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Modeling And Simulation Presentation Transcript
1.MANUFACTURING SYSTEMS
2.contents
1.What is Modeling
2.contents
1.What is Modeling
2.What is simulation
3.How to develop a simulation model
4.How to design simulation experiment
4.How to perform simulation analysis
5.What makes a problem suitable for simulation modeling and analysis.
6.How to select simulation software
7. Simulations shortcomings
8. SIMTER
9. Conclusions
10. References
3.How to develop a simulation model
4.How to design simulation experiment
4.How to perform simulation analysis
5.What makes a problem suitable for simulation modeling and analysis.
6.How to select simulation software
7. Simulations shortcomings
8. SIMTER
9. Conclusions
10. References
3.What is Modeling?
Modeling is the process of producing a model;
A model is a representation of the construction and working of some system of interest.
One purpose of a model is to enable the analyst to predict the effect of changes to the system.
On the one hand, a model should be a close approximation to the real system and incorporate most of its salient features.
On the other hand, it should not be so complex that it is impossible to understand and experiment with it.
Modeling is the process of producing a model;
A model is a representation of the construction and working of some system of interest.
One purpose of a model is to enable the analyst to predict the effect of changes to the system.
On the one hand, a model should be a close approximation to the real system and incorporate most of its salient features.
On the other hand, it should not be so complex that it is impossible to understand and experiment with it.
4.An important issue in modeling is model validity. Model validation techniques include simulating the model under known input conditions and comparing model output with system output.
Generally, a model intended for a simulation study is a mathematical model developed with the help of simulation software
Mathematical model classifications include
Deterministic (input and output variables are fixed valve)
Stochastic (output variables is probabilistic);
Static (time is not taken into account) or dynamic (time-varying interactions among variables are taken into account).
Typically, simulation models are stochastic and dynamic.
Generally, a model intended for a simulation study is a mathematical model developed with the help of simulation software
Mathematical model classifications include
Deterministic (input and output variables are fixed valve)
Stochastic (output variables is probabilistic);
Static (time is not taken into account) or dynamic (time-varying interactions among variables are taken into account).
Typically, simulation models are stochastic and dynamic.
5.A simulation of a system is the operation of a model of the system.
The model can be reconfigured and experimented with; usually, this is impossible, too expensive or impractical to do in the system it represents.
The operation of the model can be studied, and hence, properties concerning the behavior of the actual system or its subsystem can be inferred.
In its broadest sense, simulation is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time.
The model can be reconfigured and experimented with; usually, this is impossible, too expensive or impractical to do in the system it represents.
The operation of the model can be studied, and hence, properties concerning the behavior of the actual system or its subsystem can be inferred.
In its broadest sense, simulation is a tool to evaluate the performance of a system, existing or proposed, under different configurations of interest and over long periods of real time.
6.Simulation is used before an existing system is altered or a new system built
- To reduce the chances of failure to meet specifications,
- To eliminate unforeseen bottlenecks,
- To prevent under or over-utilization of resources, and
- To optimize system performance
7.Simulation study.
In a simulation study, human decision making is required at all stages, namely, model development, experiment design, output analysis, conclusion formulation, and making decisions to alter the system under study.
The only stage where human intervention is not required is the running of the simulations, which most simulation software packages perform efficiently.
- To reduce the chances of failure to meet specifications,
- To eliminate unforeseen bottlenecks,
- To prevent under or over-utilization of resources, and
- To optimize system performance
7.Simulation study.
In a simulation study, human decision making is required at all stages, namely, model development, experiment design, output analysis, conclusion formulation, and making decisions to alter the system under study.
The only stage where human intervention is not required is the running of the simulations, which most simulation software packages perform efficiently.
8.How to develop a simulation model
Simulation models consist of the following components:
System entities,
Input variables, performance measures, and
Functional relationships.
For instance in a simulation model of an M/M/l queue,
The server and the queue are system entities,
Arrival rate and service rate are input variables,
Mean wait time and maximum queue length are performance measures, and
Time in system = wait time + service time is an example of a functional relationship.
Simulation models consist of the following components:
System entities,
Input variables, performance measures, and
Functional relationships.
