Optimization of Logistics

Alice Yalaoui,

Hicham Chehade,

Farouk Yalaoui

E-Book (epub), 320 Seiten

(0)

Erste Bewertung abgeben

This book aims to help engineers, Masters students and young researchers to understand and gain a general knowledge of logistic s...

CHF115.00

Download steht sofort bereit

E-Book (epub)

Beschreibung

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.
Autorentext
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.
Inhalt
Introduction xiii Chapter 1. Modeling and Performance Evaluation 1 1.1. Introduction 1 1.2. Markovian processes 2 1.2.1. Overview of stochastic processes 2 1.2.2. Markov processes 3 1.2.2.1. Basics 3 1.2.2.2. ChapmanKolmogorov equations 4 1.2.2.3. Steady-state probabilities 5 1.2.2.4. Graph associated with a Markov process 6 1.2.2.5. Application to production systems 6 1.2.3. Markov chains 8 1.2.3.1. Basics 8 1.2.3.2. State probability vectors 9 1.2.3.3. Fundamental equation of a Markov chain 9 1.2.3.4. Graph associated with a Markov chain 10 1.2.3.5. Steady states of ergodic Markov chains 11 1.2.3.6. Application to production systems 12 1.3. Petri nets 14 1.3.1. Introduction to Petri nets 14 1.3.1.1. Basic definitions 14 1.3.1.2. Dynamics of Petri nets 15 1.3.1.3. Specific structures 16 1.3.1.4. Tools for Petri net analysis 18 1.3.1.5. Properties of Petri nets 19 1.3.2. Non-autonomous Petri nets 20 1.3.3. Timed Petri nets 20 vi Optimization of Logistics 1.3.4. Continuous Petri nets 23 1.3.4.1. Fundamental equation and performance analysis 24 1.3.4.2. Example 25 1.3.5. Colored Petri nets 27 1.3.6. Stochastic Petri nets 28 1.3.6.1. Firing time 29 1.3.6.2. Firing selection policy 29 1.3.6.3. Service policy 30 1.3.6.4. Memory policy 30 1.3.6.5. Petri net analysis 30 1.3.6.6. Marking graph 31 1.3.6.7. Generator of Markovian processes 31 1.3.6.8. Fundamental equation 32 1.3.6.9. Steady-state probabilities 32 1.3.6.10. Performance indices (steady state) 35 1.4. Discrete-event simulation 36 1.4.1. The role of simulation in logistics systems analysis 36 1.4.2. Components and dynamic evolution of systems 37 1.4.3. Representing chance and the Monte Carlo method 38 1.4.3.1. Uniform distribution U [0, 1] 38 1.4.3.2. The Monte Carlo method 39 1.4.4. Simulating probability distributions 41 1.4.4.1. Simulating random events 41 1.4.4.2. Simulating discrete random variables 44 1.4.4.3. Simulating continuous random variables 47 1.4.5. Discrete-event systems 52 1.4.5.1. Key aspects of simulation 52 1.5. Decomposition method 57 1.5.1. Presentation 57 1.5.2. Details of the method 58
Chapter 2. Optimization 61 2.1. Introduction 61 2.2. Polynomial problems and NP-hard problems 62 2.2.1. The complexity of an algorithm 62 2.2.2. Example of calculating the complexity of an algorithm 63 2.2.3. Some definitions 64 2.2.3.1. Polynomial-time algorithms 64 2.2.3.2. Pseudo-polynomial-time algorithms 64 2.2.3.3. Exponential-time algorithms 64 2.2.4. Complexity of a problem 64 2.2.4.1. Polynomial-time problems 64 2.2.4.2. NP-hard problems 64 2.3. Exact methods 64 2.3.1. Mathematical programming 64 2.3.2. Dynamic programming 65 2.3.3. Branch and bound algorithm 65 2.4. Approximate methods 66 2.4.1. Genetic algorithms 67 2.4.1.1. General principles 67 2.4.1.2. Encoding the solutions 67 2.4.1.3. Crossover operators 68 2.4.1.4. Mutation operators 70 2.4.1.5. Constructing the population in the next generation 70 2.4.1.6. Stopping condition 70 2.4.2. Ant colonies 70 2.4.2.1. General principle 70 2.4.2.2. Management of pheromones: example of the traveling salesman problem 71 2.4.3. Tabu search 72 2.4.3.1. Initial solution 73 2.4.3.2. Representing the solution 73 2.4.3.3. Creating the neighborhood 74 2.4.3.4. The tabu list 75 2.4.3.5. An illustrative ex...