Overview of Performance Evaluation
Intro & Objective
The Art of Performance Evaluation
Professional Organizations, Journals, and conferences.
Performance Projects
Common Mistakes and How to Avoid Them
Selection of Techniques and Metrics
Overview of Performance Evaluation
Intro & Objective
The Art of Performance Evaluation
Professional Organizations, Journals, and conferences.
Performance Projects
Common Mistakes and How to Avoid Them
Selection of Techniques and Metrics
A brief introduction to network simulation and the difference between simulator and emulator along with the most important types of simulations techniques.
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.
Improving layout and workload of manufacturing system using Delmia Quest simu...AM Publications
This paper describes a case study of analysis and optimization of the facility layout in a manufacturing cell
using a systematic search method and a Quest computer simulation model with graphical representation of the
manufacturing processes. The simulation model objective was to obtain Layout design to achieve a high productivity in the
flexible manufacturing system (FMS), to determine bottleneck locations and what the optimal batch size should be. The
Quest software proved to be a powerful tool in assessing what changes should be made to a manufacturing cell before
incurring manufacturing improvements and/or performing actual capital investments. The aim of this study is to get
an understanding of the cell and its behaviour regarding production and to use the simulation software to change,
analyse and improve the cell.
SIMULATION-BASED OPTIMIZATION USING SIMULATED ANNEALING FOR OPTIMAL EQUIPMENT...Sudhendu Rai
The paper describes a software toolkit that enables the data-driven simulation-based optimization of print shops It enables quick modeling of complex print production environments under the cellular production framework. The software toolkit automates several steps of the modeling process by taking declarative inputs from the end-user and then automatically generating complex simulation models that are used to determine improved design and operating points. This paper describes the addition of another layer of automation consisting of simulation-based optimization using simulated-annealing that enables automated search of a large number of design alternatives in the presence of operational constraints to determine a cost-optimal solution. The results of the application of this approach to a real-world problem are also described.
Dynamic Simulation of Construction Machinery: Towards an Operator ModelReno Filla
In dynamic simulation of complete wheel loaders, one interesting aspect, specific for the working task, is the momentary power distribution between drive train and hydraulics, which is balanced by the operator.
This paper presents the initial results to a simulation model of a human operator. Rather than letting the operator model follow a predefined path with control inputs at given points, it follows a collection of general rules that together describe the machine's working cycle in a generic way. The advantage of this is that the working task description and the operator model itself are independent of the machine's technical parameters. Complete sub-system characteristics can thus be changed without compromising the relevance and validity of the simulation. Ultimately, this can be used to assess a machine's total performance, fuel efficiency and operability already in the concept phase of the product development process.
http://arxiv.org/abs/cs/0503087
RELIABILITY OF MECHANICAL SYSTEM OF SYSTEMScscpconf
In this paper, we present a new methodology about reliability of systems of systems. We present
also an example which combines the information transformation in complex systems and virtual
design of this system based on finite element analysis. This example is help to balance the
performances and the costs in complex system, or provide the optimal solution in manufacturing
design. It can also update the existing design of component by changing the new design of this
component.
Modeling and Analysis of Flexible Manufacturing System with FlexSimijceronline
Flexible manufacturing system (FMS) is a highly integrated manufacturing system. The relation between its components is very complex. The mathematical programming approaches are very difficult to solve for very complex system so the simulation of FMS is widely used to analyze its performance measures. Also the FMS components are very sophisticated and costly. If FMS has to be implemented then it is better to analyze its results using simulation which involves no loss of money, resource and labour time. As a typical discrete event system FMS have been studied in such aspects as modeling and performance analysis. In this paper, a concept and implementation of the Flexsim for measuring and analysis of performance measures of FMS is applied. The other well defined mathematical technique, i.e. bottleneck technique has also been applied for the purpose of comparison and verification of the simulation results. An example FMS has been taken into consideration and its flexsim model and mathematical model has been constructed. Several performance measures have been used to evaluate system performance. And it has been found that the simulation techniques are easy to analyze the complex fexible manufacturing system.
1 MODULE 1 INTRODUCTION TO SIMULATION Module out.docxjeremylockett77
1
MODULE 1: INTRODUCTION TO SIMULATION
Module outline:
• What is Simulation?
