1) The document describes a simple simulation model of a single drill press processing blank parts. Parts arrive and either get processed immediately if the drill press is idle or wait in a FIFO queue.
2) The goal is to calculate performance measures like total production, average and maximum wait times, utilization over a 20 minute simulation.
3) Queueing theory cannot be directly used to calculate these measures because the model assumptions do not match reality and it only provides long-run averages rather than the desired 20 minute period results.
Objeto de conferencia
VI International Conference on Education and Information Systems, Technologies and Applications (EISTA) (Florida, Estados Unidos)
Simulation is the process of executing a model, that is a representation of a system with enough detail to describe it but not too excessive. This model has a set of entities, an internal state, a set of input variables that can be controlled and others that cannot, a list of processes that bind these input variables with the entities and one or more output values, which result from the execution of events.
Running a model is totally useless if it can not be analyzed, which means to study all interactions among input variables, model entities and their weight in the values of the output variables. In this work we consider Discrete Event Simulation, which means that the status of the system variables being simulated change in a countable set of instants, finite or countable infinite.
Existing GPSS implementations and IDE's provide a wide range of tools for analysis of the results, for generation and execution of experiments and to perform complex analysis (such as Analysis of Variance, screening and so on). This is usually enough for most common analysis, but more detailed information and much more specific analysis are often required.
In addition, teaching this kind of simulation languages is always a challenge, since the way it executes the models and the abstraction level that their entities achieve is totally different compared to general purpose programming languages, well known by most students of the area. And this is usually hard for students to understand how everything works underground.
We have developed an open source simulation framework that implements a subset of entities of GPSS language, which can be used for students to improve the understanding of these entities. This tool has also the ability to store all entities of simulations in every single simulation time, which is very useful for debugging simulations, but also for having a detailed history of all entities (permanents and temporary) in the simulations, knowing exactly how they have behaved in every simulation time.
In this paper we provide an overview of this development, making special stress on the simulation model design and on the persistence of simulation entities, which are the basis that students and researchers of the area need in order to extend the model, adapt it to their needs or add all analysis tools to the framework.
Ver registro completo en: http://sedici.unlp.edu.ar/handle/10915/5557
Objeto de conferencia
VI International Conference on Education and Information Systems, Technologies and Applications (EISTA) (Florida, Estados Unidos)
Simulation is the process of executing a model, that is a representation of a system with enough detail to describe it but not too excessive. This model has a set of entities, an internal state, a set of input variables that can be controlled and others that cannot, a list of processes that bind these input variables with the entities and one or more output values, which result from the execution of events.
Running a model is totally useless if it can not be analyzed, which means to study all interactions among input variables, model entities and their weight in the values of the output variables. In this work we consider Discrete Event Simulation, which means that the status of the system variables being simulated change in a countable set of instants, finite or countable infinite.
Existing GPSS implementations and IDE's provide a wide range of tools for analysis of the results, for generation and execution of experiments and to perform complex analysis (such as Analysis of Variance, screening and so on). This is usually enough for most common analysis, but more detailed information and much more specific analysis are often required.
In addition, teaching this kind of simulation languages is always a challenge, since the way it executes the models and the abstraction level that their entities achieve is totally different compared to general purpose programming languages, well known by most students of the area. And this is usually hard for students to understand how everything works underground.
We have developed an open source simulation framework that implements a subset of entities of GPSS language, which can be used for students to improve the understanding of these entities. This tool has also the ability to store all entities of simulations in every single simulation time, which is very useful for debugging simulations, but also for having a detailed history of all entities (permanents and temporary) in the simulations, knowing exactly how they have behaved in every simulation time.
In this paper we provide an overview of this development, making special stress on the simulation model design and on the persistence of simulation entities, which are the basis that students and researchers of the area need in order to extend the model, adapt it to their needs or add all analysis tools to the framework.
Ver registro completo en: http://sedici.unlp.edu.ar/handle/10915/5557
Innoslate is a full lifecycle systems engineering tool that provides you with the capability to perform requirements analysis, functional and physical modeling, simulation, testing, and more all in one place.
Modeling and simulation is the use of models as a basis for simulations to develop data utilized for managerial or technical decision making. In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model.
