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E-raamat: Optimization of Logistics

(University of Technology of Troyes (UTT), France), (University of Technology of Troyes (UTT), France), (University of Technology of Troyes (UTT), France), (University of Technology of Troyes (UTT), France)
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 13-Dec-2012
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118569573
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Optimization of LogisticsSuurem pilt
  • Formaat: EPUB+DRM
  • Ilmumisaeg: 13-Dec-2012
  • Kirjastus: ISTE Ltd and John Wiley & Sons Inc
  • Keel: eng
  • ISBN-13: 9781118569573
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This book aims to help engineers, Masters students and young researchers to understand and gain a general knowledge of logistic systems optimization problems and techniques, such as system design, layout, stock management, quality management, lot-sizing or scheduling. It summarizes the evaluation and optimization methods used to solve the most frequent problems. In particular, the authors also emphasize some recent and interesting scientific developments, as well as presenting some industrial applications and some solved instances from real-life cases.
Performance evaluation tools (Petri nets, the Markov process, discrete event simulation, etc.) and optimization techniques (branch-and-bound, dynamic programming, genetic algorithms, ant colony optimization, etc.) are presented first. Then, new optimization methods are presented to solve systems design problems, layout problems and buffer-sizing optimization. Forecasting methods, inventory optimization, packing problems, lot-sizing quality management and scheduling are presented with examples in the final chapters.

Arvustused

On the other hand, this book constitutes a valuable guide and convenient introduction to the fied of operations research applications for professionals, which deal with real production and logistic system design and management. It can be also recommended as a textbook for students of production management.  (Zentralblatt Math, 1 May 2013)

 