• Simulation Terminology
• Components of a System
• Models in Simulation
• Typical applications
• References
WHAT IS SIMULATION?
simulation may be defined as a technique that imitates the operation of a real world
system or processes as it evolves over time. It involves the generation of an artificial
history of the system and observation of that artificial history to obtain information and
draw inferences about the operating characteristics of the real system. Simulation
educates us on how a system operates and how the system might respond to changes. It
enables us to test alternative courses of action to determine their impact on system
performance. Before an alternative is implemented, it must be tested. Although
performing tests with the “real thing” would be ideal. This is seldom practically feasible.
The cost associated with changing/improving a system may be very high both in the
term of capital required to implement the change and losses due to interruption in
production operations and other losses. In most cases experimentation with the
proposed alternative is practically impossible. In addition, as the cost of proposed
changes (alternative solutions) increase, so does the cost of physically experimenting.
As an example, suppose a heavy-duty conveyor is being considered as an alternative to
the existing material handling method (by trucks) for improving productivity and
speeding up the production operations in a factory (seeFigre3). It is obvious that
installing the proposed conveyor on a test basis would probably not be cost effective.
Therefore, experimentation with alternative configurations would be practically
impossible. In stead, experimentation with a representative model of the system would
probably make more sense.
Simulation is a means of experimenting with a detailed model of a real system to
Determine how the system will respond to changes in its environment, structure, and its
underlying assumption [Harrel (1996)]. Management Scientist uses a wide variety of
analytical tools to model, analyze, and solve complex decision problems. These tool
include linear programming, decision analysis, forecasting, Queuing theory and
Alternative 1: Use lift-truck
2
Point A Point B
(Warehouse) (Factory)
Alternative 2: use a conveyor
Point A
(warehouse ) . . . . . . . . Point B
...
This presentation provides an introduction to quantitative trait loci (QTL) analysis and marker-assisted selection (MAS) in plant breeding. The presentation begins by explaining the type of quantitative traits. The process of QTL analysis, including the use of molecular genetic markers and statistical methods, is discussed. Practical examples demonstrating the power of MAS are provided, such as its use in improving crop traits in plant breeding programs. Overall, this presentation offers a comprehensive overview of these important genomics-based approaches that are transforming modern agriculture.
A brief introduction to network simulation and the difference between simulator and emulator along with the most important types of simulations techniques.
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.
Improving layout and workload of manufacturing system using Delmia Quest simu...AM Publications
This paper describes a case study of analysis and optimization of the facility layout in a manufacturing cell
using a systematic search method and a Quest computer simulation model with graphical representation of the
manufacturing processes. The simulation model objective was to obtain Layout design to achieve a high productivity in the
flexible manufacturing system (FMS), to determine bottleneck locations and what the optimal batch size should be. The
Quest software proved to be a powerful tool in assessing what changes should be made to a manufacturing cell before
incurring manufacturing improvements and/or performing actual capital investments. The aim of this study is to get
an understanding of the cell and its behaviour regarding production and to use the simulation software to change,
analyse and improve the cell.
SIMULATION-BASED OPTIMIZATION USING SIMULATED ANNEALING FOR OPTIMAL EQUIPMENT...Sudhendu Rai
The paper describes a software toolkit that enables the data-driven simulation-based optimization of print shops It enables quick modeling of complex print production environments under the cellular production framework. The software toolkit automates several steps of the modeling process by taking declarative inputs from the end-user and then automatically generating complex simulation models that are used to determine improved design and operating points. This paper describes the addition of another layer of automation consisting of simulation-based optimization using simulated-annealing that enables automated search of a large number of design alternatives in the presence of operational constraints to determine a cost-optimal solution. The results of the application of this approach to a real-world problem are also described.
Dynamic Simulation of Construction Machinery: Towards an Operator ModelReno Filla
In dynamic simulation of complete wheel loaders, one interesting aspect, specific for the working task, is the momentary power distribution between drive train and hydraulics, which is balanced by the operator.
This paper presents the initial results to a simulation model of a human operator. Rather than letting the operator model follow a predefined path with control inputs at given points, it follows a collection of general rules that together describe the machine's working cycle in a generic way. The advantage of this is that the working task description and the operator model itself are independent of the machine's technical parameters. Complete sub-system characteristics can thus be changed without compromising the relevance and validity of the simulation. Ultimately, this can be used to assess a machine's total performance, fuel efficiency and operability already in the concept phase of the product development process.
http://arxiv.org/abs/cs/0503087
RELIABILITY OF MECHANICAL SYSTEM OF SYSTEMScscpconf
In this paper, we present a new methodology about reliability of systems of systems. We present
also an example which combines the information transformation in complex systems and virtual
design of this system based on finite element analysis. This example is help to balance the
performances and the costs in complex system, or provide the optimal solution in manufacturing
design. It can also update the existing design of component by changing the new design of this
component.
