mapping provides CIM diagrams with
precise semantics and can be used for automatic reasoning about the
management information models, as a design aid, by means of newgeneration
CASE tools, thanks to the use of state-of-the-art automatic
reasoning systems that support the proposed logic and use algorithms
that are sound and complete with respect to the semantics. Such a
CASE tool framework has been developed by the authors and its
architecture is also introduced. The proposed formalization is not
only useful at design time, but also at run time through the use of
rational autonomous agents, in response to a need recently recognized
by the DMTF.
Abstract: Currently, slider process of Hard Disk Drive Industry
become more complex, defective diagnosis for yield improvement
becomes more complicated and time-consumed. Manufacturing data
analysis with data mining approach is widely used for solving that
problem. The existing mining approach from combining of the KMean
clustering, the machine oriented Kruskal-Wallis test and the
multivariate chart were applied for defective diagnosis but it is still
be a semiautomatic diagnosis system. This article aims to modify an
algorithm to support an automatic decision for the existing approach.
Based on the research framework, the new approach can do an
automatic diagnosis and help engineer to find out the defective
factors faster than the existing approach about 50%.
Abstract: Stochastic resonance (SR) is a phenomenon whereby
the signal transmission or signal processing through certain nonlinear
systems can be improved by adding noise. This paper discusses SR in
nonlinear signal detection by a simple test statistic, which can be
computed from multiple noisy data in a binary decision problem based
on a maximum a posteriori probability criterion. The performance of
detection is assessed by the probability of detection error Per . When
the input signal is subthreshold signal, we establish that benefit from
noise can be gained for different noises and confirm further that the
subthreshold SR exists in nonlinear signal detection. The efficacy of
SR is significantly improved and the minimum of Per can
dramatically approach to zero as the sample number increases. These
results show the robustness of SR in signal detection and extend the
applicability of SR in signal processing.
Abstract: Environmental considerations have become an integral part of developmental thinking and decision making in many countries. It is growing rapidly in importance as a discipline of its own. Preventive approaches have been used at the evolutional process of environmental management as a broad and dynamic system for dealing with pollution and environmental degradation. In this regard, Environmental Assessment as an activity for identification and prediction of project’s impacts carried out in the world and its legal significance dates back to late 1960. In Iran, according to the Article 2 of Environmental Protection Act, Environmental Impact Assessment (EIA) should be prepared for seven categories of project. This article has been actively implementing by Department of Environment at 1997. World Bank in 1989 attempted to introducing application of Environmental Assessment for making decision about projects which are required financial assistance in developing countries. So, preparing EIA for obtaining World Bank loan was obligated. Alborz Project is one of the World Bank Projects in Iran which is environmentally significant. Seven out of ten W.B safeguard policies were considered at this project. In this paper, Alborz project, objectives, safeguard policies and role of environmental management will be elaborated
Abstract: Energy Efficiency Management is the heart of a
worldwide problem. The capability of a multi-agent system as a
technology to manage the micro-grid operation has already been
proved. This paper deals with the implementation of a decisional
pattern applied to a multi-agent system which provides intelligence to
a distributed local energy network considered at local consumer level.
Development of multi-agent application involves agent
specifications, analysis, design, and realization. Furthermore, it can
be implemented by following several decisional patterns. The
purpose of present article is to suggest a new approach for a
decisional pattern involving a multi-agent system to control a
distributed local energy network in a decentralized competitive
system. The proposed solution is the result of a dichotomous
approach based on environment observation. It uses an iterative
process to solve automatic learning problems and converges
monotonically very fast to system attracting operation point.
Abstract: The design of a steam turbine is a very complex
engineering operation that can be simplified and improved thanks to
computer-aided multi-objective optimization. This process makes use
of existing optimization algorithms and losses correlations to identify
those geometries that deliver the best balance of performance (i.e.
Pareto-optimal points).
This paper deals with a one-dimensional multi-objective and
multi-point optimization of a single-stage steam turbine. Using a
genetic optimization algorithm and an algebraic one-dimensional
ideal gas-path model based on loss and deviation correlations, a code
capable of performing the optimization of a predefined steam turbine
stage was developed. More specifically, during this study the
parameters modified (i.e. decision variables) to identify the best
performing geometries were solidity and angles both for stator and
rotor cascades, while the objective functions to maximize were totalto-
static efficiency and specific work done.