Evaluation of the Process of Attention using the Simulation of Processesijtsrd
The present work aims to perform an evaluation of the customer service process in the service area, for which a study using a reliability of 90% with the process simulation technique was carried out. A descriptive - analytical methodology was developed with a sample of 68 time points for each area of the care process. The results obtained helped to measure the productivity of the service process, resulting in a productivity of 89.31%. Muñoz Martiñon Rodolfo, | Robles RamÃrez Diana P. | Vanessa Zamudio Hidalgo"Evaluation of the Process of Attention using the Simulation of Processes" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2231.pdf http://www.ijtsrd.com/other-scientific-research-area/other/2231/evaluation-of-the-process-of-attention-using-the-simulation-of-processes/muñoz-martiñon-rodolfo
Innoslate is a full lifecycle systems engineering tool that provides you with the capability to perform requirements analysis, functional and physical modeling, simulation, testing, and more all in one place.
Modeling and simulation is the use of models as a basis for simulations to develop data utilized for managerial or technical decision making. In the computer application of modeling and simulation a computer is used to build a mathematical model which contains key parameters of the physical model.
Evaluation of the Process of Attention using the Simulation of Processesijtsrd
The present work aims to perform an evaluation of the customer service process in the service area, for which a study using a reliability of 90% with the process simulation technique was carried out. A descriptive - analytical methodology was developed with a sample of 68 time points for each area of the care process. The results obtained helped to measure the productivity of the service process, resulting in a productivity of 89.31%. Muñoz Martiñon Rodolfo, | Robles RamÃrez Diana P. | Vanessa Zamudio Hidalgo"Evaluation of the Process of Attention using the Simulation of Processes" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-1 | Issue-5 , August 2017, URL: http://www.ijtsrd.com/papers/ijtsrd2231.pdf http://www.ijtsrd.com/other-scientific-research-area/other/2231/evaluation-of-the-process-of-attention-using-the-simulation-of-processes/muñoz-martiñon-rodolfo
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Sachpazis:Terzaghi Bearing Capacity Estimation in simple terms with Calculati...Dr.Costas Sachpazis
Terzaghi's soil bearing capacity theory, developed by Karl Terzaghi, is a fundamental principle in geotechnical engineering used to determine the bearing capacity of shallow foundations. This theory provides a method to calculate the ultimate bearing capacity of soil, which is the maximum load per unit area that the soil can support without undergoing shear failure. The Calculation HTML Code included.
Courier management system project report.pdfKamal Acharya
It is now-a-days very important for the people to send or receive articles like imported furniture, electronic items, gifts, business goods and the like. People depend vastly on different transport systems which mostly use the manual way of receiving and delivering the articles. There is no way to track the articles till they are received and there is no way to let the customer know what happened in transit, once he booked some articles. In such a situation, we need a system which completely computerizes the cargo activities including time to time tracking of the articles sent. This need is fulfilled by Courier Management System software which is online software for the cargo management people that enables them to receive the goods from a source and send them to a required destination and track their status from time to time.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Overview of the fundamental roles in Hydropower generation and the components involved in wider Electrical Engineering.
This paper presents the design and construction of hydroelectric dams from the hydrologist’s survey of the valley before construction, all aspects and involved disciplines, fluid dynamics, structural engineering, generation and mains frequency regulation to the very transmission of power through the network in the United Kingdom.
Author: Robbie Edward Sayers
Collaborators and co editors: Charlie Sims and Connor Healey.
(C) 2024 Robbie E. Sayers
Democratizing Fuzzing at Scale by Abhishek Aryaabh.arya
Presented at NUS: Fuzzing and Software Security Summer School 2024
This keynote talks about the democratization of fuzzing at scale, highlighting the collaboration between open source communities, academia, and industry to advance the field of fuzzing. It delves into the history of fuzzing, the development of scalable fuzzing platforms, and the empowerment of community-driven research. The talk will further discuss recent advancements leveraging AI/ML and offer insights into the future evolution of the fuzzing landscape.
Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
TECHNICAL TRAINING MANUAL GENERAL FAMILIARIZATION COURSEDuvanRamosGarzon1
AIRCRAFT GENERAL
The Single Aisle is the most advanced family aircraft in service today, with fly-by-wire flight controls.
The A318, A319, A320 and A321 are twin-engine subsonic medium range aircraft.
The family offers a choice of engines
Cosmetic shop management system project report.pdfKamal Acharya
Buying new cosmetic products is difficult. It can even be scary for those who have sensitive skin and are prone to skin trouble. The information needed to alleviate this problem is on the back of each product, but it's thought to interpret those ingredient lists unless you have a background in chemistry.
Instead of buying and hoping for the best, we can use data science to help us predict which products may be good fits for us. It includes various function programs to do the above mentioned tasks.
Data file handling has been effectively used in the program.