Modeling and Analysis of Flexible Manufacturing System with FlexSimijceronline
Flexible manufacturing system (FMS) is a highly integrated manufacturing system. The relation between its components is very complex. The mathematical programming approaches are very difficult to solve for very complex system so the simulation of FMS is widely used to analyze its performance measures. Also the FMS components are very sophisticated and costly. If FMS has to be implemented then it is better to analyze its results using simulation which involves no loss of money, resource and labour time. As a typical discrete event system FMS have been studied in such aspects as modeling and performance analysis. In this paper, a concept and implementation of the Flexsim for measuring and analysis of performance measures of FMS is applied. The other well defined mathematical technique, i.e. bottleneck technique has also been applied for the purpose of comparison and verification of the simulation results. An example FMS has been taken into consideration and its flexsim model and mathematical model has been constructed. Several performance measures have been used to evaluate system performance. And it has been found that the simulation techniques are easy to analyze the complex fexible manufacturing system.
1 MODULE 1 INTRODUCTION TO SIMULATION Module out.docxjeremylockett77
1
MODULE 1: INTRODUCTION TO SIMULATION
Module outline:
• What is Simulation?
• Simulation Terminology
• Components of a System
• Models in Simulation
• Typical applications
• References
WHAT IS SIMULATION?
simulation may be defined as a technique that imitates the operation of a real world
system or processes as it evolves over time. It involves the generation of an artificial
history of the system and observation of that artificial history to obtain information and
draw inferences about the operating characteristics of the real system. Simulation
educates us on how a system operates and how the system might respond to changes. It
enables us to test alternative courses of action to determine their impact on system
performance. Before an alternative is implemented, it must be tested. Although
performing tests with the “real thing” would be ideal. This is seldom practically feasible.
The cost associated with changing/improving a system may be very high both in the
term of capital required to implement the change and losses due to interruption in
production operations and other losses. In most cases experimentation with the
proposed alternative is practically impossible. In addition, as the cost of proposed
changes (alternative solutions) increase, so does the cost of physically experimenting.
As an example, suppose a heavy-duty conveyor is being considered as an alternative to
the existing material handling method (by trucks) for improving productivity and
speeding up the production operations in a factory (seeFigre3). It is obvious that
installing the proposed conveyor on a test basis would probably not be cost effective.
Therefore, experimentation with alternative configurations would be practically
impossible. In stead, experimentation with a representative model of the system would
probably make more sense.
Simulation is a means of experimenting with a detailed model of a real system to
Determine how the system will respond to changes in its environment, structure, and its
underlying assumption [Harrel (1996)]. Management Scientist uses a wide variety of
analytical tools to model, analyze, and solve complex decision problems. These tool
include linear programming, decision analysis, forecasting, Queuing theory and
Alternative 1: Use lift-truck
2
Point A Point B
(Warehouse) (Factory)
Alternative 2: use a conveyor
Point A
(warehouse ) . . . . . . . . Point B
...
This presentation provides an introduction to quantitative trait loci (QTL) analysis and marker-assisted selection (MAS) in plant breeding. The presentation begins by explaining the type of quantitative traits. The process of QTL analysis, including the use of molecular genetic markers and statistical methods, is discussed. Practical examples demonstrating the power of MAS are provided, such as its use in improving crop traits in plant breeding programs. Overall, this presentation offers a comprehensive overview of these important genomics-based approaches that are transforming modern agriculture.
Embracing GenAI - A Strategic ImperativePeter Windle
Artificial Intelligence (AI) technologies such as Generative AI, Image Generators and Large Language Models have had a dramatic impact on teaching, learning and assessment over the past 18 months. The most immediate threat AI posed was to Academic Integrity with Higher Education Institutes (HEIs) focusing their efforts on combating the use of GenAI in assessment. Guidelines were developed for staff and students, policies put in place too. Innovative educators have forged paths in the use of Generative AI for teaching, learning and assessments leading to pockets of transformation springing up across HEIs, often with little or no top-down guidance, support or direction.