Introduction xiii
Chapter 1 Modeling and Performance Evaluation 1(60)
1.1 Introduction
1(1)
1.2 Markovian processes
2(12)
1.2.1 Overview of stochastic processes
2(1)
1.2.2 Markov processes
3(5)
1.2.2.1 Basics
3(1)
1.2.2.2 Chapman–Kolmogorov equations
4(1)
1.2.2.3 Steady-state probabilities
5(1)
1.2.2.4 Graph associated with a Markov process
6(1)
1.2.2.5 Application to production systems
6(2)
1.2.3 Markov chains
8(6)
1.2.3.1 Basics
8(1)
1.2.3.2 State probability vectors
9(1)
1.2.3.3 Fundamental equation of a Markov chain
9(1)
1.2.3.4 Graph associated with a Markov chain
10(1)
1.2.3.5 Steady states of ergodic Markov chains
11(1)
1.2.3.6 Application to production systems
12(2)
1.3 Petri nets
14(22)
1.3.1 Introduction to Petri nets
14(6)
1.3.1.1 Basic definitions
14(1)
1.3.1.2 Dynamics of Petri nets
15(1)
1.3.1.3 Specific structures
16(2)
1.3.1.4 Tools for Petri net analysis
18(1)
1.3.1.5 Properties of Petri nets
19(1)
1.3.2 Non-autonomous Petri nets
20(1)
1.3.3 Timed Petri nets
20(3)
1.3.4 Continuous Petri nets
23(4)
1.3.4.1 Fundamental equation and performance analysis
24(1)
1.3.4.2 Example
25(2)
1.3.5 Colored Petri nets
27(1)
1.3.6 Stochastic Petri nets
28(8)
1.3.6.1 Firing time
29(1)
1.3.6.2 Firing selection policy
29(1)
1.3.6.3 Service policy
30(1)
1.3.6.4 Memory policy
30(1)
1.3.6.5 Petri net analysis
30(1)
1.3.6.6 Marking graph
31(1)
1.3.6.7 Generator of Markovian processes
31(1)
1.3.6.8 Fundamental equation
32(1)
1.3.6.9 Steady-state probabilities
32(3)
1.3.6.10 Performance indices (steady state)
35(1)
1.4 Discrete-event simulation
36(21)
1.4.1 The role of simulation in logistics systems analysis
36(1)
1.4.2 Components and dynamic evolution of systems
37(1)
1.4.3 Representing chance and the Monte Carlo method
38(3)
1.4.3.1 Uniform distribution U[ 0, 1]
38(1)
1.4.3.2 The Monte Carlo method
39(2)
1.4.4 Simulating probability distributions
41(11)
1.4.4.1 Simulating random events
41(3)
1.4.4.2 Simulating discrete random variables
44(3)
1.4.4.3 Simulating continuous random variables
47(5)
1.4.5 Discrete-event systems
52(5)
1.4.5.1 Key aspects of simulation
52(5)
1.5 Decomposition method
57(4)
1.5.1 Presentation
57(1)
1.5.2 Details of the method
58(3)
Chapter 2 Optimization 61(32)
2.1 Introduction
61(1)
2.2 Polynomial problems and NP-hard problems
62(2)
2.2.1 The complexity of an algorithm
62(1)
2.2.2 Example of calculating the complexity of an algorithm
63(1)
2.2.3 Some definitions
64(1)
2.2.3.1 Polynomial-time algorithms
64(1)
2.2.3.2 Pseudo-polynomial-time algorithms
64(1)
2.2.3.3 Exponential-time algorithms
64(1)
2.2.4 Complexity of a problem
64(1)
2.2.4.1 Polynomial-time problems
64(1)
2.2.4.2 NP-hard problems
64(1)
2.3 Exact methods
64(2)
2.3.1 Mathematical programming
64(1)
2.3.2 Dynamic programming
65(1)
2.3.3 Branch and bound algorithm
65(1)
2.4 Approximate methods
66(13)
2.4.1 Genetic algorithms
67(3)
2.4.1.1 General principles
67(1)
2.4.1.2 Encoding the solutions
67(1)
2.4.1.3 Crossover operators
68(2)
2.4.1.4 Mutation operators
70(1)
2.4.1.5 Constructing the population in the next generation
70(1)
2.4.1.6 Stopping condition
70(1)
2.4.2 Ant colonies
70(2)
2.4.2.1 General principle
70(1)
2.4.2.2 Management of pheromones: example of the traveling salesman problem
71(1)
2.4.3 Tabu search
72(4)
2.4.3.1 Initial solution
73(1)
2.4.3.2 Representing the solution
73(1)
2.4.3.3 Creating the neighborhood
74(1)
2.4.3.4 The tabu list
75(1)
2.4.3.5 An illustrative example
76(1)
2.4.4 Particle swarm algorithm
76(3)
2.4.4.1 Description
76(1)
2.4.4.2 An illustrative example
77(2)
2.5 Multi-objective optimization
79(10)
2.5.1 Definition
79(1)
2.5.2 Resolution methods
80(1)
2.5.3 Comparison criteria
81(1)
2.5.3.1 The Riise distance
81(1)
2.5.3.2 The Zitzler measure
82(1)
2.5.4 Multi-objective optimization methods
82(7)
2.5.4.1 Exact methods
82(2)
2.5.4.2 Approximate methods
84(5)
2.6 Simulation-based optimization
89(4)
2.6.1 Dedicated tools
90(1)
2.6.2 Specific methods
90(3)
Chapter 3 Design and Layout 93(50)
3.1 Introduction
93(1)
3.2 The different types of production system
94(3)
3.3 Equipment selection
97(13)
3.3.1 General overview
97(2)
3.3.2 Equipment selection with considerations of reliability
99(11)
3.3.2.1 Introduction to reliability optimization
99(1)
3.3.2.2 Design of a parallel-series system
100(10)
3.4 Line balancing
110(4)
3.4.1 The classification of line balancing problems
111(1)
3.4.1.1 The simple assembly line balancing model (SALB)
111(1)
3.4.1.2 The general assembly line balancing model (GALB)
112(1)
3.4.2 Solution methods
112(1)
3.4.2.1 Exact methods
112(1)
3.4.2.2 Approximate methods
113(1)
3.4.3 Literature review
113(1)
3.4.4 Example
113(1)
3.5 The problem of buffer sizing
114(18)
3.5.1 General overview
116(1)
3.5.2 Example of a multi-objective buffer sizing problem
116(1)
3.5.3 Example of the use of genetic algorithms
117(2)
3.5.3.1 Representation of the solutions
117(1)
3.5.3.2 Calculation of the objective function
118(1)
3.5.3.3 Selection of solutions for the archive
119(1)
3.5.3.4 New population and stopping criterion
119(1)
3.5.4 Example of the use of ant colony algorithms
119(4)
3.5.4.1 Encoding
120(1)
3.5.4.2 Construction of the ant trails
121(1)
3.5.4.3 Calculation of the visibility
121(1)
3.5.4.4 Global and local updates of the pheromones
