Abstract: Warranty is a powerful marketing tool for the
manufacturer and a good protection for both the manufacturer and the
customer. However, warranty always involves additional costs to the
manufacturer, which depend on product reliability characteristics and
warranty parameters. This paper presents an approach to optimisation
of warranty parameters for known product failure distribution to
reduce the warranty costs to the manufacturer while retaining the
promotional function of the warranty. Combination free replacement
and pro-rata warranty policy is chosen as a model and the length of
free replacement period and pro-rata policy period are varied, as well
as the coefficients that define the pro-rata cost function. Multiparametric
warranty optimisation is done by using genetic algorithm.
Obtained results are guideline for the manufacturer to choose the
warranty policy that minimises the costs and maximises the profit.
Abstract: In this article an evolutionary technique has been used
for the solution of nonlinear Riccati differential equations of fractional order. In this method, genetic algorithm is used as a tool for
the competent global search method hybridized with active-set algorithm for efficient local search. The proposed method has been
successfully applied to solve the different forms of Riccati
differential equations. The strength of proposed method has in its
equal applicability for the integer order case, as well as, fractional
order case. Comparison of the method has been made with standard
numerical techniques as well as the analytic solutions. It is found
that the designed method can provide the solution to the equation
with better accuracy than its counterpart deterministic approaches.
Another advantage of the given approach is to provide results on
entire finite continuous domain unlike other numerical methods
which provide solutions only on discrete grid of points.
Abstract: In the recent past Learning Classifier Systems have
been successfully used for data mining. Learning Classifier System
(LCS) is basically a machine learning technique which combines
evolutionary computing, reinforcement learning, supervised or
unsupervised learning and heuristics to produce adaptive systems. A
LCS learns by interacting with an environment from which it
receives feedback in the form of numerical reward. Learning is
achieved by trying to maximize the amount of reward received. All
LCSs models more or less, comprise four main components; a finite
population of condition–action rules, called classifiers; the
performance component, which governs the interaction with the
environment; the credit assignment component, which distributes the
reward received from the environment to the classifiers accountable
for the rewards obtained; the discovery component, which is
responsible for discovering better rules and improving existing ones
through a genetic algorithm. The concatenate of the production rules
in the LCS form the genotype, and therefore the GA should operate
on a population of classifier systems. This approach is known as the
'Pittsburgh' Classifier Systems. Other LCS that perform their GA at
the rule level within a population are known as 'Mitchigan' Classifier
Systems. The most predominant representation of the discovered
knowledge is the standard production rules (PRs) in the form of IF P
THEN D. The PRs, however, are unable to handle exceptions and do
not exhibit variable precision. The Censored Production Rules
(CPRs), an extension of PRs, were proposed by Michalski and
Winston that exhibit variable precision and supports an efficient
mechanism for handling exceptions. A CPR is an augmented
production rule of the form: IF P THEN D UNLESS C, where
Censor C is an exception to the rule. Such rules are employed in
situations, in which conditional statement IF P THEN D holds
frequently and the assertion C holds rarely. By using a rule of this
type we are free to ignore the exception conditions, when the
resources needed to establish its presence are tight or there is simply
no information available as to whether it holds or not. Thus, the IF P
THEN D part of CPR expresses important information, while the
UNLESS C part acts only as a switch and changes the polarity of D
to ~D. In this paper Pittsburgh style LCSs approach is used for
automated discovery of CPRs. An appropriate encoding scheme is
suggested to represent a chromosome consisting of fixed size set of
CPRs. Suitable genetic operators are designed for the set of CPRs
and individual CPRs and also appropriate fitness function is proposed
that incorporates basic constraints on CPR. Experimental results are
presented to demonstrate the performance of the proposed learning
classifier system.
Abstract: In this paper, two very different optimization
algorithms, Genetic and DIRECT algorithms, are used to history
match a bottomhole pressure response for a reservoir with wellbore
storage and skin with the best possible analytical model. No initial
guesses are available for reservoir parameters. The results show that
the matching process is much faster and more accurate for DIRECT
method in comparison with Genetic algorithm. It is furthermore
concluded that the DIRECT algorithm does not need any initial
guesses, whereas Genetic algorithm needs to be tuned according to
initial guesses.
Abstract: This paper presents a method for the optimal
allocation of Distributed generation in distribution systems. In this
paper, our aim would be optimal distributed generation allocation for
voltage profile improvement and loss reduction in distribution
network. Genetic Algorithm (GA) was used as the solving tool,
which referring two determined aim; the problem is defined and
objective function is introduced. Considering to fitness values
sensitivity in genetic algorithm process, there is needed to apply load
flow for decision-making. Load flow algorithm is combined
appropriately with GA, till access to acceptable results of this
operation. We used MATPOWER package for load flow algorithm
and composed it with our Genetic Algorithm. The suggested method
is programmed under MATLAB software and applied ETAP
software for evaluating of results correctness. It was implemented on
part of Tehran electricity distributing grid. The resulting operation of
this method on some testing system is illuminated improvement of
voltage profile and loss reduction indexes.
Abstract: Synchronization is a difficult problem in CDMA
satellite communications. Due to the influence of additive noise and
fading in the mobile channel, it is not easy to keep up with the
attenuation and offset. This paper considers a recently proposed
approach to solve the problem of synchronization chaotic Chen
system in CDMA satellite communication in the presence of constant
attenuation and offset. An analytic algorithm that provides closed
form channel and carrier offset estimates is presented. The principle
of this approach is based on adding a compensation block before the
receiver to compensate the distortion of the imperfect channel by
using genetic algorithm.
