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Manufacturing Systems Modeling and Analysis 2nd ed. 2011 [Kõva köide]

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  • Formaat: Hardback, 338 pages, kõrgus x laius: 235x155 mm, kaal: 1490 g, XVI, 338 p., 1 Hardback
  • Ilmumisaeg: 30-Nov-2010
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642166172
  • ISBN-13: 9783642166174
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Manufacturing Systems Modeling and Analysis 2nd ed. 2011Suurem pilt
  • Formaat: Hardback, 338 pages, kõrgus x laius: 235x155 mm, kaal: 1490 g, XVI, 338 p., 1 Hardback
  • Ilmumisaeg: 30-Nov-2010
  • Kirjastus: Springer-Verlag Berlin and Heidelberg GmbH & Co. K
  • ISBN-10: 3642166172
  • ISBN-13: 9783642166174
Teised raamatud teemal:
Püsilink: https://www.kriso.ee/db/9783642166174.html
Märksõnad:
This text presents the practical application of queueing theory results for the design and analysis of manufacturing and production systems. This textbook makes accessible to undergraduates and beginning graduates many of the seemingly esoteric results of queueing theory. In an effort to apply queueing theory to practical problems, there has been considerable research over the previous few decades in developing reasonable approximations of queueing results. This text takes full advantage of these results and indicates how to apply queueing approximations for the analysis of manufacturing systems. Support is provided through the web site http://msma.tamu.edu. Students will have access to the answers of odd numbered problems and instructors will be provided with a full solutions manual, Excel files when needed for homework, and computer programs using Mathematica that can be used to solve homework and develop additional problems or term projects. In this second edition a separate appendix dealing with some of the basic event-driven simulation concepts has been added.
1 Basic Probability Review
1(44)
1.1 Basic Definitions
1(3)
1.2 Random Variables and Distribution Functions
4(6)
1.3 Mean and Variance
10(3)
1.4 Important Distributions
13(11)
1.5 Multivariate Distributions
24(8)
1.6 Combinations of Random Variables
32(4)
1.6.1 Fixed Sum of Random Variables
32(1)
1.6.2 Random Sum of Random Variables
33(2)
1.6.3 Mixtures of Random Variables
35(1)
Appendix
36(1)
Problems
37(7)
References
44(1)
2 Introduction to Factory Models
45(24)
2.1 The Basics
45(9)
2.1.1 Notation, Definitions and Diagrams
46(3)
2.1.2 Measured Data and System Parameters
49(5)
2.2 Introduction to Factory Performance
54(6)
2.2.1 The Modeling Method
55(3)
2.2.2 Model Usage
58(1)
2.2.3 Model Conclusions
59(1)
2.3 Deterministic vs Stochastic Models
60(2)
Appendix
62(3)
Problems
65(2)
References
67(2)
3 Single Workstation Factory Models
69(40)
3.1 First Model
69(4)
3.2 Diagram Method for Developing the Balance Equations
73(3)
3.3 Model Shorthand Notation
76(1)
3.4 An Infinite Capacity Model (M/M/1)
77(4)
3.5 Multiple Server Systems with Non-identical Service Rates
81(4)
3.6 Using Exponentials to Approximate General Times
85(5)
3.6.1 Erlang Processing Times
85(2)
3.6.2 Erlang Inter-Arrival Times
87(2)
3.6.3 Phased Inter-arrival and Processing Times
89(1)
3.7 Single Server Model Approximations
90(7)
3.7.1 General Service Distributions
91(2)
3.7.2 Approximations for G/G/1 Systems
93(2)
3.7.3 Approximations for G/G/c Systems
95(2)
Appendix
97(3)
Problems
100(7)
References
107(2)
4 Processing Time Variability
109(16)
4.1 Natural Processing Time Variability
111(2)
4.2 Random Breakdowns and Repairs During Processing
113(2)
4.3 Operator-Machine Interactions
115(6)
Problems
121(2)
References
123(2)
5 Multiple-Stage Single-Product Factory Models
125(34)
5.1 Approximating the Departure Process from a Workstation
125(3)
5.2 Serial Systems Decomposition
128(5)
5.3 Nonserial Network Models
133(5)
5.3.1 Merging Inflow Streams
133(2)
5.3.2 Random Splitting of the Departure Stream
135(3)
5.4 The General Network Approximation Model
138(12)
5.4.1 Computing Workstation Mean Arrival Rates
139(2)
5.4.2 Computing Squared Coefficients of Variation for Arrivals
141(9)
Appendix
150(2)
Problems
152(5)
References
