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 ε-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.