Manufacturing Systems Modeling and Analysis



Similar documents
Basic Queuing Relationships

LECTURE 16. Readings: Section 5.1. Lecture outline. Random processes Definition of the Bernoulli process Basic properties of the Bernoulli process

Queueing Systems. Ivo Adan and Jacques Resing

Push and Pull Production Systems

Determining Inventory Levels in a CONWIP Controlled Job Shop

QNAT. A Graphical Queuing Network Analysis Tool for General Open and Closed Queuing Networks. Sanjay K. Bose

HSR HOCHSCHULE FÜR TECHNIK RA PPERSW I L

SPARE PARTS INVENTORY SYSTEMS UNDER AN INCREASING FAILURE RATE DEMAND INTERVAL DISTRIBUTION

Network Design Performance Evaluation, and Simulation #6

Waiting Times Chapter 7

M/M/1 and M/M/m Queueing Systems

Drop Call Probability in Established Cellular Networks: from data Analysis to Modelling

Pull versus Push Mechanism in Large Distributed Networks: Closed Form Results

Basic Multiplexing models. Computer Networks - Vassilis Tsaoussidis

OPTIMIZED PERFORMANCE EVALUATIONS OF CLOUD COMPUTING SERVERS

Case Study I: A Database Service

CAPACITY PLANNING IN A SEMICONDUCTOR WAFER FABRICATION FACILITY WITH TIME CONSTRAINTS BETWEEN PROCESS STEPS. A Dissertation Presented

Using Queueing Network Models to Set Lot-sizing Policies. for Printed Circuit Board Assembly Operations. Maged M. Dessouky

Tenth Problem Assignment

Operations and Supply Chain Simulation with AnyLogic 7.2

Chapter 2. Simulation Examples 2.1. Prof. Dr. Mesut Güneş Ch. 2 Simulation Examples

When Load Testing Large User Population Web Applications The Devil Is In the (Virtual) User Details

Graduate Certificate Supply Chain Management

Characterizing Task Usage Shapes in Google s Compute Clusters

PERFORMANCE ANALYSIS OF AN AUTOMATED PRODUCTION SYSTEM WITH QUEUE LENGTH DEPENDENT SERVICE RATES

Performance Workload Design

THE CONTROL OF AN INTEGRATED PRODUCTION-INVENTORY SYSTEM WITH JOB SHOP ROUTINGS AND STOCHASTIC ARRIVAL AND PROCESSING TIMES

4 The M/M/1 queue. 4.1 Time-dependent behaviour

Advances in Stochastic Models for Reliability, Quality and Safety

Design of Enterprise Systems

Modeling Stochastic Inventory Policy with Simulation

Supplement to Call Centers with Delay Information: Models and Insights

Hydrodynamic Limits of Randomized Load Balancing Networks

Keywords: Dynamic Load Balancing, Process Migration, Load Indices, Threshold Level, Response Time, Process Age.

ISSN: ISO 9001:2008 Certified International Journal of Engineering Science and Innovative Technology (IJESIT) Volume 2, Issue 4, July 2013

How To Understand And Solve A Linear Programming Problem

A SIMULATION STUDY FOR DYNAMIC FLEXIBLE JOB SHOP SCHEDULING WITH SEQUENCE-DEPENDENT SETUP TIMES

Process simulation. Enn Õunapuu

Discrete-Event Simulation

Load Balancing and Switch Scheduling

STATISTICS AND DATA ANALYSIS IN GEOLOGY, 3rd ed. Clarificationof zonationprocedure described onpp

Non-Life Insurance Mathematics

SIMULATION AND MODELLING OF RAID 0 SYSTEM PERFORMANCE

1. Repetition probability theory and transforms

Arena Tutorial 1. Installation STUDENT 2. Overall Features of Arena

Chapter 13 Waiting Lines and Queuing Theory Models - Dr. Samir Safi

Lean Six Sigma Lean 201 Introduction. TECH QUALITY and PRODUCTIVITY in INDUSTRY and TECHNOLOGY

