Optimization of Logistics

Name: Optimization of Logistics
Brand: Wiley-ISTE
Price: 137.99 EUR
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Alice Yalaoui Hicham Chehade Farouk Yalaoui Lionel Amodeo(Autor*in)

Wiley-ISTE (Verlag)

Erschienen am 13. Dezember 2012

320 Seiten

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Inhalt

Intro
Dedication
Title
Copyright
Chapter 1. Modeling and Performance Evaluation
1.1. Introduction
1.2. Markovian processes
1.2.1. Overview of stochastic processes
1.2.2. Markov processes
1.2.2.1. Basics
1.2.2.2. Chapman-Kolmogorov equations
1.2.2.3. Steady-state probabilities
1.2.2.4. Graph associated with a Markov process
1.2.2.5. Application to production systems
1.2.3. Markov chains
1.2.3.1. Basics
1.2.3.2. State probability vectors
1.2.3.3. Fundamental equation of a Markov chain
1.2.3.4. Graph associated with a Markov chain
1.2.3.5. Steady states of ergodic Markov chains
1.2.3.6. Application to production systems
1.3. Petri nets
1.3.1. Introduction to Petri nets
1.3.1.1. Basic definitions
1.3.1.2. Dynamics of Petri nets
1.3.1.3. Specific structures
1.3.1.4. Tools for Petri net analysis
1.3.1.5. Properties of Petri nets
1.3.1.5.1. Reachability
1.3.1.5.2. Boundedness
1.3.1.5.3. Liveness and blocking
1.3.1.5.4. Application to production systems
1.3.2. Non-autonomous Petri nets
1.3.3. Timed Petri nets
1.3.4. Continuous Petri nets
1.3.4.1. Fundamental equation and performance analysis
1.3.4.2. Example
1.3.5. Colored Petri nets
1.3.6. Stochastic Petri nets
1.3.6.1. Firing time
1.3.6.2. Firing selection policy
1.3.6.3. Service policy
1.3.6.4. Memory policy
1.3.6.5. Petri net analysis
1.3.6.6. Marking graph
1.3.6.7. Generator of Markovian processes
1.3.6.8. Fundamental equation
1.3.6.9. Steady-state probabilities
1.3.6.10. Performance indices (steady state)
1.4. Discrete-event simulation
1.4.1. The role of simulation in logistics systems analysis
1.4.2. Components and dynamic evolution of systems
1.4.3. Representing chance and the Monte Carlo method
1.4.3.1. Uniform distribution U [0, 1]
1.4.3.1.1. Definition of a pseudorandom sequence
1.4.3.1.2. Obtaining a pseudorandom sequence
1.4.3.2. The Monte Carlo method
1.4.3.2.1. General principle
1.4.3.2.2. Example of application
1.4.4. Simulating probability distributions
1.4.4.1. Simulating random events
1.4.4.1.1. The case of two possible events
1.4.4.1.2. Case of several mutually exclusive events
1.4.4.1.3. Case of dependent events
1.4.4.2. Simulating discrete random variables
1.4.4.2.1. General case
1.4.4.3. Simulating continuous random variables
1.4.4.3.1. Inverse function method
1.4.4.3.2. Case of the normal distribution
1.4.4.3.3. Complex continuous random variables
1.4.4.3.4. The approximation method
1.4.4.3.5. The exclusion method
1.4.4.3.6. The superposition method
1.4.5. Discrete-event systems
1.4.5.1. Key aspects of simulation
1.4.5.1.1. Description of the system
1.4.5.1.2. Procedure
1.4.5.1.3. Example of an application
1.4.5.1.4. Integrating data to carry out a random selection
1.4.5.1.5. Time management
1.4.5.1.6. Transitional regime
1.5. Decomposition method
1.5.1. Presentation
1.5.2. Details of the method
Chapter 2. Optimization
2.1. Introduction
2.2. Polynomial problems and NP-hard problems
2.2.1. The complexity of an algorithm
