Genetic Algorithm Approach for GA Technologies Project Scheduling

This report explores the application of genetic algorithms within GA Technologies, focusing on project scheduling, particularly in the context of mould maintenance within the plastic industry. It delves into the complexities of production scheduling, the importance of maintenance planning, and the challenges associated with resource management, including injection moulds and tools. The report reviews literature on Monte Carlo simulations, dynamic scheduling, and the use of genetic algorithms to optimize processes. It highlights the significance of joint scheduling, where production schedules are integrated with maintenance schedules to minimize downtime and reduce costs. The study also examines the use of tool management systems to monitor and maintain tools. The genetic algorithm is employed to address the plastic productivity and maintenance processes. The report references the context of the plastic industry in the USA and the importance of efficient production and resource allocation.

Running Head: GA TECHNOLOGIES
GA Technologies
Name of the Student
Name of the University
Author Note

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Table of Contents
Introduction:....................................................................................................................................3
Literature Review:...........................................................................................................................3
Conclusion:....................................................................................................................................11
Reflection:......................................................................................................................................11
Reference:......................................................................................................................................13

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Introduction:
The paper is responsible for the discussion regarding the genetic algorithm approach for
the process of project scheduling with the help of mould maintenance. The maintenance
scheduling procedure involves the maintenance of the single resource machine. The report is also
discusses the process of genetic algorithm. The maintenance process depends on the priority that
is set by the tool management process. This procedure is done at the time of production. In the
manufacturing industry the activities related to maintenance are executed at a short span of time.
This helps in the reduction of the maintenance time. The example of this implementation is the
plastic industry. The plastic industry in United States of America is the fourth largest industry.
The USA plastic industry is responsible for the supply of the large amount of plastic products
around the globe. The plastic is supplied for the packaging of the different products such as the
various electronic products, automobiles and the aircrafts. In order to depict the process of
maintenance of the resources, the paper takes the help of product scheduling along with the
scheduling of the mould.
Literature Review:
The Monte Carlo simulation includes the fact that the data processing of the wide range
stochastic will generate probable distribution among the processing of the sampling of the
quantitative repetitive simulation of the processing of the data management methodology. The
uncertainty of the random sampling includes the fact that the random sampling will be having
much higher probability regarding the processing of the evaluation of the complex systems or
processes. The quantitative behavior of the complete project management will include the

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process of the data management of the sensitivity analysis and numerical integration. Error
propagation has the highest probability in the processing of the data. This makes the entir process
to be complex and the process being complex enough takes more time in the completion of the
project. Nano biotechnology methods are used for the processing of the methodology which are
used for the problem solving facilities. The Monte Carlo method are also use by the scientists
and the engineers and the mathematicians for the business simulation.
According to Candon, the functioning of the dynamic real time scheduling is dependent
on the broad range of the systematic allocation of the resources. This ensures the fact that the
suitable proceeding resources will be refrained in the processing of the data management. The
complexity in the algorithm that is required for the processing of the data that is required in the
purpose of relocating the scheduling system. this ensures the fact that the main schedule is
performed in the processing of the real time lift scheduling. This will ensure the fact that the data
management of the lift scheduling will be done with utmost efficiency. This is done with the lift
monitoring system. This will ensure the fact that the complexity of the lift management is
processed. The algorithm that is used is the A* heuristic process, which is supported by real time
monitoring system. the traffic modeling takes into consideration the fact that the data processing
will cause the performance of the lift installation and the intelligent lift scheduling will deal with
the processing of the intelligent lift scheduling
The modern sea combat uses the processing that the adaptive system will involve a large
number of simulation and the complex processing of the typical adaptive simulation including
the traditional method. The first method implementation will include the fact that the simulation
of the GA will be processed with utmost efficiency. The qualitative structure of the simulation
system will provide agent involvement and the most important feature proving to be the agent

