基于GSA方法的多级库存优化问题研究

Chapter 1 Introduction 1.1 Inventory Management 1.2 Multi-Echelon Inventory Systems 1.3 Multi-Echelon Inventory Management 1.4 Models and Methods Used for Multi-Echelon Inventory Management 1.4.1 Inventory Models 1.4.2 Inventory Policies 1.4.3 Inventory Optimization Approaches 1.5 The Problems Studied in This Thesis 1.6 Literature Review of Multi-Echelon Inventory Management 1.6.1 General Studies of Multi-Echelon Inventory Management 1.6.2 Stochastic Service Approach for Serial Inventory Systems 1.6.3 Stochastic Service Approach for Assembly Inventory Systems 1.6.4 Stochastic Service Approach for Distribution Inventory Systems 1.6.5 Guaranteed Service Approach for Multi-Echelon Inventory Systems 1.6.6 Comparison of Stochastic-Service Approach and Guaranteed-Service Approach 1.7 The Contributions of the Thesis 1.8 Conclusion Chapter 2 Preliminaries 2.1 Fundamentals 2.1.1 Network Structures 2.1.2 Demand Processes 2.2 Inventory Control 2.2.1 Inventory Accounting 2.2.2 Batch Ordering (R, Q) Policy 2.2.3 Performance Measures for Inventory Control 2.3 Guaranteed Service Approach 2.4 Operating Flexibility and GSA 2.5 Batch Ordering (R, Q) Policy and GSA Chapter 3 Optimization of (R, Q) Policies for Serial Systems 3.1 Problem Description 3.1.1 Serial System Studied 3.1.2 Maximum Reasonable Lead Time Demand Level 3.1.3 Cost Structure 3.2 Mathematical Model Formulation 3.2.1 Definitions and Notations 3.2.2 Objective Function 3.2.3 Model Formulation 3.3 Dynamic Programming Algorithms for Q-problem... 3.3.1 Basic Principle of DP 3.3.2 Dynamic Programming Algorithm 3.4 Dynamic Programming Algorithm for R-problem 3.5 Optimization Procedure 3.5.1 The Calculation of the Fill Rate fl 3.5.2 Algorithm for Original Model P 3.6 Experiments Results 3.6.1 Experiments for the Resolution of Q-problem 3.6.2 Experiments for the Resolution of R-problem 3.6.3 Experiments for the Resolution of Problem P with a Given Service Level 3.6.4 Structural Analysis of the (R, Q) Policy Found by the GSA 3.7 Conclusion Chapter 4 Optimization of (R, Q) Policies for Assembly Systems 4.1 Problem Description 4.2 Mathematical Model Formulation 4.3 Dynamic Programming Algorithm for Q-problem 4.3.1 Assumptions and Notations 4.3.2 State Space of Qi 4.3.3 State Space Reduction 4.3.4 Dynamic Programme Algorithm 4.4 Dynamic Programming Algorithm for R-problem 4.5 Optimization Procedure 4.6 Experiments Results 4.6.1 Experiments for the Resolution of Q-problem 4.6.2 Experiments for the Resolution of R-problem 4.6.3 Experiments for the Sensitivity Analysis for the Two Algorithms 4.6.4 Experiments for the Resolution of Problem P with a Given Service Level 4.7 Conclusions Chapter 5 Optimization of (R, Q) Policies for Two-Level Distribution Systems 5.1 Problem Description 5.2 Mathematical Model Formulation 5.3 Dynamic Programming Algorithm for Q-problem 5.3.l Integer-ratio Constraints for Q-problem 5.3.2 Dynamic Programming for Q-problem 5.4 Dynamic Programming Algorithm for R-problem 5.5 Optimization Procedure 5.6 Numerical Experiments 5.6.1 Experiments for the Resolution of Q-problem 5.6.2 Experiments for the Resolution of R-problem 5.6.3 Experiments for the Resolution of Problem P with a Given Service Level 5.7 Conclusion Chapter 6 Conclusions and Perspectives Resume en Francais References