For instance in a simulation model of an M/M/l queue,
The server and the queue are system entities,
Arrival rate and service rate are input variables,
Mean wait time and maximum queue length are performance measures, and
Time in system = wait time + service time is an example of a functional relationship.
9.How to develop a simulation model
Step 1. Identify the problem.
Step 2. Formulate the problem
step3 . Collect and process real system data
Step 4. Formulate and develop a model.
Step 5. Validate the model.
Step 6. Document model for future use.
Step 1. Identify the problem.
Step 2. Formulate the problem
step3 . Collect and process real system data
Step 4. Formulate and develop a model.
Step 5. Validate the model.
Step 6. Document model for future use.
10.Step 1. Select appropriate experimental design.
Step 2. Establish experimental conditions for runs.
step 3. Perform simulations runs
Step 2. Establish experimental conditions for runs.
step 3. Perform simulations runs
11.What Makes A Problem Suitable For Simulation Modeling And Analysis?
It is impossible or extremely expensive to observe certain processes in the real world, e.g., next year’s cancer statistics.
Problems in which mathematical model can be formulated but analytic solutions are either impossible (e.g., job shop scheduling problem, high-order difference equations) or too complicated ( e.g. complex system like the stock market, and large scale queuing models).
It is impossible or extremely expensive to validate the mathematical model describing the system, e.g., due to insufficient data
It is impossible or extremely expensive to observe certain processes in the real world, e.g., next year’s cancer statistics.
Problems in which mathematical model can be formulated but analytic solutions are either impossible (e.g., job shop scheduling problem, high-order difference equations) or too complicated ( e.g. complex system like the stock market, and large scale queuing models).
It is impossible or extremely expensive to validate the mathematical model describing the system, e.g., due to insufficient data
12.How to select simulation software?
A simulation model can be built using general purpose programming languages which are familiar to the analyst, available over a wide variety of platforms.
There are hundreds of simulation products on the market, many with price tags of $15,000 or more. Naturally, the question of how to select the best simulation software for an application arises.
Metrics for evaluation include
Modeling flexibility,
Ease of use,
Modeling structure (hierarchical v/s flat; object-oriented v/s nested),
Code reusability,
Graphic user interface,
Animation,
Hardware and software requirements,
Statistical capabilities,
Output reports and graphical plots,
Customer support, and documentation
A simulation model can be built using general purpose programming languages which are familiar to the analyst, available over a wide variety of platforms.
There are hundreds of simulation products on the market, many with price tags of $15,000 or more. Naturally, the question of how to select the best simulation software for an application arises.
Metrics for evaluation include
Modeling flexibility,
Ease of use,
Modeling structure (hierarchical v/s flat; object-oriented v/s nested),
Code reusability,
Graphic user interface,
Animation,
Hardware and software requirements,
Statistical capabilities,
Output reports and graphical plots,
Customer support, and documentation
13.The two types of simulation packages are
Simulation languages and
Application-oriented simulators
Simulation languages offer more flexibility than the application-oriented simulators
Simulation languages and
Application-oriented simulators
Simulation languages offer more flexibility than the application-oriented simulators
14.Simulations shortcomings
Simulation in discrete parts manufacturing seldom addresses sustainability issues.
Current simulation products do not typically support the modeling of environmental concerns or impacts; e.g., energy consumption or carbon footprint, waste/hazardous materials disposal, and pollution.
Regional differences in environmental safety requirements are not represented in simulation environments.
Recovery, recycling, and life cycle costs (LCC) of materials are not addressed in design and manufacturing simulations.
Simulations do not deal with the usage and disposal practices of product users after sale
Simulation in discrete parts manufacturing seldom addresses sustainability issues.
Current simulation products do not typically support the modeling of environmental concerns or impacts; e.g., energy consumption or carbon footprint, waste/hazardous materials disposal, and pollution.
Regional differences in environmental safety requirements are not represented in simulation environments.
Recovery, recycling, and life cycle costs (LCC) of materials are not addressed in design and manufacturing simulations.
Simulations do not deal with the usage and disposal practices of product users after sale
15.SIMTER - Advanced Simulation-based Production Development Tool For Traditional Manufacturing Industries
Collaboration of Finnish and Swedish Government
The SIMTER project focused on producing three sub-tools (LoA, ergonomics, environmental) and integrating them into a single SIMTER tool.