Finally, an accurate analysis of the obtained results was carried
out.
Abstract: In this paper, a heuristic method for simultaneous
rescue robot path-planning and mission scheduling is introduced
based on project management techniques, multi criteria decision
making and artificial potential fields path-planning. Groups of
injured people are trapped in a disastrous situation. These people are
categorized into several groups based on the severity of their
situation. A rescue robot, whose ultimate objective is reaching
injured groups and providing preliminary aid for them through a path
with minimum risk, has to perform certain tasks on its way towards
targets before the arrival of rescue team. A decision value is assigned
to each target based on the whole degree of satisfaction of the criteria
and duties of the robot toward the target and the importance of
rescuing each target based on their category and the number of
injured people. The resulted decision value defines the strength of the
attractive potential field of each target. Dangerous environmental
parameters are defined as obstacles whose risk determines the
strength of the repulsive potential field of each obstacle. Moreover,
negative and positive energies are assigned to the targets and
obstacles, which are variable with respects to the factors involved.
The simulation results show that the generated path for two cases
studies with certain differences in environmental conditions and
other risk factors differ considerably.
Abstract: Managing the emergency situations at the Emergency
Staff requires a high co-operation between its members and their fast
decision making. For these purpose it is necessary to prepare Emergency Staff members adequately. The aim of this paper is to
describe the development of information support that focuses to
emergency staff processes and effective decisions. The information
support is based on the principles of process management, and
Process Framework for Emergency Management was used during the
development. The output is the information system that allows users
to simulate an emergency situation, including effective decision making. The system also evaluates the progress of the emergency
processes solving by quantitative and qualitative indicators. By using
the simulator, a higher quality education of specialists can be achieved. Therefore, negative impacts resulting from arising emergency situations can be directly reduced.
Abstract: In this paper, we propose a modified version of the
Constant Modulus Algorithm (CMA) tailored for blind Decision
Feedback Equalizer (DFE) of first order Markovian time varying
channels. The proposed NonStationary CMA (NSCMA) is designed
so that it explicitly takes into account the Markovian structure of
the channel nonstationarity. Hence, unlike the classical CMA, the
NSCMA is not blind with respect to the channel time variations.
This greatly helps the equalizer in the case of realistic channels, and
avoids frequent transmissions of training sequences.
This paper develops a theoretical analysis of the steady state
performance of the CMA and the NSCMA for DFEs within a time
varying context. Therefore, approximate expressions of the mean
square errors are derived. We prove that in the steady state, the
NSCMA exhibits better performance than the classical CMA. These
new results are confirmed by simulation.
Through an experimental study, we demonstrate that the Bit Error
Rate (BER) is reduced by the NSCMA-DFE, and the improvement
of the BER achieved by the NSCMA-DFE is as significant as the
channel time variations are severe.
Abstract: Data Mining aims at discovering knowledge out of
data and presenting it in a form that is easily comprehensible to
humans. One of the useful applications in Egypt is the Cancer
management, especially the management of Acute Lymphoblastic
Leukemia or ALL, which is the most common type of cancer in
children.
This paper discusses the process of designing a prototype that can
help in the management of childhood ALL, which has a great
significance in the health care field. Besides, it has a social impact
on decreasing the rate of infection in children in Egypt. It also
provides valubale information about the distribution and
segmentation of ALL in Egypt, which may be linked to the possible
risk factors.
Undirected Knowledge Discovery is used since, in the case of this
research project, there is no target field as the data provided is
mainly subjective. This is done in order to quantify the subjective
variables. Therefore, the computer will be asked to identify
significant patterns in the provided medical data about ALL. This
may be achieved through collecting the data necessary for the
system, determimng the data mining technique to be used for the
system, and choosing the most suitable implementation tool for the
domain.
The research makes use of a data mining tool, Clementine, so as to
apply Decision Trees technique. We feed it with data extracted from
real-life cases taken from specialized Cancer Institutes. Relevant
medical cases details such as patient medical history and diagnosis
are analyzed, classified, and clustered in order to improve the disease
management.