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involvement in the processing of the simulation. The decision making structure will deal with the
genetic algorithm in the processing of the simulation and the process is used in terms of VBA
excel. This helps in the processing of the disclosing of the rules including the warfare outcome.
One of the processes for molding is the injection molding procedure which is a major part
of the plastic industry and takes up around thirty two percent of the plastics around the world1.
There are two basic resources of the plastic material production and both are bound to deteriorate
with the passage of time. The two main resources involve the injection machine and the mould.
The literature of the various researches have led to the development of models that contribute to
the solving of the various problems related to manufacture of plastics. In the real scenario the
procedure of scheduling of the project and planning of the maintenance is decided in an
independent manner. The production schedules are most of the times disturbed by the failure of
the equipments and proper preventive measures should be taken to get rid of this issue. This in
turn will cause the maintenance cost to rise by leap and bounds2. This is where the importance of
maintenance planning comes to the picture. The maintenance scheduling has to be considered in
the work process of production. If the production process is considered without taking into
account maintenance parameter then the process of production will be interrupted. Studies have
been made on the scheduling process and a number of conclusions has been drawn regarding the
solutions. The maintenance procedure problem was carried out in a time frame in order to get rid
of the maintenance. The maintenance issue is solved by relating the job along with the
1Arima, Sumika, Yutong Zhang, Yutaka Akiyama, and Yuri Ishizaki. "Dynamic scheduling of product-mix
production systems of MTS and MTO." In e-Manufacturing and Design Collaboration (eMDC) & Semiconductor
Manufacturing (ISSM), 2017 Joint International Symposium on, pp. 1-4. IEEE, 2017.
2Froger, Aurélien, Michel Gendreau, Jorge E. Mendoza, Éric Pinson, and Louis-Martin Rousseau. "Maintenance
scheduling in the electricity industry: A literature review." European Journal of Operational Research 251, no. 3
(2016): 695-706.

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maintenance schedule. The time of the maintenance depends on the running time of the factory
resources during the process of production. With the passage of time the manufacture process
starts to deteriorate because of the fall in the condition or the quality of the resources. When this
happens it takes a longer time and effort to maintain. For this reason the paper represents the idea
of joint scheduling.
The problem with the production maintenance process is that the consideration is taken for the
availability of the machine. However, the consideration for the availability of the other resources
which are important are not considered. The other resources involve the injection mould. In this
literature review the tool for the management of the mould is also considered3. The process of
tool scheduling is done in order to manage the tools that are important for plastic production. The
tool schedule is responsible for the for handling and keeping record of the number of tools, the
condition of these tools and the condition of these tools. There are researches that are related to
the tools of the plastic production. With the process of tool management, the number of repair or
replace is known in advance. There are fixed time intervals on which the maintenance is carried
3Ghamisi, Pedram, and Jon AtliBenediktsson. "Feature selection based on hybridization of genetic algorithm and
particle swarm optimization." IEEE Geoscience and Remote Sensing Letters 12, no. 2 (2015): 309-313.

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out. The process of tool management also sets the time interval4. This process makes an
assumption regarding the maintenance activities. The most important maintenance activities are
given the first priority and the least important maintenance activities are given less priority. The
start and the end times of the maintenance activities are given set by the tool management. The
combinational activities is more important than the traditional setting. The genetic algorithm has
been used in the paper in order to maintain the plastic productivity. The genetic algorithm does
all the process of maintenance of the plastic process. The genetic algorithm follows some of the
methodologies such as the encoding of the chromosome, basic genetic operation and the
crossover operation.
Effort has been made in order to involve the maintenance factor in production. This was not
possible in the single handed machine scheduling problem. There were certain solutions which
were suggested such as the bound algorithm. The implementation of this process deals with the
reduction of the total time that is necessary for the completion of the process5. In the other
procedure denoting genetic algorithm the machines must be maintained at a regular interval. The
genetic process reduces the machine scheduling process6. There are a number of problems that
4Qiu, Meikang, Zhong Ming, Jiayin Li, KekeGai, and ZiliangZong. "Phase-change memory optimization for green
cloud with genetic algorithm." IEEE Transactions on Computers 64, no. 12 (2015): 3528-3540.
5Fitouhi, Mohamed-Chahir, and Mustapha Nourelfath. "Integrating noncyclical preventive maintenance scheduling
and production planning for multi-state systems." Reliability Engineering & System Safety 121 (2014): 175-186.
6Nourmohammadzadeh, Abtin, and Sven Hartmann. "Fault classification of a centrifugal pump in normal and noisy
environment with artificial neural network and support vector machine enhanced by a genetic algorithm."
In International Conference on Theory and Practice of Natural Computing, pp. 58-70. Springer, Cham, 2015.