Simter- Core Area
Discrete event simulation
Level of automation
Environmental impacts
Ergonomics
Collaboration of Finnish and Swedish Government
The SIMTER project focused on producing three sub-tools (LoA, ergonomics, environmental) and integrating them into a single SIMTER tool.
Simter- Core Area
Discrete event simulation
Level of automation
Environmental impacts
Ergonomics
16.Environmental wastes include
Energy, water, or raw materials consumed in excess of what is needed to meet customer needs
Pollutants and material wastes released into the environment, such as air emissions, wastewater discharges, hazardous wastes and solid wastes (trash or discarded scrap)
Energy, water, or raw materials consumed in excess of what is needed to meet customer needs
Pollutants and material wastes released into the environment, such as air emissions, wastewater discharges, hazardous wastes and solid wastes (trash or discarded scrap)
17.ergonomics
In the SIMTER project, integrated simulation tool developed to maximize production efficiency and to balance manual and automated work subject to ergonomics constraints.
Several common and validated methods for evaluating the ergonomics of working postures are employed in the SIMTER tool.
These are
RULA-Rapid Upper Limb Assessment
Ovako Working posture Analysis System
ERGOCAN-(Combination of RULA and OWAS)
In the SIMTER project, integrated simulation tool developed to maximize production efficiency and to balance manual and automated work subject to ergonomics constraints.
Several common and validated methods for evaluating the ergonomics of working postures are employed in the SIMTER tool.
These are
RULA-Rapid Upper Limb Assessment
Ovako Working posture Analysis System
ERGOCAN-(Combination of RULA and OWAS)
18.Conclusions of Simter tool
Determining the influence of levels of automation on ergonomics, and environmental impacts.
Fill the gap between Life Cycle Assessment and conventional process simulation, and identifying the most significant environmental factors to be taken into account.
Determining the influence of levels of automation on ergonomics, and environmental impacts.
Fill the gap between Life Cycle Assessment and conventional process simulation, and identifying the most significant environmental factors to be taken into account.
19.INRODUCTION
The furniture industry is operating on tighter margins and ever increasing competition.
With more competition and ever changing consumer demands, manufacturers are frequently realizing the necessity to reengineer their facility to satisfy the needs of many product groups and styles.
Manufacturers are constantly under a directive to improve product quality while simultaneously reducing costs and increasing profit margins.
The furniture industry is operating on tighter margins and ever increasing competition.
With more competition and ever changing consumer demands, manufacturers are frequently realizing the necessity to reengineer their facility to satisfy the needs of many product groups and styles.
Manufacturers are constantly under a directive to improve product quality while simultaneously reducing costs and increasing profit margins.
20.Often engineers are assigned one of two major tasks: Either redesign an existing facility to meet current market demands, or design a new plant from scratch.
The first task is difficult to perform with the plant already in production, and mistakes in the new alignment can be costly.
Therefore, in either case it is crucial to build a model of the system to use in the engineer’s analysis to minimize errors in layout design, system behavioral assumptions, and capital costs.
The first task is difficult to perform with the plant already in production, and mistakes in the new alignment can be costly.
Therefore, in either case it is crucial to build a model of the system to use in the engineer’s analysis to minimize errors in layout design, system behavioral assumptions, and capital costs.
21.In this paper, we are going to evaluate the proposed layout of a dining room tabletop plant.
The plant consists of a machining cell where all of the legs and side apron pieces are cut and drilled, as well as a sanding operation where all of the tabletops come down a sanding line.
After these operations, the tops and legs are joined together and placed on a conveyor system as they pass through the staining room. After the staining operations are performed, the table is sent through a finishing line before moving to final assembly, and then finally to shipping.
The plant consists of a machining cell where all of the legs and side apron pieces are cut and drilled, as well as a sanding operation where all of the tabletops come down a sanding line.
After these operations, the tops and legs are joined together and placed on a conveyor system as they pass through the staining room. After the staining operations are performed, the table is sent through a finishing line before moving to final assembly, and then finally to shipping.
22.Sim X, Inc. of Woodbridge, VA is a simulation-consulting firm that focuses on delivering state-of- the-art multi-purpose simulation models.