122(1)
3.5.5 Example of the use of simulation-based optimization
123(9)
3.5.5.1 Simulation model
125(4)
3.5.5.2 Optimization algorithms
129(1)
3.5.5.3 The pairing of simulation and optimization
130(1)
3.5.5.4 Results and comparison
130(2)
3.6 Layout
132(11)
3.6.1 Types of facility layout
132(1)
3.6.1.1 Logical layout
132(1)
3.6.1.2 Physical layout
133(1)
3.6.2 Approach for treating a layout problem
133(2)
3.6.2.1 Linear layout
134(1)
3.6.2.2 Functional layout
135(1)
3.6.2.3 Cellular layout
135(1)
3.6.2.4 Fixed layout
135(1)
3.6.3 The best-known methods
135(1)
3.6.4 Example of arranging a maintenance facility
136(4)
3.6.5 Example of laying out an automotive workshop
140(3)
Chapter 4 Tactical Optimization 143(90)
4.1 Introduction
143(1)
4.2 Demand forecasting
143(12)
4.2.1 Introduction
143(1)
4.2.2 Categories and methods
144(1)
4.2.3 Time series
145(1)
4.2.4 Models and series analysis
146(9)
4.2.4.1 Additive models
147(2)
4.2.4.2 Multiplicative model
149(1)
4.2.4.3 Exponential smoothing
150(5)
4.3 Stock management
155(23)
4.3.1 The different types of stocked products
156(1)
4.3.2 The different types of stocks
157(1)
4.3.3 Storage costs
157(2)
4.3.4 Stock management
159(4)
4.3.4.1 Functioning of a stock
159(2)
4.3.4.2 Stock monitoring
161(1)
4.3.4.3 Stock valuation
162(1)
4.3.5 ABC classification method
163(2)
4.3.6 Economic quantities
165(9)
4.3.6.1 Economic quantity: the Wilson formula
166(1)
4.3.6.2 Economic quantity with a discount threshold
167(1)
4.3.6.3 Economic quantity with a uniform discount
168(1)
4.3.6.4 Economic quantity with a progressive discount
169(1)
4.3.6.5 Economic quantity with a variable ordering cost
170(1)
4.3.6.6 Economic quantity with order consolidation
171(1)
4.3.6.7 Economic quantity with a non-zero delivery time
172(1)
4.3.6.8 Economic quantity with progressive input
172(1)
4.3.6.9 Economic quantity with tolerated shortage
173(1)
4.3.7 Replenishment methods
174(4)
4.3.7.1 The (r, Q) replenishment method
175(1)
4.3.7.2 The (T, S) replenishment method
175(1)
4.3.7.3 The (s, S) replenishment method
175(1)
4.3.7.4 The (T,r, 8) replenishment method
176(1)
4.3.7.5 The (T,r,Q) replenishment method
177(1)
4.3.7.6 Security stock
177(1)
4.4 Cutting and packing problems
178(8)
4.4.1 Classifying cutting and packing problems
179(4)
4.4.2 Packing problems in industrial systems
183(3)
4.4.2.1 Model
183(2)
4.4.2.2 Solution
185(1)
4.5 Production and replenishment planning, lot-sizing methods
186(12)
4.5.1 Introduction
186(1)
4.5.2 MRP and lot-sizing
186(1)
4.5.3 Lot-sizing methods
187(3)
4.5.3.1 The characteristic elements of the models
188(1)
4.5.3.2 Lot-sizing in the scientific literature
189(1)
4.5.4 Examples
190(8)
4.5.4.1 The Wagner-Whitin method
191(2)
4.5.4.2 The Florian and Klein method
193(5)
4.6 Quality management
198(35)
4.6.1 Evaluation, monitoring and improvement tools
198(7)
4.6.1.1 The objective of metrology
198(1)
4.6.1.2 Concepts of error and uncertainty
198(1)
4.6.1.3 Statistical quality control
199(1)
4.6.1.4 Stages of control
199(1)
4.6.1.5 Tests of normality
200(5)
4.6.2 Types of control
205(29)
4.6.2.1 Reception or final control
205(1)
4.6.2.2 Reception control by measurement
206(3)
4.6.2.3 Manufacturing control
209(5)
4.6.2.4 Control charts
214(19)
Chapter 5 Scheduling 233(40)
5.1 Introduction
233(1)
5.2 Scheduling problems
234(39)
5.2.1 Basic notions
234(1)
5.2.2 Notation
234(1)
5.2.3 Definition of the criteria and objective functions
234(5)
5.2.3.1 Flow time
235(1)
5.2.3.2 Lateness
235(1)
5.2.3.3 Tardiness
235(1)
5.2.3.4 The earliness
236(1)
5.2.3.5 Objective functions
236(2)
5.2.3.6 Properties of schedules
238(1)
5.2.4 Project scheduling
239(15)
5.2.4.1 Definition of a project
239(1)
5.2.4.2 Projects with unlimited resources
240(7)
5.2.4.3 Projects with consumable resources
247(5)
5.2.4.4 Minimal-cost scheduling
252(2)
5.2.5 Single-machine problems
254(13)
5.2.5.1 Minimization of the mean flow time 1/ri = 0/Σ Ci
254(3)
5.2.5.2 Minimization of the mean weighted flow time 1/ri =0/ΣwiCi
257(1)
5.2.5.3 Minimization of the mean flow time 1/ri,pmtn/ΣCi
257(2)
5.2.5.4 Minimization of the maximum tardiness Tmax, 1/ri=0/Tmax
259(2)
5.2.5.5 Minimization of the maximum tardiness when the jobs have different arrival dates, with pre-emption 1/ri, pmtn/Tmax
261(1)
5.2.5.6 Minimization of the mean tardiness 1//T
261(4)
5.2.5.7 Minimization of the flow time 1/ri/F
265(2)
5.2.6 Scheduling a flow shop workshop
267(3)
5.2.6.1 The two-machine problem
267(1)
5.2.6.2 A particular case of the three-machine problem
268(1)
5.2.6.3 The m-machine problem
268(2)
5.2.7 Parallel-machine problems
270(3)
5.2.7.1 Identical machines, ri 0, Min F
270(1)
5.2.7.2 Identical machines, ri = 0, Min Cmax, interruptible jobs
271(2)
Bibliography 273(12)
Index 285
Dr Alice Yalaoui is associate professor at the University of Technology of Troyes, France.

Dr Hicham Chehade is an assistant professor at the University of Technology of Troyes (UTT), France.

Professor Farouk Yalaou, is full professor at the University of Technology of Troyes, France (UTT), France.

Professor Lionel Amodeo, is full professor at the University of Technology of Troyes, France (UTT), France.
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