8GA TECHNOLOGIES
can be reduced by the process of joint scheduling problem. By this process the idle time of the
machine may be reduced. The scheduling production with the scheduling of the mould consist of
M number of injection and N number of injection moulds. Each machine, m can perform various
jobs j with a specific number of mould n. The operating time of the process may be represented
by L. When a particular injection mould is loaded then the machine would be performing the
same operation as the other machines. Each machine are being subjected to the maximum age,
machine age and mould age. Age is represented as an operating time of a resource and it is
represented in a cumulative manner. All the jobs that are carried out are available for processing
in the zero time frame. The job splitting is not possible in this genetic process.
According to Serrador, Pedro, and Rodney Turner, the resource that is constrained in the
projectdeals with the fact that the prosecution of the scheduling o the project terminologies. The
project plan is minimized under to complete the project with utmost efficiency. This will ensure
the fact that the projection of the project will ensure the fact that the span required for the
procession of the project will be minimized. The resource renewal will include the fact that the
activities are accomplished in a proper way and the entire project is accomplishedin positive
way. The most important task of the object manager is storedschedule the project, leading to the
fact that the time consumption of the process is checked and the processing of the data
management will process the entire project will be completed with efficiency.
According to Candon, the resource constrained is used for the purposing of the resource
constrained schedule problem. This fact ensures that the processing the project will contain the
labeling in terms of ‘j’ and is considered to be present in the range between (1. . . . j). ‘ j ‘ is
considered to be the analogous representative of the process that must not start before the ending

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of the entire project processing, leading of the fact that j cats as the closing factor of the project7.
The set of actions that provide dummy during the functioning of the entire business processing is
termed to be K. the perperiodavailability constant is proponed by Rk. The parameters are
denoted to be not regular in nature, leading to the fact that the activity will not be interrupted.
According to Joulin, Armand, and Tomas Mikolov, the genetic algorithms are applied on
the principles of the processing of the data with the help of the processing of the biological
valuation to evolve the processing of date data, which will include the usage of thetechnology of
survival of the fittest. This will include the fact that the processing of the data is described as the
subsection of 3:3. The extraction of the data from the section is termed as POP. After the
application of POP operator, the entire process gets diversified n the processing of the project
management. After getting the next generation, the crossover of the projection starts again.
According to Davari, Morteza, and Erik Demeulemeester, the organizational problems
will include the probation of the Gas are not allowed to operate directly , leading to the genetic
operator modification. This make the problem are specific. Hartman, in his solution sated that the
progression of the following task is maintained in a process that ensures the fact that the
decoding of the process is performed. The activity list must be procured to be specific in nature
leading to the scheduling of the decoding procedure.Each and every activity that is assigned take
into consideration the representation of the RCPSP. The dummy sink activity does not take much
time and the time that is incurred in the process is 0. The activity of the scheduling is started,
leading to the fact that the data processing is ensured and the finishing time is considered to be
the procession of the entire project.
7Serrador, Pedro, and Rodney Turner. "The relationship between project success and project
efficiency." Project Management Journal 46, no. 1 (2015): 30-39.

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According to Sparaciari, Carlo, Stefano Olivares, and Matteo GA Paris, the impossibility
regarding the performance of the SGS, the presence of the arbitrary RCPSP is observed. The
decoding of the GA ensures the fact that the processing of the decoded procedure is transcript in
the processing of the GA8. This factr takes into consideration the fact that the procession will
include the extension of the processing of the SGS will perform better and in a more efficient
processing. This ensures the fact that the data processing of the GA is very efficiency. This
ensures that the GA adapts itself in the processing of the data transcription f the business
management. This process ensures the fact that is arbitrary in nature. The type leading is used for
the generation of the dynamically instance of the self adapting GA.
According to Serrador, Pedro, and Rodney Turner, the most straight forward process
deals with the methodology of the processing f the serial processing of the project. This parallel
recommending of the processing endures the factors regarding motivation of the speaking of the
clients9. This fact ensures that similarity of the SGS population is checked and the correct
detection procedures are made. The low resourcecapacity denotes the capacitive processing of
the data management. The probability of selecting the RS is very less and the probability lies
between 0 and 1. The low resource strength is the main reason behind the low probability of the
less selection of the RS.
Conclusion:
The above study shows how Monte Carlo simulation has been using statistical and
sampling methods for estimating mathematical functions and operations of complicated systems.
8Candon,. "Primary Care Appointment Availability and the ACA Insurance Expansions." (2017)
9Davari, Morteza, and Erik Demeulemeester. "A novel branch-and-bound algorithm for the
chance-constrained RCPSP." (2016).