This Gasta posits a strategic approach to integrating AI into HEIs to prepare staff, students and the curriculum for an evolving world and workplace. We will highlight the advantages of working with these technologies beyond the realm of teaching, learning and assessment by considering prompt engineering skills, industry impact, curriculum changes, and the need for staff upskilling. In contrast, not engaging strategically with Generative AI poses risks, including falling behind peers, missed opportunities and failing to ensure our graduates remain employable. The rapid evolution of AI technologies necessitates a proactive and strategic approach if we are to remain relevant.
Students, digital devices and success - Andreas Schleicher - 27 May 2024..pptxEduSkills OECD
Andreas Schleicher presents at the OECD webinar ‘Digital devices in schools: detrimental distraction or secret to success?’ on 27 May 2024. The presentation was based on findings from PISA 2022 results and the webinar helped launch the PISA in Focus ‘Managing screen time: How to protect and equip students against distraction’ https://www.oecd-ilibrary.org/education/managing-screen-time_7c225af4-en and the OECD Education Policy Perspective ‘Students, digital devices and success’ can be found here - https://oe.cd/il/5yV
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
Power-sharing Class 10 is a vital aspect of democratic governance. It refers to the distribution of power among different organs of government, levels of government, and social groups. This ensures that no single entity can control all aspects of governance, promoting stability and unity in a diverse society.
For more information, visit-www.vavaclasses.com
Solid waste management & Types of Basic civil Engineering notes by DJ Sir.pptxDenish Jangid
Solid waste management & Types of Basic civil Engineering notes by DJ Sir
Types of SWM
Liquid wastes
Gaseous wastes
Solid wastes.
CLASSIFICATION OF SOLID WASTE:
Based on their sources of origin
Based on physical nature
SYSTEMS FOR SOLID WASTE MANAGEMENT:
METHODS FOR DISPOSAL OF THE SOLID WASTE:
OPEN DUMPS:
LANDFILLS:
Sanitary landfills
COMPOSTING
Different stages of composting
VERMICOMPOSTING:
Vermicomposting process:
Encapsulation:
Incineration
MANAGEMENT OF SOLID WASTE:
Refuse
Reuse
Recycle
Reduce
FACTORS AFFECTING SOLID WASTE MANAGEMENT:
2024.06.01 Introducing a competency framework for languag learning materials ...Sandy Millin
http://sandymillin.wordpress.com/iateflwebinar2024
Published classroom materials form the basis of syllabuses, drive teacher professional development, and have a potentially huge influence on learners, teachers and education systems. All teachers also create their own materials, whether a few sentences on a blackboard, a highly-structured fully-realised online course, or anything in between. Despite this, the knowledge and skills needed to create effective language learning materials are rarely part of teacher training, and are mostly learnt by trial and error.
Knowledge and skills frameworks, generally called competency frameworks, for ELT teachers, trainers and managers have existed for a few years now. However, until I created one for my MA dissertation, there wasn’t one drawing together what we need to know and do to be able to effectively produce language learning materials.
This webinar will introduce you to my framework, highlighting the key competencies I identified from my research. It will also show how anybody involved in language teaching (any language, not just English!), teacher training, managing schools or developing language learning materials can benefit from using the framework.
2. Introduction to Simulation
Simulation
the imitation of the operation of a real-world process or system over time
to develop a set of assumptions of mathematical, logical, and symbolic
relationship between the entities of interest, of the system.
to estimate the measures of performance of the system with the
simulation-generated data
Simulation modeling can be used
as an analysis tool for predicting the effect of changes to existing systems
as a design tool to predict the performance of new systems
Real-world
process concerning the behavior of a system
A set of assumptions
Modeling
& Analysis
2
8. When Simulation is the Appropriate Tool (1)
Simulation enables the study of, and experimentation with, the
internal interactions of a complex system, or of a subsystem
within a complex system.
Informational, organizational, and environmental changes can
be simulated, and the effect of these alterations on the model’s
behavior can be observed.
The knowledge gained in designing a simulation model may be
of great value toward suggesting improvement in the system
under investigation.
By changing simulation inputs and observing the resulting
outputs, valuable insight may be obtained into which variables
are most important and how variables interact.
Simulation can be used as a pedagogical device to reinforce
analytic solution methodologies.
8
9. Simulation can be used to experiment with new designs or
policies prior to implementation, so as to prepare for what
may happen.
Simulation can be used to verify analytic solutions.
By simulating different capabilities for a machine,
requirements can be determined.
Simulation models designed for training allow learning
without the cost and disruption of on-the-job learning.
