Abstract: Supplier selection is a multi criteria decision-making process that comprises tangible and intangible factors. The majority of previous supplier selection techniques do not consider strategic perspective. Besides, uncertainty is one of the most important obstacles in supplier selection. For the first, time in this paper, the idea of the algorithm " Knapsack " is used to select suppliers Moreover, an attempt has to be made to take the advantage of a simple numerical method for solving model .This is an innovation to resolve any ambiguity in choosing suppliers. This model has been tried in the suppliers selected in a competitive environment and according to all desired standards of quality and quantity to show the efficiency of the model, an industry sample has been uses.
Abstract: This paper aims to develop a model that assists the
international retailer in selecting the country that maximizes the
degree of fit between the retailer-s goals and the country
characteristics in his initial internationalization move. A two-stage
multi criteria decision model is designed integrating the Analytic
Hierarchy Process (AHP) and Goal Programming. Ethical, cultural,
geographic and economic proximity are identified as the relevant
constructs of the internationalization decision. The constructs are
further structured into sub-factors within analytic hierarchy. The
model helps the retailer to integrate, rank and weigh a number of
hard and soft factors and prioritize the countries accordingly. The
model has been implemented on a Turkish luxury goods retailer who
was planning to internationalize. Actual entry of the specific retailer
in the selected country is a support for the model. Implementation on
a single retailer limits the generalizability of the results; however, the
emphasis of the paper is on construct identification and model
development. The paper enriches the existing literature by proposing
a hybrid multi objective decision model which introduces new soft
dimensions i.e. perceived distance, ethical proximity, humane
orientation to the decision process and facilitates effective decision
making.
Abstract: Existing experiences indicate that one of the most
prominent reasons that some ERP implementations fail is related to
selecting an improper ERP package. Among those important factors
resulting in inappropriate ERP selections, one is to ignore preliminary
activities that should be done before the evaluation of ERP packages.
Another factor yielding these unsuitable selections is that usually
organizations employ prolonged and costly selection processes in
such extent that sometimes the process would never be finalized
or sometimes the evaluation team might perform many key final
activities in an incomplete or inaccurate way due to exhaustion, lack
of interest or out-of-date data. In this paper, a systematic approach
that recommends some activities to be done before and after the
main selection phase is introduced for choosing an ERP package. On
the other hand, the proposed approach has utilized some ideas that
accelerates the selection process at the same time that reduces the
probability of an erroneous final selection.
Abstract: Generally speaking, the mobile robot is capable of
sensing its surrounding environment, interpreting the sensed
information to obtain the knowledge of its location and the
environment, planning a real-time trajectory to reach the object. In
this process, the issue of obstacle avoidance is a fundamental topic to
be challenged. Thus, an adaptive path-planning control scheme is
designed without detailed environmental information, large memory
size and heavy computation burden in this study for the obstacle
avoidance of a mobile robot. In this scheme, the robot can gradually
approach its object according to the motion tracking mode, obstacle
avoidance mode, self-rotation mode, and robot state selection. The
effectiveness of the proposed adaptive path-planning control scheme
is verified by numerical simulations of a differential-driving mobile
robot under the possible occurrence of obstacle shapes.
Abstract: Interactive push VOD system is a new kind of system
that incorporates push technology and interactive technique. It can
push movies to users at high speeds at off-peak hours for optimal
network usage so as to save bandwidth. This paper presents effective
software-based solution for processing mass downstream data at
terminals of interactive push VOD system, where the service can
download movie according to a viewer-s selection. The downstream
data is divided into two catalogs: (1) the carousel data delivered
according to DSM-CC protocol; (2) IP data delivered according to
Euro-DOCSIS protocol. In order to accelerate download speed and
reduce data loss rate at terminals, this software strategy introduces
caching, multi-thread and resuming mechanisms. The experiments
demonstrate advantages of the software-based solution.
Abstract: In order to reduce cost, increase quality, and for
timely supplying production systems has considerably taken the
advantages of supply chain management and these advantages are
also competitive. Selection of appropriate supplier has an important
role in improvement and efficiency of systems.
The models of supplier selection which have already been used by
researchers have considered selection one or more suppliers from
potential suppliers but in this paper selecting one supplier as partner
from one supplier that have minimum one period supplying to buyer
is considered.
This paper presents a conceptual model for partner selection and
application of Degree of Adoptive (DOA) model for final selection.