Utilizing the ProModel simulation engine from ProModel Corporation of Orem, UT, Sim X has built advanced models for a number of furniture manufacturers.
The models being created not only address issues regarding facility layout, but also can be easily adapted to examine the effect on plant operations resulting from modifications in product styles and machine route changes as is often dictated by market demand.
Utilizing the ProModel simulation engine from ProModel Corporation of Orem, UT, Sim X has built advanced models for a number of furniture manufacturers.
The models being created not only address issues regarding facility layout, but also can be easily adapted to examine the effect on plant operations resulting from modifications in product styles and machine route changes as is often dictated by market demand.
23.Objectives:
The first step in modeling a furniture facility is to determine the objectives.
To generalize, assume that the first objective is to determine staffing levels in a machining cell.
The second objective is to determine batch sizes and perform a line-balancing act between multiple machine cells.
The third objective is to determine buffer sizes at the major staging areas.
The first step in modeling a furniture facility is to determine the objectives.
To generalize, assume that the first objective is to determine staffing levels in a machining cell.
The second objective is to determine batch sizes and perform a line-balancing act between multiple machine cells.
The third objective is to determine buffer sizes at the major staging areas.
24.MODEL DEVELOPMENT
Sim X uses the ProModel simulation engine as the modeling language to address the problem.
After developing the model in ProModel, Sim X increases the usability of the tool by creating a custom front-end user interface utilizing the latest Visual Basic features of Microsoft Excel and the Active X capability of ProModel.
The user simply manipulates the input data on a spreadsheet and the input parameters transfer automatically into the simulation model when executed.
Sim X uses the ProModel simulation engine as the modeling language to address the problem.
After developing the model in ProModel, Sim X increases the usability of the tool by creating a custom front-end user interface utilizing the latest Visual Basic features of Microsoft Excel and the Active X capability of ProModel.
The user simply manipulates the input data on a spreadsheet and the input parameters transfer automatically into the simulation model when executed.
25.Model Inputs
The model has been developed to be as flexible as possible.
The Microsoft Excel front-end interface takes into account specific input parameters by categorizing issues utilizing a series of orksheets in the Excel workbook.
The model has been developed to be as flexible as possible.
The Microsoft Excel front-end interface takes into account specific input parameters by categorizing issues utilizing a series of orksheets in the Excel workbook.
26.Model Outputs
Many performance measures are collected in multiple reports. The default statistics being collected by the ProModel Output database include:
Buffer levels over time
Operator utilization
Cycle times for each suit
In the example of the tabletop versus apron manufacturing line, one of these detailed system behaviors the model would report is the start and end time (for their respective machining cells) of dependent pieces that needed to be matched for the staining operation(see Figure)
Many performance measures are collected in multiple reports. The default statistics being collected by the ProModel Output database include:
Buffer levels over time
Operator utilization
Cycle times for each suit
In the example of the tabletop versus apron manufacturing line, one of these detailed system behaviors the model would report is the start and end time (for their respective machining cells) of dependent pieces that needed to be matched for the staining operation(see Figure)
27.When dealing with thousands of parts that are elements of hundreds of items, it is often appropriate to examine the start and end times of each part in a more formal format for the entire system.
Therefore, Sim X developed a method to integrate the back-end into Microsoft Project to examine the implications of scheduling practices on the production time for a given cutting.
Therefore, Sim X developed a method to integrate the back-end into Microsoft Project to examine the implications of scheduling practices on the production time for a given cutting.
28.RESULTS
The models being created not only serve the initial purpose of determining buffer space and resource levels, but they are being used on a regular basis to evaluate new cuttings. This multi-functionality feature has turned the simulation models into operational planning tools.
The models being created not only serve the initial purpose of determining buffer space and resource levels, but they are being used on a regular basis to evaluate new cuttings. This multi-functionality feature has turned the simulation models into operational planning tools.
29.The furniture industry has begun to utilize the latest in simulation technology.
With a Microsoft Excel front-end interface, simulation is being brought directly to the plant floor where the everyday engineer can evaluate changes quickly and accurately.
With a Microsoft Excel front-end interface, simulation is being brought directly to the plant floor where the everyday engineer can evaluate changes quickly and accurately.
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