The automated cosmetic shop management system should deal with the automation of general workflow and administration process of the shop. The main processes of the system focus on customer's request where the system is able to search the most appropriate products and deliver it to the customers. It should help the employees to quickly identify the list of cosmetic product that have reached the minimum quantity and also keep a track of expired date for each cosmetic product. It should help the employees to find the rack number in which the product is placed.It is also Faster and more efficient way.
Quality defects in TMT Bars, Possible causes and Potential Solutions.PrashantGoswami42
Maintaining high-quality standards in the production of TMT bars is crucial for ensuring structural integrity in construction. Addressing common defects through careful monitoring, standardized processes, and advanced technology can significantly improve the quality of TMT bars. Continuous training and adherence to quality control measures will also play a pivotal role in minimizing these defects.
2. Fundamental Simulation Concepts
2
A Simple Processing System
“Blank” parts arrive to a drilling center
processed by a single drill press, and then leave
If a part arrives and finds the drill press idle, its processing at the drill press starts right away; otherwise, it waits in a
First-In First-Out (FIFO), that is, first-come first-served queue. This is the logical structure of the model.
3. Fundamental Simulation Concepts
3
The system starts at time 0 minutes with no parts present and
the drill press idle.
We’ve decided that the simulation will
stop at exactly time 20 minutes.
4. Fundamental Simulation Concepts
4
Our goal is to calculate some performance
measures as follows:
The total production (number of parts that complete their service at the drill press and leave)
during the 20 minutes of operation. Presumably, more is better.
1
2 The average waiting time in queue of parts that enter service at the drill press during the simulation.
3
From a performance standpoint, small is good.
The maximum waiting time in queue of parts that enter service at the drill press during the simulation.
Small is good.
4 The time-average number of parts waiting in the queue
This one indicates how long the queue is (on average), which might be of interest for allocating
floor space.
5. Fundamental Simulation Concepts
5
Our goal is to calculate some performance
measures as follows (Continue):
The maximum number of parts that were ever waiting in the queue .
The average and maximum total time in system of parts that finish being processed on the drill press and leave.
Also called cycle time , this is the time that elapses between a part’s arrival and its departure,
The utilization of the drill press, defined as the proportion of time it is busy during the simulation.
8. Fundamental Simulation Concepts
8
Since this is a queue, why queueing theory is not suitable for
calculating mentioned performance measurements:
It’s been around for a century, and a lot of very bright people have worked hard to develop it.
In some situations, it can result in simple formulas from which you can get a lot of insight.
Probably the simplest and most popular object of queueing theory is the M/M/1 queue . The first “M” states that the
arrival process is Markovian ; that is, the interarrival times are independent and identically distributed “draws” from
an exponential probability distribution.
The second “M” stands for the service-time distribution, and here it’s also exponential. The “1” indicates that there’s just
a single server. So at least on the surface this looks pretty good for our model.
Better yet, most of our output performance measures can be expressed as
simple formulas. For instance, the average waiting time in queue (expected
from a long run) is
expected value of the
interarrival-time distribution
the expected value of the
service-time distribution
9. Fundamental Simulation Concepts
9
Such an approach can sometimes give a reasonable order-of-magnitude approximation that might facilitate crude
comparisons. But there are problems too
The estimates of 𝜇𝐴 and 𝜇𝑆 aren’t exact, so there will be error in the result as well.
The assumptions of exponential interarrival-time and service-time distributions are essential to deriving the
preceding formula, and we probably don’t satisfy these assumptions. This calls into question the validity of the
formula. While there are more sophisticated versions for more general queueing models, there will always be
assumptions to worry about.
The formula is for long-run performance, not the 20-minute period we want. This is typical of most (but not all)
queueing theory.
The formula doesn’t provide any information on the natural variability in the system. This is not only a difficulty
in analysis but might also be of inherent interest itself, as in the variability of production. (It’s sometimes possible,
though, to find other formulas that measure variability.)
10. Pieces of a Simulation Model
10
Entities
Most simulations involve “players” called entities that move around, change status, affect
and are affected by other entities and the state of the system, and affect the output
performance measures.
Entities are the dynamic objects in the simulation—they usually are created, move around
for a while, and then are disposed of as they leave. It’s possible, though, to have entities that
never leave but just keep circulating in the system.
For example:
In bank, customers are entities.
In a production line, products are entities
In a gas station, cars are entities
11. Pieces of a Simulation Model
11
Attributes
To individualize entities, you attach attributes to them. An attribute is a common
characteristic of all entities, but with a specific value that can differ from one entity
to another. For instance, our part entities could have attributes called Due Date,
Priority, and Color to indicate these characteristics for each individual entity. It’s up
to you to figure out what attributes your entities need, name them, assign values to
them, change them as appropriate, and then use them when it’s time (that’s all
part of modeling).