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The report has helped in understanding that the tool is essential to investigate intractable
problems analytically. The discussion proves that in case all the results are obtained in one single
model, every outcome is not needed to be calculated. The method exploits randomness to arrive
at the desired outcomes. It can be agreed that Monte Carlo simulation is much more effective
than other modes of algorithms. Notably, it has been deriving outcomes that are found
complicated and impossible to practice. Moreover, they are distinctive in their dependency on
advanced computational techniques of the current age.
Reflection:
Monte Carlo simulations, as I urge, is merely a complicated argument. It has no epistemic
power beyond its arguments. I can say that Monte Carlo simulations are useful so far as they
differ from the standard methods of analysis. My above conclusion has merely identified how
they have been similar. I have understood that those epistemic outcomes provided by the
simulations have been highly pragmatic. They have been opening no more epistemic channel.
However, I cannot deny the fact that the data generated through this process is helpful to create
graphs of various outcomes and different scopes of occurrences. Furthermore, it has been
possible to model multiple interdependent relationships between different input variables.
Besides, the method has helped me to analyse precisely which input have been possessing what
value as any result is derived.

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Reference:
Serrador, Pedro, and Rodney Turner. "The relationship between project success and project
efficiency." Project Management Journal 46, no. 1 (2015): 30-39.
Candon,. "Primary Care Appointment Availability and the ACA Insurance Expansions." (2017).
Joulin, Armand, and Tomas Mikolov. "Inferring algorithmic patterns with stack-augmented
recurrent nets." In Advances in neural information processing systems, pp. 190-198. 2015.
Davari, Morteza, and Erik Demeulemeester. "A novel branch-and-bound algorithm for the
chance-constrained RCPSP." (2016).
Sparaciari, Carlo, Stefano Olivares, and Matteo GA Paris. "Gaussian-state interferometry with
passive and active elements." Physical Review A 93, no. 2 (2016): 023810.
Froger, Aurélien, Michel Gendreau, Jorge E. Mendoza, Éric Pinson, and Louis-Martin Rousseau.
"Maintenance scheduling in the electricity industry: A literature review." European Journal of
Operational Research 251, no. 3 (2016): 695-706.
Ghamisi, Pedram, and Jon AtliBenediktsson. "Feature selection based on hybridization of
genetic algorithm and particle swarm optimization." IEEE Geoscience and Remote Sensing
Letters 12, no. 2 (2015): 309-313.
Nourmohammadzadeh, Abtin, and Sven Hartmann. "Fault classification of a centrifugal pump in
normal and noisy environment with artificial neural network and support vector machine
enhanced by a genetic algorithm." In International Conference on Theory and Practice of
Natural Computing, pp. 58-70. Springer, Cham, 2015.

13GA TECHNOLOGIES
Qiu, Meikang, Zhong Ming, Jiayin Li, KekeGai, and ZiliangZong. "Phase-change memory
optimization for green cloud with genetic algorithm." IEEE Transactions on Computers 64, no.
12 (2015): 3528-3540.
Fitouhi, Mohamed-Chahir, and Mustapha Nourelfath. "Integrating noncyclical preventive
maintenance scheduling and production planning for multi-state systems." Reliability
Engineering & System Safety 121 (2014): 175-186.
Arima, Sumika, Yutong Zhang, Yutaka Akiyama, and Yuri Ishizaki. "Dynamic scheduling of
product-mix production systems of MTS and MTO." In e-Manufacturing and Design
Collaboration (eMDC) & Semiconductor Manufacturing (ISSM), 2017 Joint International
Symposium on, pp. 1-4. IEEE, 2017.