157(2)
6 Multiple Product Factory Models
159(38)
6.1 Product Flow Rates
160(2)
6.2 Workstation Workloads
162(1)
6.3 Service Time Characteristics
163(1)
6.4 Workstation Performance Measures
164(3)
6.5 Processing Step Modeling Paradigm
167(10)
6.5.1 Service Time Characteristics
170(2)
6.5.2 Performance Measures
172(2)
6.5.3 Alternate Approaches
174(3)
6.6 Group Technology and Cellular Manufacturing
177(7)
Problems
184(12)
References
196(1)
7 Models of Various Forms of Batching
197(44)
7.1 Batch Moves
198(8)
7.1.1 Batch Forming Time
199(2)
7.1.2 Batch Queue Cycle Time
201(1)
7.1.3 Batch Move Processing Time Delays
202(2)
7.1.4 Inter-departure Time SCV with Batch Move Arrivals
204(2)
7.2 Batching for Setup Reduction
206(3)
7.2.1 Inter-departure Time SCV with Batch Setups
209(1)
7.3 Batch Service Model
209(4)
7.3.1 Cycle Time for Batch Service
210(1)
7.3.2 Departure Process for Batch Service
211(2)
7.4 Modeling the Workstation Following a Batch Server
213(9)
7.4.1 A Serial System Topology
213(1)
7.4.2 Branching Following a Batch Server
214(8)
7.5 Batch Network Examples
222(8)
7.5.1 Batch Network Example 1
222(4)
7.5.2 Batch Network Example 2
226(4)
Problems
230(10)
References
240(1)
8 WIP Limiting Control Strategies
241(40)
8.1 Closed Queueing Networks for Single Products
242(13)
8.1.1 Analysis with Exponential Processing Times
245(7)
8.1.2 Analysis with General Processing Times
252(3)
8.2 Closed Queueing Networks with Multiple Products
255(12)
8.2.1 Mean Value Analysis for Multiple Products
256(4)
8.2.2 Mean Value Analysis Approximation for Multiple Products
260(2)
8.2.3 General Service Time Approximation for Multiple Products
262(5)
8.3 Production and Sequencing Strategies
267(5)
8.3.1 Problem Statement
268(1)
8.3.2 Push Strategy Model
269(2)
8.3.3 CONWIP Strategy Model
271(1)
Appendix
272(1)
Problems
273(6)
References
279(2)
9 Serial Limited Buffer Models
281(40)
9.1 The Decomposition Approach Used for Kanban Systems
282(2)
9.2 Modeling the Two-Node Subsystem
284(9)
9.2.1 Modeling the Service Distribution
285(3)
9.2.2 Structure of the State-Space
288(2)
9.2.3 Generator Matrix Relating System Probabilities
290(1)
9.2.4 Connecting the Subsystems
291(2)
9.3 Example of a Kanban Serial System
293(17)
9.3.1 The First Forward Pass
294(6)
9.3.2 The Backward Pass
300(7)
9.3.3 The Remaining Iterations
307(1)
9.3.4 Convergence and Factory Performance Measures
308(2)
9.3.5 Generalizations
310(1)
9.4 Setting Kanban Limits
310(7)
9.4.1 Allocating a Fixed Number of Buffer Units
311(4)
9.4.2 Cycle Time Restriction
315(1)
9.4.3 Serial Factory Results
316(1)
Problems
317(3)
References
320(1)
A Simulation Overview
321(10)
A.1 Random Variates
321(2)
A.2 Event-Driven Simulations
323(7)
References
330(1)
Glossary 331(4)
Index 335
Guy L. Curry is a Professor of Industrial and Systems Engineering at Texas A&M University. He received B.S. and M.S. degrees in mathematics from the University of Oklahoma and Wichita State University, respectively, and a Ph.D. in industrial engineering from the University of Arkansas. Prior to joining Texas A&M University, he was an operations research analyst with Boeing and Sun Oil. He has received several research and teaching awards and co-authored three books. Dr. Curry teaches courses in simulation, optimization, and production /manufacturing systems. His current research interests include modeling and analysis techniques for production and manufacturing systems.

Richard M. Feldman is a Professor of Industrial and Systems Engineering at Texas A&M University. He received a B.A. degree in mathematics from Hope College, an M.S. degree in mathematics from Michigan State University, an M.S. degree in Industrial and System Engineering from Ohio University, and a Ph.D. in Industrial Engineering from Northwestern University. His teaching interests include simulation, applied probability, and queueing theory. His consulting and funded research activities have involved modeling and simulation within manufacturing, transportation, and biological contexts. He has received several teaching awards, published papers in applied probability and queueing theory, and co-authored four books.