The resultants presented, show that the receiver is able to recover
rapidly the synchronization with the transmitter.
Abstract: A suspension bridge is the most suitable type of structure for a long-span bridge due to rational use of structural materials. Increased deformability, which is conditioned by appearance of the elastic and kinematic displacements, is the major disadvantage of suspension bridges. The problem of increased kinematic displacements under the action of non-symmetrical load can be solved by prestressing. The prestressed suspension bridge with the span of 200 m was considered as an object of investigations. The cable truss with the cross web was considered as the main load carrying structure of the prestressed suspension bridge. The considered cable truss was optimized by 47 variable factors using Genetic algorithm and FEM program ANSYS. It was stated, that the maximum total displacements are reduced up to 29.9% by using of the cable truss with the rational characteristics instead of the single cable in the case of the worst situated load.
Abstract: This paper presents an approach for daily optimal operation of distribution networks considering Distributed Generators (DGs). Due to private ownership of DGs, a cost based compensation method is used to encourage DGs in active and reactive power generation. The objective function is summation of electrical energy generated by DGs and substation bus (main bus) in the next day. A genetic algorithm is used to solve the optimal operation problem. The approach is tested on an IEEE34 buses distribution feeder.
Abstract: Intelligent deep-drawing is an instrumental research field in sheet metal forming. A set of 28 different experimental data have been employed in this paper, investigating the roles of die radius, punch radius, friction coefficients and drawing ratios for axisymmetric workpieces deep drawing. This paper focuses an evolutionary neural network, specifically, error back propagation in collaboration with genetic algorithm. The neural network encompasses a number of different functional nodes defined through the established principles. The input parameters, i.e., punch radii, die radii, friction coefficients and drawing ratios are set to the network; thereafter, the material outputs at two critical points are accurately calculated. The output of the network is used to establish the best parameters leading to the most uniform thickness in the product via the genetic algorithm. This research achieved satisfactory results based on demonstration of neural networks.
Abstract: The main goal of this work is to propose a way for
combined use of two nontraditional algorithms by solving topological
problems on telecommunications concentrator networks. The
algorithms suggested are the Simulated Annealing algorithm and the
Genetic Algorithm. The Algorithm of Simulated Annealing unifies
the well known local search algorithms. In addition - Simulated
Annealing allows acceptation of moves in the search space witch lead
to decisions with higher cost in order to attempt to overcome any
local minima obtained. The Genetic Algorithm is a heuristic approach
witch is being used in wide areas of optimization works. In the last
years this approach is also widely implemented in
Telecommunications Networks Planning. In order to solve less or
more complex planning problem it is important to find the most
appropriate parameters for initializing the function of the algorithm.
Abstract: Transmission network expansion planning (TNEP) is a basic part of power system planning that determines where, when and how many new transmission lines should be added to the network. Up till now, various methods have been presented to solve the static transmission network expansion planning (STNEP) problem. But in all of these methods, lines adequacy rate has not been considered at the end of planning horizon, i.e., expanded network misses adequacy after some times and needs to be expanded again. In this paper, expansion planning has been implemented by merging lines loading parameter in the STNEP and inserting investment cost into the fitness function constraints using genetic algorithm. Expanded network will possess a maximum adequacy to provide load demand and also the transmission lines overloaded later. Finally, adequacy index could be defined and used to compare some designs that have different investment costs and adequacy rates. In this paper, the proposed idea has been tested on the Garvers network. The results show that the network will possess maximum efficiency economically.
Abstract: Load managing method on road became necessary
since overloaded vehicles occur damage on road facilities and existing
systems for preventing this damage still show many
problems.Accordingly, efficient managing system for preventing
overloaded vehicles could be organized by using the road itself as a
scale by applying genetic algorithm to analyze the load and the drive
information of vehicles.Therefore, this paper organized Ubiquitous
sensor network system for development of intelligent overload vehicle
regulation system, also in this study, to use the behavior of road, the
transformation was measured by installing underground box type
indoor model and indoor experiment was held using genetic algorithm.
And we examined wireless possibility of overloaded vehicle
regulation system through experiment of the transmission and
reception distance.If this system will apply to road and bridge, might
be effective for economy and convenience through establishment of
U-IT system..
Abstract: The Resource-Constrained Project Scheduling
Problem (RCPSP) is concerned with single-item or small batch
production where limited resources have to be allocated to dependent
activities over time. Over the past few decades, a lot of work has
been made with the use of optimal solution procedures for this basic
problem type and its extensions. Brucker and Knust[1] discuss, how
timetabling problems can be modeled as a RCPSP. Authors discuss
high school timetabling and university course timetabling problem as
an example. We have formulated two mathematical formulations of
course timetabling problem in a new way which are the prototype of
single-mode RCPSP. Our focus is to show, how course timetabling
problem can be transformed into RCPSP. We solve this
transformation model with genetic algorithm.
Abstract: Supply chain consists of all stages involved, directly
or indirectly, includes all functions involved in fulfilling a customer
demand. In two stage transportation supply chain problem,
transportation costs are of a significant proportion of final product
costs. It is often crucial for successful decisions making approaches
in two stage supply chain to explicit account for non-linear
transportation costs. In this paper, deterministic demand and finite
supply of products was considered. The optimized distribution level
and the routing structure from the manufacturing plants to the
distribution centres and to the end customers is determined using
developed mathematical model and solved by proposed particle
swarm optimization based genetic algorithm. Numerical analysis of
the case study is carried out to validate the model.