Animation shows a system in simulated operation so that the
plan can be visualized.
The modern system (factory, wafer fabrication plant, service
organization, etc.) is so complex that the interactions can be
treated only through simulation.
When Simulation is the Appropriate Tool (2)
9
10. When Simulation is not Appropriate
When the problem can be solved using common sense.
When the problem can be solved analytically.
When it is easier to perform direct experiments.
When the simulation costs exceed the savings.
When the resources or time are not available.
When system behavior is too complex or can’t be defined.
When there isn’t the ability to verify and validate the model.
10
11. Advantages and Disadvantages of Simulation (1)
Advantages
New polices, operating procedures, decision rules, information flows,
organizational procedures, and so on can be explored without disrupting
ongoing operations of the real system.
New hardware designs, physical layouts, transportation systems, and so
on, can be tested without committing resources for their acquisition.
Hypotheses about how or why certain phenomena occur can be tested
for feasibility.
Insight can be obtained about the interaction of variables.
Insight can be obtained about the importance of variables to the
performance of the system.
Bottleneck analysis can be performed indicating where work-in-process,
information, materials, and so on are being excessively delayed.
A simulation study can help in understanding how the system operates
rather than how individuals think the system operates.
“What-if” questions can be answered. This is particularly useful in the
design of new system.
11
12. Advantages and Disadvantages of Simulation (2)
Disadvantages
Model building requires special training. It is an art that is learned
over time and through experience. Furthermore, if two models are
constructed by two competent individuals, they may have similarities,
but it is highly unlikely that they will be the same.
Simulation results may be difficult to interpret. Since most simulation
outputs are essentially random variables (they are usually based on
random inputs), it may be hard to determine whether an observation is
a result of system interrelationships or randomness.
Simulation modeling and analysis can be time consuming and
expensive. Skimping on resources for modeling and analysis may
result in a simulation model or analysis that is not sufficient for the
task.
Simulation is used in some cases when an analytical solution is
possible, or even preferable. This might be particularly true in the
simulation of some waiting lines where closed-form queueing models
are available. 12
13. Areas of Application (1)
WSC(Winter Simulation Conference) : http://www.wintersim.org
Manufacturing Applications
Analysis of electronics assembly operations
Design and evaluation of a selective assembly station for high-precision
scroll compressor shells
Comparison of dispatching rules for semiconductor manufacturing using
large-facility models
Evaluation of cluster tool throughput for thin-film head production
Determining optimal lot size for a semiconductor back-end factory
Optimization of cycle time and utilization in semiconductor test
manufacturing
Analysis of storage and retrieval strategies in a warehouse
Investigation of dynamics in a service-oriented supply chain
Model for an Army chemical munitions disposal facility
Semiconductor Manufacturing
Comparison of dispatching rules using large-facility models
The corrupting influence of variability
A new lot-release rule for wafer fabs 13
14. Assessment of potential gains in productivity due to proactive reticle
management
Comparison of a 200-mm and 300-mm X-ray lithography cell
Capacity planning with time constraints between operations
300-mm logistic system risk reduction
Construction Engineering
Construction of a dam embankment
Trenchless renewal of underground urban infrastructures
Activity scheduling in a dynamic, multi-project setting
Investigation of the structural steel erection process
Special-purpose template for utility tunnel construction
Military Application
Modeling leadership effects and recruit type in an Army recruiting station
Design and test of an intelligent controller for autonomous underwater
vehicles
Modeling military requirements for nonwarfighting operations
Multi-trajectory performance for varying scenario sizes
Using adaptive agent in U.S Air Force pilot retention
Areas of Application (2)
14
15. Logistics, Transportation, and Distribution Applications
Evaluating the potential benefits of a rail-traffic planning algorithm
Evaluating strategies to improve railroad performance
Parametric modeling in rail-capacity planning
Analysis of passenger flows in an airport terminal
Proactive flight-schedule evaluation
Logistics issues in autonomous food production systems for
extended-duration space exploration
Sizing industrial rail-car fleets
Product distribution in the newspaper industry
Design of a toll plaza
Choosing between rental-car locations
Quick-response replenishment
Areas of Application (3)
15
16. Business Process Simulation
Impact of connection bank redesign on airport gate assignment
Product development program planning
Reconciliation of business and systems modeling
Personnel forecasting and strategic workforce planning
Human Systems
Modeling human performance in complex systems
Studying the human element in air traffic control
Areas of Application (4)
16