Modeling and Performance Evaluation of Computer Systems Security Operation 1

MATHEMATICAL METHODS OF STATISTICS

Software Performance and Scalability

Simple Markovian Queueing Systems

Programma della seconda parte del corso

A Diagnostic Approach to Scheduling

Project Scheduling: PERT/CPM

Network traffic: Scaling

Chapter 5 Supporting Facility and Process Flows

Simulation Tools Evaluation using Theoretical Manufacturing Model

Chapter 3 ATM and Multimedia Traffic

Univariate and Multivariate Methods PEARSON. Addison Wesley

Response Times in an Accident and Emergency Service Unit. Apurva Udeshi

UNIT 2 QUEUING THEORY

Analysis of a Production/Inventory System with Multiple Retailers

QoS-Aware Storage Virtualization for Cloud File Systems. Christoph Kleineweber (Speaker) Alexander Reinefeld Thorsten Schütt. Zuse Institute Berlin

There are a number of factors that increase the risk of performance problems in complex computer and software systems, such as e-commerce systems.

Graphing Made Easy for Project Management

Simple Methods and Procedures Used in Forecasting

UNIVERSITY OF TARTU FACULTY OF MATHEMATICS AND COMPUTER SCIENCE INSTITUTE OF COMPUTER SCIENCE

Scheduling Glossary Activity. A component of work performed during the course of a project.

The Master s Degree with Thesis Course Descriptions in Industrial Engineering

Customer Success Stories

LPV model identification for power management of Web service systems Mara Tanelli, Danilo Ardagna, Marco Lovera

Routing in packet-switching networks

System Identification for Acoustic Comms.:

business statistics using Excel OXFORD UNIVERSITY PRESS Glyn Davis & Branko Pecar

How To Predict Performance From A Network Model In Unminer (Uml)

2WB05 Simulation Lecture 8: Generating random variables

Factors to Describe Job Shop Scheduling Problem

Periodic Capacity Management under a Lead Time Performance Constraint

Advanced Signal Processing and Digital Noise Reduction

LOGICAL TOPOLOGY DESIGN Practical tools to configure networks

CURRENT wireless personal communication systems are

SCHEDULING OF NON-REPETITIVE LEAN MANUFACTURING SYSTEMS UNDER UNCERTAINTY USING INTELLIGENT AGENT SIMULATION

Dong-Ping Song. Optimal Control and Optimization. of Stochastic. Supply Chain Systems. 4^ Springer

VENDOR MANAGED INVENTORY

Multipath TCP in Data Centres (work in progress)

AFM Ch.12 - Practice Test

Transcription:

Guy L. Curry Richard M. Feldman Manufacturing Systems Modeling and Analysis 4y Springer

1 Basic Probability Review 1 1.1 Basic Definitions 1 1.2 Random Variables and Distribution Functions 4 1.3 Mean and Variance 10 1.4 Important Distributions 13 1.5 Multivariate Distributions 23 1.6 Combinations of Random Variables 31 1.6.1 Fixed Sum of Random Variables 31 1.6.2 Random Sum of Random Variables 32 1.6.3 Mixtures of Random Variables 34 Appendix 35 Problems 36 References 43 2 Introduction to Factory Models 45 2.1 The Basics / 45 2.1.1 Notation, Definitions and Diagrams 46 2.1.2 Measured Data and System Parameters 49 2.2 Introduction to Factory Performance 54 2.2.1 The Modeling Method 55 2.2.2 Model Usage 58 2.2.3 Model Conclusions 59 2.3 Deterministic vs Stochastic Models 60 Appendix 62 Problems 65 References v 67 3 Single Workstation Factory Models 69 3.1 FirstModel 69 3.2 Diagram Method for Developing the Balance Equations 73 3.3 Model Shorthand Notation 76