The most important thing to remember about attributes is that their values are tied to
specific entities. The same attribute will generally have different values for different
entities, just as different parts have different due dates, priorities, and color codes.
Think of an attribute as a tag attached to each entity, but what’s written on this tag
can differ across entities to characterize them individually.
Arena keeps track of some attributes automatically, but you may need to define,
assign values to, change, and use attributes of your own.
12. Pieces of a Simulation Model
12
(Global)
Variables
A variable (or a global variable) is a piece of information that reflects some characteristic
of your system, regardless of how many or what kinds of entities might be around.
There are two types of variables: Arena built-in variables (number in queue, number of
busy servers, current simulation clock time, and so on) and user-defined variables (mean
service time, travel time, current shift, and so on).
Variables are used for lots of different purposes. For instance, the time to move between any two
stations in a model might be the same throughout the model, and a variable called Transfer Time
could be defined and set to the appropriate value and then used wherever this constant is needed
Some built-in Arena variables for our model include the status of the drill press (busy or idle), the time
(simulation clock), and the current length of the queue.
13. Pieces of a Simulation Model
13
Resources
Entities often compete with each other for service from resources that
represent things like personnel, equipment, or space in a storage area of
limited size.
An entity seizes (units of) a resource when available and releases it (or
them) when finished. It’s better to think of the resource as being given to
the entity rather than the entity being assigned to the resource since an
entity (like a part) could need simultaneous service from multiple resources.
In our example, there is just a single drill press, so this resource has a
single unit available at all times.
14. Pieces of a Simulation Model
14
Queues
When an entity can’t move on, perhaps because it needs to
seize a unit of a resource that’s tied up by another entity, it
needs a place to wait, which is the purpose of a queue. In
Arena, queues have names and can also have capacities to
represent, for instance, limited floor space for a buffer.
You’d have to decide as part of your modeling how to
handle an entity arriving at a queue that’s already full.
15. Pieces of a Simulation Model
15
Statistical
Accumulators
To get your output performance measures, you have to keep track of various intermediate
statistical-accumulator variables as the simulation progresses. In our example, we’ll watch:
The number of parts produced so far
The total of the waiting times in queue so far
The number of parts that have passed through the queue so far (since we’ll need this as the
denominator in the average waiting-time output measure)
The longest time spent in queue we’ve seen so far
The total of the time spent in the system by all parts that have departed so far
The longest time in system we’ve seen so far
The area so far under the queue-length curve 𝑄(𝑡)
The highest level that 𝑄(𝑡) has so far attained
The area so far under the server-busy function
16. Overview of a Simulation Study
16
In deciding how to model a system, you’ll find that issues related to design and analysis and
representing the model in the software certainly are essential to a successful simulation study
Understand the system
Whether or not the system exists, you must have an intuitive, down-to-earth feel for what’s going on.
This will entail site visits and involvement of people who work in the system on a day-to-day basis.
Be clear about your goals
Realism is the watchword here; don’t promise the sun, moon, and stars. Understand what can be
learned from the study, and expect no more.
Formulate the model representation
What level of detail is appropriate? What needs to be modeled carefully and what can be dealt with in
a fairly crude, high level manner? Get buy-ins to the modeling assumptions from management and
those in decision-making positions.
17. Overview of a Simulation Study
17
Translate into modeling software
Once the modeling assumptions are agreed upon, represent them faithfully in the simulation software.
If there are difficulties, be sure to manage them out in an open and honest way rather than burying
them. Involve those who really know what’s going on (animation can be a big help here).
Validate the model
Do the input distributions match what you’ve observed in the field? Do the output performance
measures from the model match up with those from reality? While statistical tests can be carried out
here, a good dose of common sense is also valuable.
Design the experiments
Plan out what it is you want to know and how your simulation experiments will get you to the
answers in a precise and efficient way. Often, principles of classical statistical experimental design
can be of great help here.
18. Overview of a Simulation Study
18
Analyze your results
Carry out the right kinds of statistical analyses to be able to make accurate and precise statements.
This is clearly tied up intimately with the design of the simulation experiments.
Get insight
This is far more easily said than done. What do the results mean at the gut level? Does it all make
sense? What are the implications? What further questions (and maybe simulations) are suggested by
the results? Are you looking at the right set of performance measures?
Document what you’ve done
You’re not going to be around forever, so make it easier on the next person to understand what you’ve
done and to carry things further. Documentation is also critical for getting management buy-in and
implementation of the recommendations you’ve worked so hard to be able to make with precision and
confidence.