Abstract: The identification and classification of the spine deformity play an important role when considering surgical planning for adolescent patients with idiopathic scoliosis. The subject of this article is the Lenke classification of scoliotic spines using Cobb angle measurements. The purpose is two-fold: (1) design a rulebased diagram to assist clinicians in the classification process and (2) investigate a computer classifier which improves the classification time and accuracy. The rule-based diagram efficiency was evaluated in a series of scoliotic classifications by 10 clinicians. The computer classifier was tested on a radiographic measurement database of 603 patients. Classification accuracy was 93% using the rule-based diagram and 99% for the computer classifier. Both the computer classifier and the rule based diagram can efficiently assist clinicians in their Lenke classification of spine scoliosis.
Abstract: In this paper a new approach is proposed for the
adaptation of the simulated annealing search in the field of the
Multi-Objective Optimization (MOO). This new approach is called
Multi-Case Multi-Objective Simulated Annealing (MC-MOSA). It
uses some basics of a well-known recent Multi-Objective Simulated
Annealing proposed by Ulungu et al., which is referred in the
literature as U-MOSA. However, some drawbacks of this algorithm
have been found, and are substituted by other ones, especially in
the acceptance decision criterion. The MC-MOSA has shown better
performance than the U-MOSA in the numerical experiments. This
performance is further improved by some other subvariants of the
MC-MOSA, such as Fast-annealing MC-MOSA, Re-annealing MCMOSA
and the Two-Stage annealing MC-MOSA.
Abstract: The γ-turns play important roles in protein folding and
molecular recognition. The prediction and analysis of γ-turn types are
important for both protein structure predictions and better
understanding the characteristics of different γ-turn types. This study
proposed a physicochemical property-based decision tree (PPDT)
method to interpretably predict γ-turn types. In addition to the good
prediction performance of PPDT, three simple and human
interpretable IF-THEN rules are extracted from the decision tree
constructed by PPDT. The identified informative physicochemical
properties and concise rules provide a simple way for discriminating
and understanding γ-turn types.
Abstract: This paper aims at developing a multilevel fuzzy
decision support model for urban rail transit planning schemes in
China under the background that China is presently experiencing an
unprecedented construction of urban rail transit. In this study, an
appropriate model using multilevel fuzzy comprehensive evaluation
method is developed. In the decision process, the followings are
considered as the influential objectives: traveler attraction,
environment protection, project feasibility and operation. In addition,
consistent matrix analysis method is used to determine the weights
between objectives and the weights between the objectives-
sub-indictors, which reduces the work caused by repeated
establishment of the decision matrix on the basis of ensuring the
consistency of decision matrix. The application results show that
multilevel fuzzy decision model can perfectly deal with the
multivariable and multilevel decision process, which is particularly
useful in the resolution of multilevel decision-making problem of
urban rail transit planning schemes.
Abstract: To fight against the economic crisis, French
Government, like many others in Europe, has decided to give a boost
to high-speed line projects. This paper explores the implementation
and decision-making process in TGV projects, their evolutions,
especially since the Mediterranean TGV-line. This project was
probably the most controversial, but paradoxically represents today a
huge success for all the actors involved.
What kind of lessons we can learn from this experience? How to
evaluate the impact of this project on TGV-line planning? How can
we characterize this implementation and decision-making process
regards to the sustainability challenges?
The construction of Mediterranean TGV-line was the occasion to
make several innovations: to introduce more dialog into the decisionmaking
process, to take into account the environment, to introduce a
new project management and technological innovations. That-s why
this project appears today as an example in terms of integration of
sustainable development.
In this paper we examine the different kinds of innovations
developed in this project, by using concepts from sociology of
innovation to understand how these solutions emerged in a
controversial situation. Then we analyze the lessons which were
drawn from this decision-making process (in the immediacy and a
posteriori) and the way in which procedures evolved: creation of new
tools and devices (public consultation, project management...).
Finally we try to highlight the impact of this evolution on TGV
projects governance. In particular, new methods of implementation
and financing involve a reconfiguration of the system of actors. The
aim of this paper is to define the impact of this reconfiguration on
negotiations between stakeholders.
Abstract: This paper presents various classifiers results from a system that can automatically recognize four different static human body postures in video sequences. The considered postures are standing, sitting, squatting, and lying. The three classifiers considered are a naïve one and two based on the belief theory. The belief theory-based classifiers use either a classic or restricted plausibility criterion to make a decision after data fusion. The data come from the people 2D segmentation and from their face localization. Measurements consist in distances relative to a reference posture. The efficiency and the limits of the different classifiers on the recognition system are highlighted thanks to the analysis of a great number of results. This system allows real-time processing.