Lab Module 03 Serial Manufacturing Line – Part II

Slides:

Advertisements

Similar presentations

M. Colledani, T. Tolio Dipartimento di Meccanica

Advertisements

1 Copyright © 2009, Oracle. All rights reserved. B Table Descriptions.

McGraw-Hill/Irwin Copyright © 2013 by The McGraw-Hill Companies, Inc. All rights reserved. A PowerPoint Presentation Package to Accompany Applied Statistics.

Part 3 Probabilistic Decision Models

Copyright © Jeffrey S. Smith | All Rights Reserved

Operations Scheduling

4.4 Price Chapter 27. Price Price is the amount paid by consumers for a product.

Lab Module 05 Entity Routing

Lab Module 10 Simulation-based Optimization

Lab Module 06 Routing with Sequences

Operations Management

Lab Module 02 Serial Manufacturing Line

Lab Module 04 More on Simio Animation

Lab Module 9 User-defined Statistics

Lab Module 11 Vehicles and Conveyors

SMA 6304/MIT2.853/MIT2.854 Manufacturing Systems Lecture 19-20: Single-part-type, multiple stage systems Lecturer: Stanley B. Gershwin

Traffic and routing. Network Queueing Model Packets are buffered in egress queues waiting for serialization on line Link capacity is C bps Average packet.

Presented to AGIFORS YM Study Group Bangkok, Thailand May 2001 Larry Weatherford University of Wyoming Dispersed Fares within a Fare Class: How Can We.

Class-constrained Packing Problems with Application to Storage Management in Multimedia Systems Tami Tamir Department of Computer Science The Technion.

S. Chopra/Operations/Managing Services1 Operations Management: Capacity Management in Services Module u Why do queues build up? u Process attributes and.

PEDESTRAIN CELLULAR AUTOMATA AND INDUSTRIAL PROCESS SIMULATION Alan Jolly (a), Rex Oleson II (b), Dr. D. J. Kaup (c) (a,b,c) Institute for Simulation and.

Dynamic Tuning of the IEEE Protocol to Achieve a Theoretical Throughput Limit Frederico Calì, Marco Conti, and Enrico Gregori IEEE/ACM TRANSACTIONS.

Introduction to Arena A Simple Simulation. Model1 We examine a simple model: parts arrive at a server, are served, and depart the system. There will be.

Two Media Access Control Protocols (MAC) for Wireless Networks.

Statistics for Managers Using Microsoft Excel, 5e © 2008 Pearson Prentice-Hall, Inc.Chap 6-1 Statistics for Managers Using Microsoft® Excel 5th Edition.

Problem 8.4 K = ∞ R i = 60/4 = 15 /hr T p = 3 min = 0.05 hour c= 1 R p = c/Tp = 20 /hour Both Ti and Tp exponential Server $20 /hr Phone $5/hr Wait cost.

Statistics for Managers Using Microsoft Excel, 4e © 2004 Prentice-Hall, Inc. Chap 6-1 Chapter 6 The Normal Distribution and Other Continuous Distributions.

Management of Waiting Lines

Internet Queuing Delay Introduction How many packets in the queue? How long a packet takes to go through?

Dimitrios Konstantas, Evangelos Grigoroudis, Vassilis S. Kouikoglou and Stratos Ioannidis Department of Production Engineering and Management Technical.

Model and tools. Traffic Model Poisson law – Napoléon worries about the statistics of horse accidents of his generals – Poisson confirms these are unfrequent.

Dr. Cesar Malave Texas A & M University

Flows and Networks Plan for today (lecture 5): Last time / Questions? Blocking of transitions Kelly / Whittle network Optimal design of a Kelly / Whittle.

Introduction to Queuing Theory

1 Chapter 5 Flow Lines Types Issues in Design and Operation Models of Asynchronous Lines –Infinite or Finite Buffers Models of Synchronous (Indexing) Lines.

Management of Waiting Lines McGraw-Hill/Irwin Copyright © 2012 by The McGraw-Hill Companies, Inc. All rights reserved.

ECES 741: Stochastic Decision & Control Processes – Chapter 1: The DP Algorithm 31 Alternative System Description If all w k are given initially as Then,

Networks of Queues Plan for today (lecture 6): Last time / Questions? Product form preserving blocking Interpretation traffic equations Kelly / Whittle.

McGraw-Hill/Irwin Copyright © 2010 by The McGraw-Hill Companies, Inc. All rights reserved. Chapter 6 Continuous Random Variables.