The attributes weight in this model is prepared through AHP
model. After making the descriptive model, determining the
attributes and measuring the parameters of the adaptive is examined
in an auto industry of Iran(Zagross Khodro co.) and results are
presented.
Abstract: Adaptive Genetic Algorithms extend the Standard Gas
to use dynamic procedures to apply evolutionary operators such as
crossover, mutation and selection. In this paper, we try to propose a
new adaptive genetic algorithm, which is based on the statistical
information of the population as a guideline to tune its crossover,
selection and mutation operators. This algorithms is called Statistical
Genetic Algorithm and is compared with traditional GA in some
benchmark problems.
Abstract: In this paper, cloud resource broker using goalbased
request in medical application is proposed. To handle recent
huge production of digital images and data in medical informatics
application, the cloud resource broker could be used by medical
practitioner for proper process in discovering and selecting correct
information and application. This paper summarizes several
reviewed articles to relate medical informatics application with
current broker technology and presents a research work in applying
goal-based request in cloud resource broker to optimize the use of
resources in cloud environment. The objective of proposing a new
kind of resource broker is to enhance the current resource
scheduling, discovery, and selection procedures. We believed that
it could help to maximize resources allocation in medical
informatics application.
Abstract: Chemical industry project management involves
complex decision making situations that require discerning abilities
and methods to make sound decisions. Project managers are faced
with decision environments and problems in projects that are
complex. In this work, case study is Research and Development
(R&D) project selection. R&D is an ongoing process for forward
thinking technology-based chemical industries. R&D project
selection is an important task for organizations with R&D project
management. It is a multi-criteria problem which includes both
tangible and intangible factors. The ability to make sound decisions
is very important to success of R&D projects. Multiple-criteria
decision making (MCDM) approaches are major parts of decision
theory and analysis. This paper presents all of MCDM approaches
for use in R&D project selection. It is hoped that this work will
provide a ready reference on MCDM and this will encourage the
application of the MCDM by chemical engineering management.
Abstract: In this paper we proposed a method for finding video
frames representing one sign in the finger alphabet. The method is
based on determining hands location, segmentation and the use of
standard video quality evaluation metrics. Metric calculation is
performed only in regions of interest. Sliding mechanism for finding
local extrema and adaptive threshold based on local averaging is used
for key frames selection. The success rate is evaluated by recall,
precision and F1 measure. The method effectiveness is compared
with metrics applied to all frames. Proposed method is fast, effective
and relatively easy to realize by simple input video preprocessing
and subsequent use of tools designed for video quality measuring.
Abstract: This paper presents a novel algorithm of stereo
correspondence with rank transform. In this algorithm we used the
genetic algorithm to achieve the accurate disparity map. Genetic
algorithms are efficient search methods based on principles of
population genetic, i.e. mating, chromosome crossover, gene
mutation, and natural selection. Finally morphology is employed to
remove the errors and discontinuities.
Abstract: Despite of many scholars and practitioners recognize
the knowledge management implementation in an organizations but
insufficient attention has been paid by researchers to select suitable
knowledge portal system (KPS) selection. This study develops a
Multi Criteria Decision making model based on the fuzzy VIKOR
approach to help organizations in selecting KPS. The suitable portal
is the critical influential factors on the success of knowledge
management (KM) implementation in an organization.
Abstract: This paper presents a methodology for operational and
economic characteristics based evaluation and selection of a power
plant using Graph theoretic approach. A universal evaluation index
on the basis of Operational and economics characteristics of a plant is
proposed which evaluates and ranks the various types of power plants.
The index thus obtained from the pool of operational characteristics
of the power plant attributes Digraph. The Digraph is developed
considering Operational and economics attributes of the power plants
and their relative importance for their smooth operation, installation
and commissioning and prioritizing their selection. The sensitivity
analysis of the attributes towards the objective has also been carried
out in order to study the impact of attributes over the desired outcome
i.e. the universal operational-economics index of the power plant.
Abstract: Decision making preferences to certain criteria
usually focus on positive degrees without considering the negative
degrees. However, in real life situation, evaluation becomes more
comprehensive if negative degrees are considered concurrently.
Preference is expected to be more effective when considering both
positive and negative degrees of preference to evaluate the best
selection. Therefore, the aim of this paper is to propose the
conflicting bifuzzy preference relations in group decision making by
utilization of a novel score function. The conflicting bifuzzy
preference relation is obtained by introducing some modifications on
intuitionistic fuzzy preference relations. Releasing the intuitionistic
condition by taking into account positive and negative degrees
simultaneously and utilizing the novel score function are the main
modifications to establish the proposed preference model. The
proposed model is tested with a numerical example and proved to be
simple and practical. The four-step decision model shows the
efficiency of obtaining preference in group decision making.
Abstract: H.264/AVC offers a considerably higher improvement
in coding efficiency compared to other compression standards such
as MPEG-2, but computational complexity is increased significantly.
In this paper, we propose selective mode decision schemes for fast
intra prediction mode selection. The objective is to reduce the
computational complexity of the H.264/AVC encoder without
significant rate-distortion performance degradation. In our proposed
schemes, the intra prediction complexity is reduced by limiting the
luma and chroma prediction modes using the directional information
of the 16×16 prediction mode. Experimental results are presented to
show that the proposed schemes reduce the complexity by up to 78%
maintaining the similar PSNR quality with about 1.46% bit rate
increase in average.
Abstract: Lung cancer accounts for the most cancer related deaths for men as well as for women. The identification of cancer associated genes and the related pathways are essential to provide an important possibility in the prevention of many types of cancer. In this work two filter approaches, namely the information gain and the biomarker identifier (BMI) are used for the identification of different types of small-cell and non-small-cell lung cancer. A new method to determine the BMI thresholds is proposed to prioritize genes (i.e., primary, secondary and tertiary) using a k-means clustering approach. Sets of key genes were identified that can be found in several pathways. It turned out that the modified BMI is well suited for microarray data and therefore BMI is proposed as a powerful tool for the search for new and so far undiscovered genes related to cancer.
Abstract: There is an urgent need to develop novel
Mycobacterium tuberculosis (Mtb) drugs that are active against drug
resistant bacteria but, more importantly, kill persistent bacteria. Our
study structured based on integrated analysis of metabolic pathways,
small molecule screening and similarity Search in PubChem
Database. Metabolic analysis approaches based on Unified weighted
used for potent target selection. Our results suggest that pantothenate
synthetase (panC) and and 3-methyl-2-oxobutanoate hydroxymethyl
transferase (panB) as a appropriate drug targets. In our study, we
used pantothenate synthetase because of existence inhibitors. We
have reported the discovery of new antitubercular compounds
through ligand based approaches using computational tools.
Abstract: The belief K-modes method (BKM) approach is a new
clustering technique handling uncertainty in the attribute values of
objects in both the cluster construction task and the classification one.
Like the standard version of this method, the BKM results depend on
the chosen initial modes. So, one selection method of initial modes
is developed, in this paper, aiming at improving the performances of
the BKM approach. Experiments with several sets of real data show
that by considered the developed selection initial modes method, the
clustering algorithm produces more accurate results.
Abstract: The ever increasing use of World Wide Web in the
existing network, results in poor performance. Several techniques
have been developed for reducing web traffic by compressing the size
of the file, saving the web pages at the client side, changing the burst
nature of traffic into constant rate etc. No single method was
adequate enough to access the document instantly through the
Internet. In this paper, adaptive hybrid algorithms are developed for
reducing web traffic. Intelligent agents are used for monitoring the
web traffic. Depending upon the bandwidth usage, user-s preferences,
server and browser capabilities, intelligent agents use the best
techniques to achieve maximum traffic reduction. Web caching,
compression, filtering, optimization of HTML tags, and traffic
dispersion are incorporated into this adaptive selection. Using this
new hybrid technique, latency is reduced to 20 – 60 % and cache hit
ratio is increased 40 – 82 %.
Abstract: Camera calibration is an indispensable step for augmented
reality or image guided applications where quantitative information
should be derived from the images. Usually, a camera
calibration is obtained by taking images of a special calibration object
and extracting the image coordinates of projected calibration marks
enabling the calculation of the projection from the 3d world coordinates
to the 2d image coordinates. Thus such a procedure exhibits
typical steps, including feature point localization in the acquired
images, camera model fitting, correction of distortion introduced by
the optics and finally an optimization of the model-s parameters. In
this paper we propose to extend this list by further step concerning
the identification of the optimal subset of images yielding the smallest
overall calibration error. For this, we present a Monte Carlo based
algorithm along with a deterministic extension that automatically
determines the images yielding an optimal calibration. Finally, we
present results proving that the calibration can be significantly
improved by automated image selection.