Simulation

1. Introduction
A process model is a computer representation of a real world system or process.
For our purposes, we are restricting the topic to models for manufacturing and
manufacturing-related processes, such as batch documentation, material
replenishment or warehousing, and quality testing laboratories. The main purpose of
the computer model is to act as a substitute for the real thing. It may be too
expensive or disruptive to experiment on the real process or perhaps the real
process does not yet exist and is still being designed. Regardless the scenario, a
computer model can perform experiments or 'what-if' studies that can add to your
understanding of the real world system and can identify and compare alternatives
that improve the system in some way, usually to reduce costs or increase the
throughput.
Another argument for using computer-based modeling is that the real world
system is often very complicated involving many interactions between variables that
may be unknown or poorly defined with significant variability that tends to hide the
underlying relationships. A good model is a simplified version of the true system that
captures the essential relationships while leaving out unimportant details. If
variability exists and can be quantified, then this can be included in the model to
provide a more realistic result.
Definitions
Simulation is sometimes used interchangeably with modeling, but really,
simulation is the result of running a model. The model comes first; that model is
then used to perform simulation studies. Typically, you are using a model to either
reproduce a historical period (for validation purposes) or to extrapolate data to
predict the future (for what-if studies). You may perform many simulations with a
single model exploring additional alternatives or replication with each simulation.

2. Benefits of Simulation
We categorize the benefits according to the situation in which a model is developed
and used:
1.Design phase
Facility is being designed or will soon be designed. Best to start as early as possible in
the design process. Changes to the design, prompted by simulation results, are more
cheaply made early in the design.
2.Renovation
A facility exists already and it needs to be improved in some way. Typical objectives
are to increase throughput, reduce manufacturing costs, lower inventories, or some
combination of these. Note that the improvements that come out of a study for an
existing facility may or may not include equipment changes. Sometimes
improvements can be made simply by changing the operating procedures or product
scheduling alone.
3.Design phase or new facility
Process modeling of a new design can provide the right number and size of
necessary processing and supporting equipment. This is particularly valuable for
equipment that is shared by multiple unit procedures such as CIP skids, utility system
designs and powder bins and tablet totes in an OSD facility. The overall production
process may have an equipment bottleneck; the model will identify the bottleneck
and ensure that it does not constrain production to a value less than the business
objectives for the facility. Also, the model will examine other potential bottlenecks. If

3. their constraint is close to the desired bottleneck, then schedule disruptions or other
variability may result in a constraint that is lower than the design, effectively
substituting a more restrictive bottleneck for the desired one.
Changes occur frequently during the design process. Engineers find better
alternatives and management may change the product mix of volumes that the
facility will be expected to make. In these instances, having a process model of the
design is especially beneficial. The model can be used to evaluate the alternatives
and ensure that the changes are cost effective while maintaining the desired
throughput. Staffing levels and shift schedules can also be compared with a model.
This helps with cost justification of the design change.
4.Facility Renovation
A model of an existing facility can provide the same benefits as in a design model
with two important differences. First, there is historical data that can be used to give
a better model and to validate the performance of the model against. This helps to
prevent process modeling errors and provides greater confidence in the model
results. Secondly, the model is more likely to be used for operational decisions, such
as production schedules and shift schedules. Equipment changes are still possible,
but the scale of the possible changes may be lower if the facility footprint is fixed.
Intermediate inventories may be fixed for the same reason, creating other problems
for manufacturing. Models of existing facilities tend to be more realistic and detailed,
which requires more engineering time to build, validate and use the model.
STELLA SOFTWARE
STELLA is a short for Systems Thinking, Experimental Learning Laboratory with
Animation and also marketed as iThink.It is a visual programming
language for system dynamics modeling introduced by Barry Richmond in 1985. The
program, distributed by isee systems (formerly High Performance Systems) allows

4. users to run models created as graphical representations of a system using four
fundamental building blocks. STELLA has been used in academia as a teaching tool
and has been utilized in a variety of research and business applications. The program
has received positive reviews, being praised in particular for its ease of use and low
cost.
The objective of simulation is to observe the equilibrium resulted from the
relationships rat and relationships of rat and owl. The objective of this simulation is
also to perceive the long concerned with these three entities.
SIMULATED MODEL
A. Software of Simulation
This simulated model is developed by using Stella 8 software. It is a graphical
simulation program developed by Isee Systems Inc. in Windows platform.
B. Sector of Palm-Oil Resource
The initial value for overall palm was recorded. The calculation concerned in
determining the initial value of this palm resource is based on the information taken.
Second,the owl concerned as the predator to rat simulation is from species tytoalba
that is barn owl. The ection of this type of owl as the predator is due to its ability to
live with the diet of rats amounting. The estimation of rat nutrition by a couple of
owls and its one owlet was calculated. Owl is a medium size bird with 38 cm or 16 in
length of body, 106 cm opening of wing has long and strong-gripping legs. The
biological control cared is the usage of predator to control pest population in an area.
The rat is the pest, the owl is the predator and the palm-oil plantation provides the
food supply to the pest. These three elements are taken into considerations in this
simulation research.The objective of simulation is to observe the equilibrium