8/24/04 Paul A. Jensen Operations Research Models and Methods Copyright All rights reserved Material Movement The movement of material through the.

COMP155 Computer Simulation September 8, States and Events  A state is a condition of a system at some point in time  An event is something that.

Reid & Sanders, Operations Management © Wiley 2002 Waiting Line Models A SUPPLEMENT.

Slide 1 Matching Supply with Demand: An Introduction to Operations Management Gérard Cachon ChristianTerwiesch All slides in this file are copyrighted.

CS352 - Introduction to Queuing Theory Rutgers University.

Learning Simio Chapter 10 Analyzing Input Data

Stevenson and Ozgur First Edition Introduction to Management Science with Spreadsheets McGraw-Hill/Irwin Copyright © 2007 by The McGraw-Hill Companies,

Chapter 6 Managing Capacity

© 2002 Prentice-Hall, Inc.Chap 5-1 Statistics for Managers Using Microsoft Excel 3 rd Edition Chapter 5 The Normal Distribution and Sampling Distributions.

Copyright ©: Nahrstedt, Angrave, Abdelzaher, Caccamo1 Queueing Systems.

EVOLUTION OF INDUSTRIAL ENGINEERING Prof. Dr. Orhan TORKUL Res. Asst. M. Raşit CESUR Res. Asst. Furkan YENER.

CHANNEL ALLOCATION FOR SMOOTH VIDEO DELIVERY OVER COGNITIVE RADIO NETWORKS Globecom 2010, FL, USA 1 Sanying Li, Tom H. Luan, Xuemin (Sherman) Shen Department.

Management of Waiting Lines Copyright © 2015 McGraw-Hill Education. All rights reserved. No reproduction or distribution without the prior written consent.

OPERATIONS MANAGEMENT INTEGRATING MANUFACTURING AND SERVICES FIFTH EDITION Mark M. Davis Janelle Heineke Copyright ©2005, The McGraw-Hill Companies, Inc.

Simple Queueing Theory: Page 5.1 CPE Systems Modelling & Simulation Techniques Topic 5: Simple Queueing Theory  Queueing Models  Kendall notation.

Queueing Theory/Waiting Line: Models and Analysis Navneet Vidyarthi

Cold Jet VMWare Consolidation Ben Snowden. Agenda Problems Project Description and Use Deliverables Demonstration Conclusion.

1 Ardavan Asef-Vaziri Jan.-2016Basics Probability Distributions- Uniform.

McGraw-Hill/Irwin Copyright © 2009 by The McGraw-Hill Companies, Inc. All Rights Reserved. Chapter 18 Management of Waiting Lines.

Lin/Operations/Managing Services1 Capacity Management in Services Module u Queuing processes and performance measures u Why do queues build up? u Performance.

Objectives Model a Subway sandwich shop in Simio Perform basic verification analysis o Define experiment “Responses” o Learn how to interpret SMORE plots.

TYPES OF SOLUTIONS OF LINEAR EQUATIONS

Professor Arne Thesen, University of Wisconsin-Madison

IV-2 Manufacturing Systems modeling

System Performance: Queuing

Variability 8/24/04 Paul A. Jensen

Statistics for Managers Using Microsoft® Excel 5th Edition

Queueing Theory 2008.

Jeff Smith Auburn University

Ian Falu Prety Widjaja IE-642

Presentation transcript:

Lab Module 03 Serial Manufacturing Line – Part II Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved

Objectives 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

Video 1 – System Description and Initial Model 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. Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved

Video 2 – Capacitated Buffers and Reference Properties 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. Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved

Reference Properties Using a reference to a model property to specify the value for an object property. Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved

Video 3 – ResourceStates, Pie Charts, Status Plots 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. Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved

Video 3 – ResourceStates, Pie Charts, Status Plots Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved

Processing Time Distribution Video 4 - Assignments Create experiment responses for the average numbers of parts in the buffers between M1 and M2, M2 and M3, and M3 and M4. Create a status plot showing the average numbers of parts in the same buffers. 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. Processing Time Distribution M1 M2 M3 M4 A Exponential(1) Exponential(1.1) Exponential(1.2) B Triangular(.5, 1, 1.5) C Uniform(1, 2) Uniform(.5, 2.5) Exponential(1.5) Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved

Video 4 - Assignments 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. 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 Copyright © 2013 - Jeffrey S. Smith and Simio LLC | All Rights Reserved