3.4 An Infinite Capacity Model (M/M/l) 77 3.5 Multiple Server Systems with Non-identical Service Rates 81 3.6 Using Exponentials to Approximate General Times 85 3.6.1 Erlang Processing Times 85 3.6.2 Erlang Inter-Arrival Times 87 3.6.3 Phased Inter-arrival and Processing Times 89 3.7 Single Server Model Approximations 90 3.7.1 General Service Distributions 91 3.7.2 Approximations for G/G/l Systems 93 3.7.3 Approximations for G/G/c Systems 95 Appendix 97 Problems 100 References 107 Processing Time Variability 109 4.1 Natural Processing Time Variability Ill 4.2 Random Breakdowns and Repairs During Processing 113 Problems 121 References 123 Multiple-Stage Single-Product Factory Models 125 5.1 Approximating the Departure Process from a Workstation 125 5.2 Serial Systems Decomposition 128 5.3 Nonserial Network Models 133 5.3.1 Merging Inflow Streams 133 5.3.2 Random Splitting of the Departure Stream 135 5.4 The General Network Approximation Model 138 5.4.1 Computing Workstation Mean Arrival Rates 139 5.4.2 Computing Squared Coefficients of Variation for Arrivals.. 141 Appendix '. 150 Problems 152 References 157 Multiple Product Factory Models 159 6.1 Product Flow Rates 160 6.2 Workstation Workloads.' 162 6.3 Service Time Characteristics 163 6.4 Workstation Performance Measures 164 6.5 Processing Step Modeling Paradigm 167 6.5.1 Service Time Characteristics...- 170 6.5.2 Performance Measures 172 6.6 Group Technology and Cellular Manufacturing 177 Problems 184 References 196

xi 7 Models of Various Forms of Batching 197 7.1 Batch Moves 198 7.1.1 Batch Forming Time 199 7.1.2 Batch Queue Cycle Time 201 7.1.3 Batch Move Processing Time Delays 202 7.1.4 Inter-departure Time SCV with Batch Move Arrivals 204 7.2 Batching for Setup Reduction 206 7.2.1 Inter-departure Time SCV with Batch Setups 209 7.3 Batch Service Model 209 7.3.1 Cycle Time for Batch Service 210 7.3.2 Departure Process for Batch Service 211 7.4 Modeling the Workstation Following a Batch Server 213 7.4.1 A Serial System Topology 213 7.4.2 Branching Following a Batch Server 214 7.5 Batch Network Examples 222 7.5.1 Batch Network Example 1 222 7.5.2 Batch Network Example 2 226 Problems 230 References 240 8 WIP Limiting Control Strategies 241 8.1 Closed Queueing Networks for Single Products 242 8.1.1 Analysis with Exponential Processing Times 245 8.1.2 Analysis with General Processing Times 252 8.2 Closed Queueing Networks with Multiple Products 255 8.2.1 Mean Value Analysis for Multiple Products 256 8.2.2 Mean Value Analysis Approximation for Multiple Products. 260 8.2.3 General Service Time Approximation for Multiple Products : 262 8.3 Production and Sequencing Strategies: A case study 267 8.3.1 Problem Statement 268 8.3.2 Push Strategy Model 269 8.3.3 CONWIP Strategy Model 271 Appendix 272 Problems 273 References 279 9 Serial Limited Buffer Models 281 9.1 The Decomposition Approach used for Kanban Systems 282 9.2 Modeling The Two-Node Subsystem 284 9.2.1 Modeling the Service Distribution 285 9.2.2 Structure of the State-Space 288 9.2.3 Generator Matrix Relating System Probabilities 290 9.2.4 Connecting the Subsystems 291

xii Contents 9.3 Example of a Kanban Serial System 293 9.3.1 The First Forward Pass 294 9.3.2 The Backward Pass 300 9.3.3 The Remaining Iterations 307 9.3.4 Convergence and Factory Performance Measures 308 9.3.5 Generalizations 310 9.4 Setting Kanban Limits 310 9.4.1 Allocating a Fixed Number of Buffer Units 311 9.4.2 Cycle Time Restriction 315 9.4.3 Serial Factory Results 316 Problems 317 References 320 Glossary 321 Index 325