5. resulted from the relationships of palm-oil and rat and relationships of rat and owl.
The objective of this simulation is also to perceive the long-term effects.
How to use Stella Sofware

6. The original graph of biological control agent for controlling rat in palm ecosystem
Graph of high population of rats vs low population of owl and effects on palm ecosystem

7. Advantages of stella software
Some benefits for using STELLA are:
• the language increases the accuracy and clarity of verbal descriptions,ambiguities
diminish, and communication becomes much more efficient and effective.
• the software provides a check on intuition, and also provides a vehicle for building
an understanding of why.
• the tools facilitates putting together in an organized and clear way the qualitative
and quantitative approaches present in the CIR‐DSS framework
• the tool enables an easier operation, demonstration, and replication of the CIR‐
DSS framework, serving as the basis for analyzing different types of infrastructure.
The use of STELLA for the CIR‐DSS framework requires the use of several different
infrastructure templates to build the full model. The identified templates at present
Graph of low population of rats vs high population of owl and effects on palm ecosystem

8. are the Main Chain for the overall CIR‐DSS framework, the Attribute Tracking for the
overall resilience improvement goal of the infrastructure system, and the Relative
Attractiveness for identifying better projects choices to improve infrastructure
systems.
Disadvantages
Simulation does not generate optimal solutions. It may take a long time to develop a
good simulation model. In certain cases simulation models can be very expensive.
The decision-maker must provide all information (depending on the model) about
the constraints and conditions for examination, as simulation does not give the
answers by itself.
Why is simulation important in learning process?
Deep Learning

9. Instructional simulations have the potential to engage students in "deep learning"
that empowers understanding as opposed to "surface learning" that requires only
memorization. A good summary of how deep learning contrasts with surface
learning is given at the Engineering Subject Centre: Deep and Surface Approaches
to Learning. Deep learning means that students:
Learn scientific methods including :
 The importance of model building.
Experiments and simulations are the way scientists do their work. Using
instructional simulations gives students concrete formats of what it means to
think like a scientist and do scientific work.
 The relationships among variables in a model or models.
Simulation allows students to change parameter values and see what
happens. Students develop a feel for what variables are important and the
significance of magnitude changes in parameters.
 Data issues, probability and sampling theory.
Simulations help students understand probability and sampling theory.
Instructional simulations have proven their worth many times over in the
statistics based fields. The ability to match simulation results with an analytically
derived conclusion is especially valuable in beginning classes, where students
often struggle with sampling theory. Given the utility of data simulation, it is not
surprising that SERC has an existing module on teaching with data simulation.
 How to use a model to predict outcomes.
Simulations help students understand that scientific knowledge rests on the
foundation of testable hypotheses.
Learn to reflect on and extend knowledge by
 Actively engaging in student-student or instructor-student conversations needed to
conduct a simulation.
Instructional simulations by their very nature cannot be passive learning.
Students are active participants in selecting parameter values, anticipating
outcomes, and formulating new questions to ask.
 Transferring knowledge to new problems and situations

10. A well done simulation is constructed to include an extension to a new
problem or new set of parameters that requires students to extend what they
have learned in an earlier context.
 understanding and refining their own thought processes.
A well done simulation includes a strong reflection summary that requires
students to think about how and why they behaved as they did during the
simulation.
 seeing social processes and social interactions in action.
This is one of the most significant outcomes of simulation in social science
disciplines such as sociology and political science.
Simulation in future learning
Research on student learning maintains that teachers are the most important
school-related factor influencing student achievement (Edutopia, 2008). Teacher
education programs train our teachers, providing initial and ongoing support,
resources, and hands-on experience, to prepare them for their teaching careers.
These programs face at least two important challenges that call for a more
sophisticated education process. On one hand, teachers need an ever-growing set of
knowledge, skills, and attitudes to meet their responsibilities; on the other hand,
faced with decreased funding, increased regulation, and growing competition for
available teaching jobs, they must clearly demonstrate their competencies in
enhancing students’ learning (Girod & Girod, 2008).
Teaching Practice and the Practicum
Classroom teaching practice provides most student teachers with their first
experience in applying the knowledge and exercising the skills that they study. The
practicum is intended to give pre-service teachers the opportunity to develop
practical skills and knowledge, receive feedback from experts and professionals,
and gain experience with students and the school environment that can directly help
them to prepare for classroom teaching. Also, practicum experiences allow teacher
candidates to learnand grow in protected settings (Girod & Girod, 2008). Therefore,
field experiences are often identified as the most important aspect of teacher
education programs (Arnett & Freeburg, 2008; Phillion, Miller, & Lehman, 2005).

11. However, the practicum is fraught with difficulties, including a lack of appropriate
field placements, particularly for rural, special-needs, and rarely found conditions;
shortages of host teachers willing to provide their time and expertise; host teachers’
poor teaching practices, particularly with special-needs students; limited
opportunity for repeated practice; and poor integration with the university
curriculum (Billingsley & Scheuermann, 2014; Howey, 1996; McPherson,
Tyler-Wood, Mcenturff, & Peak, 2011; Wilson, Floden, & Ferrini-Mundy,
2001; Young, 1998). It is, therefore, important to consider ways to augment the
traditional practicum to enhance both the quantity and quality of students’
pre-service teaching experience.