2.2.2. Example of calculating the complexity of an algorithm
2.2.3. Some definitions
2.2.3.1. Polynomial-time algorithms
2.2.3.2. Pseudo-polynomial-time algorithms
2.2.3.3. Exponential-time algorithms
2.2.4. Complexity of a problem
2.2.4.1. Polynomial-time problems
2.2.4.2. NP-hard problems
2.3. Exact methods
2.3.1. Mathematical programming
2.3.2. Dynamic programming
2.3.3. Branch and bound algorithm
2.4. Approximate methods
2.4.1. Genetic algorithms
2.4.1.1. General principles
2.4.1.2. Encoding the solutions
2.4.1.3. Crossover operators
2.4.1.4. Mutation operators
2.4.1.5. Constructing the population in the next generation
2.4.1.6. Stopping condition
2.4.2. Ant colonies
2.4.2.1. General principle
2.4.2.2. Management of pheromones: example of the traveling salesman problem
2.4.2.2.1. The construction of a solution
2.4.2.2.2. Pheromone update
2.4.3. Tabu search
2.4.3.1. Initial solution
2.4.3.2. Representing the solution
2.4.3.3. Creating the neighborhood
2.4.3.4. The tabu list
2.4.3.5. An illustrative example
2.4.4. Particle swarm algorithm
2.4.4.1. Description
2.4.4.2. An illustrative example
2.5. Multi-objective optimization
2.5.1. Definition
2.5.2. Resolution methods
2.5.3. Comparison criteria
2.5.3.1. The Riise distance
2.5.3.2. The Zitzler measure
2.5.4. Multi-objective optimization methods
2.5.4.1. Exact methods
2.5.4.1.1. The e-constraint method
2.5.4.1.2. The two-phase method
2.5.4.1.3. The parallel partitioning method
2.5.4.1.4. The K-PPM method
2.5.4.2. Approximate methods
2.5.4.2.1. Multi-objective genetic algorithms
2.5.4.2.2. NSGA
2.5.4.2.3. NSGA-II
2.5.4.2.4. The Strength Pareto Evolutionary algorithm
2.5.4.2.5. SPEA-II
2.5.4.2.6. Multi-objective ant colonies
2.5.4.2.7. Constructing the ant trails
2.5.4.2.8. Global and local updates of pheromones
2.6. Simulation-based optimization
2.6.1. Dedicated tools
2.6.2. Specific methods
Chapter 3. Design and Layout
3.1. Introduction
3.2. The different types of production system
3.3. Equipment selection
3.3.1. General overview
3.3.2. Equipment selection with considerations of reliability
3.3.2.1. Introduction to reliability optimization
3.3.2.2. Design of a parallel-series system
3.3.2.2.1. Definition of the system and models
3.3.2.2.2. Solution method
3.3.2.2.3. An example of an application
3.4. Line balancing
3.4.1. The classification of line balancing problems
3.4.1.1. The simple assembly line balancing model (SALB)
3.4.1.2. The general assembly line balancing model (GALB)
3.4.2. Solution methods
3.4.2.1. Exact methods
3.4.2.2. Approximate methods
3.4.3. Literature review
3.4.4. Example
3.5. The problem of buffer sizing
3.5.1. General overview
3.5.2. Example of a multi-objective buffer sizing problem
3.5.3. Example of the use of genetic algorithms
3.5.3.1. Representation of the solutions
3.5.3.2. Calculation of the objective function
3.5.3.3. Selection of solutions for the archive
3.5.3.4. New population and stopping criterion
3.5.4. Example of the use of ant colony algorithms
3.5.4.1. Encoding
3.5.4.2. Construction of the ant trails
3.5.4.3. Calculation of the visibility
3.5.4.4. Global and local updates of the pheromones
3.5.5. Example of the use of simulation-based optimization
3.5.5.1. Simulation model
3.5.5.2. Optimization algorithms
3.5.5.3. The pairing of simulation and optimization
3.5.5.4. Results and comparison
3.6. Layout
3.6.1. Types of facility layout
3.6.1.1. Logical layout
3.6.1.2. Physical layout
3.6.2. Approach for treating a layout problem
3.6.2.1. Linear layout
3.6.2.2. Functional layout
3.6.2.3. Cellular layout
3.6.2.4. Fixed layout
3.6.3. The best-known methods
3.6.4. Example of arranging a maintenance facility
3.6.5. Example of laying out an automotive workshop
Chapter 4. Tactical Optimization
4.1. Introduction
4.2. Demand forecasting
4.2.1. Introduction
4.2.2. Categories and methods
4.2.3. Time series
4.2.4. Models and series analysis
4.2.4.1. Additive models
4.2.4.2. Multiplicative model
4.2.4.3. Exponential smoothing
4.2.4.3.1. Simple exponential smoothing
4.2.4.3.2. Double exponential smoothing
4.3. Stock management
4.3.1. The different types of stocked products
4.3.2. The different types of stocks
4.3.3. Storage costs
4.3.4. Stock management
4.3.4.1. Functioning of a stock
4.3.4.2. Stock monitoring
4.3.4.3. Stock valuation
4.3.5. ABC classification method
4.3.6. Economic quantities
4.3.6.1. Economic quantity: the Wilson formula
4.3.6.2. Economic quantity with a discount threshold
4.3.6.3. Economic quantity with a uniform discount
4.3.6.4. Economic quantity with a progressive discount
4.3.6.5. Economic quantity with a variable ordering cost
4.3.6.6. Economic quantity with order consolidation
4.3.6.7. Economic quantity with a non-zero delivery time
4.3.6.8. Economic quantity with progressive input
4.3.6.9. Economic quantity with tolerated shortage
4.3.7. Replenishment methods
4.3.7.1. The (r, Q) replenishment method
4.3.7.2. The (T , S) replenishment method
4.3.7.3. The (s, S) replenishment method
4.3.7.4. The (T , r, S) replenishment method
4.3.7.5. The (T , r, Q) replenishment method
4.3.7.6. Security stock
4.4. Cutting and packing problems
4.4.1. Classifying cutting and packing problems
4.4.2. Packing problems in industrial systems
4.4.2.1. Model
4.4.2.2. Solution
4.4.2.2.1. The bottom left heuristc
4.4.2.2.2. Example
4.5. Production and replenishment planning, lot-sizing methods
4.5.2. MRP and lot-sizing
4.5.3. Lot-sizing methods
4.5.3.1. The characteristic elements of the models
4.5.3.1.1. The planning time frame and time scale
4.5.3.1.2. The demand
4.5.3.1.3. The constraints
4.5.3.1.4. The structure of the production process
4.5.3.1.5. The objective function
4.5.3.2. Lot-sizing in the scientific literature
4.5.3.2.1. Models with stationary demand and without capacity constraints
4.5.3.2.2. Models with dynamic demand and without capacity constraints
4.5.3.2.3. Models with dynamic demand and capacity constraints
4.5.4. Examples
4.5.4.1. The Wagner-Whitin method
4.5.4.1.1. Model
4.5.4.1.3. Example of an application
4.5.4.2. The Florian and Klein method
4.5.4.2.1. Model
4.5.4.2.2. Method
4.5.4.2.3. Solution of a subproblem
4.5.4.2.4. Example of an application
4.6. Quality management
4.6.1. Evaluation, monitoring and improvement tools
4.6.1.1. The objective of metrology
4.6.1.2. Concepts of error and uncertainty
4.6.1.3. Statistical quality control
4.6.1.4. Stages of control
4.6.1.5. Tests of normality
4.6.1.5.1. The normal distribution
4.6.1.5.2. Histograms
4.6.1.5.3. Henry?s test
4.6.1.5.4. Chi-squared test
4.6.2. Types of control
4.6.2.1. Reception or final control
4.6.2.2. Reception control by measurement
4.6.2.2.1. Plans based on the (known or unknown) standard deviation
4.6.2.2.2. Control plans based on tolerance limits
4.6.2.2.3. Simple sampling plan
4.6.2.2.4. Progressive sampling plan
4.6.2.3. Manufacturing control
4.6.2.3.1. Capability
4.6.2.3.2. Machine capability
4.6.2.3.3. The Cm machine capability
4.6.2.3.4. The Cmk machine capability
4.6.2.3.5. The process capability
4.6.2.3.6. The target capability: Cc (or Cpm)
4.6.2.4. Control charts
4.6.2.4.1. Description of a control chart
4.6.2.4.2. Sampling protocol
4.6.2.4.3. Measurement control charts
4.6.2.4.4. Estimation of the dispersion
4.6.2.4.5. Mean charts
4.6.2.4.6. Low capability or non-performance
4.6.2.4.7. High capability
4.6.2.4.8. Standard deviation (s) charts and range (R) charts
4.6.2.4.9. Summary of charts
4.6.2.4.10. Attribute-based control charts
4.6.2.4.11. (C) charts
4.6.2.4.12. Example of an application of control charts
Chapter 5. Scheduling
5.1. Introduction
5.2. Scheduling problems
5.2.1. Basic notions
5.2.2. Notation
5.2.3. Definition of the criteria and objective functions
5.2.3.1. Flow time
5.2.3.2. Lateness
5.2.3.3. Tardiness
5.2.3.4. The earliness
5.2.3.5. Objective functions
5.2.3.5.1. Minimization of the total flow time
5.2.3.5.2. Minimization of the total tardiness
5.2.3.5.3. Minimization of the total earliness
5.2.3.5.4. Minimization of the makespan
5.2.3.5.5. Minimization of the number of tardy jobs
5.2.3.5.6. Graham et al. notation
5.2.3.6. Properties of schedules
5.2.3.6.1. Regular criteria
5.2.3.6.2. Irregular criteria
5.2.3.6.3. Semi-active scheduling
5.2.3.6.4. Non-semi-active scheduling
5.2.3.6.5. Active scheduling
5.2.4. Project scheduling
5.2.4.1. Definition of a project
5.2.4.2. Projects with unlimited resources
5.2.4.2.1. Potential event representations
5.2.4.2.2. Potential job representation
5.2.4.2.3. Resolution methods
5.2.4.2.4. The Dijkstra method
5.2.4.2.5. The Bellman algorithm
5.2.4.3. Projects with consumable resources
5.2.4.3.1. Resolution method
5.2.4.4. Minimal-cost scheduling
5.2.5. Single-machine problems
5.2.5.1. Minimization of the mean flow time 1/ri = 0/SCi
5.2.5.2. Minimization of the mean weighted flow time 1/ri = 0/ SwiCi
5.2.5.3. Minimization of the mean flow time 1/ri, pmtn/ SCi
5.2.5.4. Minimization of the maximum tardiness Tmax, 1/ri = 0/Tmax
5.2.5.5. Minimization of the maximum tardiness when the jobs have different arrival dates, with pre-emption 1/ri, pmtn/Tmax
5.2.5.6. Minimization of the mean tardiness 1//T
5.2.5.6.1. Approximate method
5.2.5.6.2. Exact method
5.2.5.6.3. Lower bound
5.2.5.6.4. Dominance property 1
5.2.5.6.5. Dominance property 2
5.2.5.7. Minimization of the flow time 1/ri/F
5.2.5.7.1. P RT F method
5.2.5.7.2. EST method
5.2.5.7.3. AP RT F method
5.2.6. Scheduling a flow shop workshop
5.2.6.1. The two-machine problem
5.2.6.1.1. The Johnson rule
5.2.6.1.2. Implementation
5.2.6.1.3. An example of application
5.2.6.2. A particular case of the three-machine problem
5.2.6.3. The m-machine problem
5.2.6.3.1. The Palmer heuristic [PAL 65]
5.2.6.3.2. The Gupta heuristic [GUP 71]
5.2.6.3.3. The Campbell, Dudek and Smith heuristic [CAM 71]
5.2.7. Parallel-machine problems
5.2.7.1. Identical machines, ri = 0, M in F
5.2.7.2. Identical machines, ri = 0, M in Cmax, interruptible jobs
Bibliography
Index

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