Learning Simio Lab SeriesLab Module 03 Serial Manufacturing Line Part II


This lab module is a continuation of the basic Simio modeling lessons. The objectives of the lab are to learn about the event mode for the Source object,
- Uploaded on | 1 Views
-
kommer
About Learning Simio Lab SeriesLab Module 03 Serial Manufacturing Line Part II
PowerPoint presentation about 'Learning Simio Lab SeriesLab Module 03 Serial Manufacturing Line Part II'. This presentation describes the topic on This lab module is a continuation of the basic Simio modeling lessons. The objectives of the lab are to learn about the event mode for the Source object,. The key topics included in this slideshow are . Download this presentation absolutely free.
Presentation Transcript
Slide1Learning Simio Lab SeriesLab Module 03 Serial Manufacturing Line – Part II Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved 1
Slide2Objectives and Outline• Lab Objectives – Continue learning basic Simio modeling – Learn the event mode for the Source object – Learn about buffer capacities for the Server Object – Learn about reference properties – Learn the basics of dynamic pie charts and status plots • Lab Outline – Video 1 – Describe the system and develop the initial Simio model. – Video 2 – Add buffer capacities, introduce and use reference properties . – Video 3 – Described Resource States and added pie charts and status plots. – Video 4 – Assignments. Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved 2
Slide3Video 1 – System Description and Initial Model3 Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved M1 M2 M3 M4 Capacity Analysis Parts are continuously available (infinite supply) and there is infinite demand (i.e., we can sell all that we can make) Processing times at each station are exponential with mean of 1 minute. The buffers between stations are finite and we’re interested in determining the impact of buffer size on system capacity .
Slide4video 2 – capacitated buffers and reference properties4 Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved M1 M2 M3 M4 Capacity Analysis Parts are continuously available (infinite supply) and there is infinite demand (i.e., we can sell all that we can make) Processing times at each station are exponential with mean of 1 minute. The buffers between stations are finite and we’re interested in determining the impact of buffer size on system capacity .
Slide5Reference Properties• Using a reference to a model property to specify the value for an object property. 5 Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved
Slide6Video 3 – ResourceStates, Pie Charts, Status Plots6 Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved M1 M2 M3 M4 Capacity Analysis Parts are continuously available (infinite supply) and there is infinite demand (i.e., we can sell all that we can make) Processing times at each station are exponential with mean of 1 minute. The buffers between stations are finite and we’re interested in determining the impact of buffer size on system capacity .
Slide7Video 3 – ResourceStates, Pie Charts, Status Plots7 Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved
Slide8Video 4 - Assignments1. Create experiment responses for the average numbers of parts in the buffers between M1 and M2, M2 and M3, and M3 and M4. 2. Create a status plot showing the average numbers of parts in the same buffers. 3. For the following three configurations, assume that you have 20 total buffer slots to distribute between the three buffers (i.e., (7, 3, 10) has a capacity 7 buffer between M1 and M2, a capacity 3 buffer between M2 and M3, and a capacity 10 buffer between M3 and M4). Find the buffer allocation that maximizes throughput of the line. 8 Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved Processing Time Distribution M1 M2 M3 M4 A Exponential(1) Exponential(1.1) Exponential(1.2) Exponential(1.1) B Triangular(.5, 1, 1.5) Triangular(.5, 1, 1.5) Triangular(.5, 1, 1.5) Triangular(.5, 1, 1.5) C Uniform(1, 2) Uniform(.5, 2.5) Exponential(1.5) Uniform(1, 2)
Slide9Video 4 - Assignments4. Supposed that there is a 5% probably of fallout at each machine. In other words, 5% of parts processed at each machine are bad and exit the system without proceeding to the “next” machine in the system. Modify your model to reflect this change. 5. Determine whether the optimal buffer configurations from question 3 change as a result of the part fallout. • Note that you should justify your choice of – Replication length; – Warmup period length; and – Number of replications for questions 3 and 5 9 Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved