| Description |
1 online resource (369 pages) |
| Series |
ISTE |
|
ISTE
|
| Contents |
Cover; 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; 1.2.3. Markov chains; 1.3. Petri nets; 1.3.1. Introduction to Petri nets; 1.3.2. Non-autonomous Petri nets; 1.3.3. Timed Petri nets; 1.3.4. Continuous Petri nets; 1.3.5. Colored Petri nets; 1.3.6. Stochastic Petri nets; 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 |
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1.4.3. Representing chance and the Monte Carlo method1.4.4. Simulating probability distributions; 1.4.5. Discrete-event systems; 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; 2.2.4. Complexity of a problem; 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 |
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2.4.1. Genetic algorithms2.4.2. Ant colonies; 2.4.3. Tabu search; 2.4.4. Particle swarm algorithm; 2.5. Multi-objective optimization; 2.5.1. Definition; 2.5.2. Resolution methods; 2.5.3. Comparison criteria; 2.5.4. Multi-objective optimization methods; 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; 3.4. Line balancing |
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3.4.1. The classification of line balancing problems3.4.2. Solution 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; 3.5.4. Example of the use of ant colony algorithms; 3.5.5. Example of the use of simulation-based optimization; 3.6. Layout; 3.6.1. Types of facility layout; 3.6.2. Approach for treating a layout problem; 3.6.3. The best-known methods; 3.6.4. Example of arranging a maintenance facility |
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3.6.5. Example of laying out an automotive workshopChapter 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; 4.3. 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; 4.3.5. ABC classification method; 4.3.6. Economic quantities; 4.3.7. Replenishment methods; 4.4. Cutting and packing problems; 4.4.1. Classifying cutting and packing problems |
| Summary |
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 (Pet |
| Notes |
4.4.2. Packing problems in industrial systems |
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Print version record |
| Subject |
Computer science -- Mathematics.
|
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Business logistics.
|
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Logistics -- Mathematical models
|
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Business -- Mathematical models.
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Business logistics
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Business -- Mathematical models
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Computer science -- Mathematics
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Logistics -- Mathematical models
|
| Form |
Electronic book
|
| Author |
Chehade, Hicham
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Yalaoui, Farouk
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Amodeo, Lionel
|
| ISBN |
9781118569573 |
|
1118569571 |
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