Contents & References of Solving the multi-depot vehicle routing problem with a time window using an efficient meta-engineering algorithm
Chapter one: General research. 1
1-1 Introduction. 2
1-2- Necessity and importance of transportation planning. 3
1-3- Transportation in Iran. 4
1-4- The purpose of conducting the study. 5
1-5- Definition of the problem. 6
1-6- Summarizing and presenting the content. 7
Chapter Two: Research literature. 9
2-1-Introduction. 10
2-2- Routing problem. 10
2-3- The issue of the round seller. 10
2-4- Vehicle routing issue. 13
2-5- Components of the VRP problem. 14
2-5-1- General characteristics of customers. 14
2-5-2 characteristics of vehicles. 15
2-5-4- Types of objective functions in VRP. 16
2-5-6 Some problems of VRP modeling in real conditions. 16
2-6-Mathematical definition of the vehicle routing problem in general. 17
2-6-1 General model of VRP problem. 18
2-7-Methods for solving classic vehicle routing problems. 20
2-7-1-exact methods. 20
2-7-2-innovative methods. 22
2-7-3- Metaheuristic methods. 24
2-8- Main types of vehicle routing problem. 26
2-8-1 Vehicle routing with limited vehicle capacity. 27
2-8-2-The problem of routing vehicles with heterogeneous fleet. 28
2-8-3-The issue of vehicle routing with delivery division. 30
2-8-4- Vehicle routing with delivery and collection. 33
2-8-5- Routing problem of vehicle rounds. 34
2-8-5-1 Mathematical definition of periodic vehicle routing problem (PVRP) 35
2-8-5-2- Mathematical model of PVRP. 37
2-8-6- The problem of routing vehicles with multiple warehouses. 41
2-8-6-1- Mathematical definition of MDVRP problem. 42
2-8-7- Vehicle routing problem with time window. 44
2-8-7-1 Division of VRPTW problem. 45
2-8-7-1-1 Hard time window models. 46
2-8-7-1-2- models of soft time windows. 46
2-9- Summary. 53
Chapter three: research method. 55
3-1 Introduction. 56
3-2 Characteristics and assumptions of the model. 56
3-2-1- Assumptions. 56
3-2-2 Definition of signs and parameters 56
3-2-2-1 indices 57
3-2-2-2 parameters 57
3-2-2-3 decision variables. 58
3-2-2-4 Mathematical model. 58
3-3 Overview of Genetic Algorithm (GA) 60
3-3-1 Definition. 60
3-3-2 passage on natural genetics. 61
3-3-3 vocabulary of genetic algorithm. 66
3-3-4 General structure of genetic algorithm. 67
3-3-5 key concepts of genetic algorithm. 68
3-3-6 Coding. 69
3-3-7 Creating the initial population. 71
3-3-8 Applying genetics. 71
3-3-8-1 Act of transformation. 72
3-3-8-1-1 Sampling space. 72
3-3-8-1-2 Sampling mechanism. 73
3-3-8-1-4 elitism. 75
3-3-8-2 Combined operators. 75
3-3-8-2-1 types of compound operators. 75
3-3-8 -2 -2 combination possibility. 78
3-3-8-3 jump operators. 79
3-3-8-3-1 types of jump operators. 80
3-3-9 fitting function. 81
3-3-10 method of implementing the genetic algorithm. 82
3-4 proposed structure of genetic algorithm. 84
3-4-1 Genetic clustering. 84
3-4-1-1 Showing the string (chromosome) 84
3-4-1-2 Creating the initial population. 85
3-4-1-3 Calculation of the fitting function. 85
3-4-1-3 selection. 85
3-4-1-4 combination. 86
3-4-1-5 mutation. 86
3-4-1-6 Stop condition. 87
3-4-2 genetic algorithm. 87
3-4-2-1 How to display the answers 87
3-4-2-2 Defining the degree of fitness. 88
3-4-2-3 selection mechanism. 89
3-4-2-3 combination operator. 89
3-4-2-4 mutation operator. 91
3-5 K-Mean algorithm. 92
3-6 Fuzzy c-mean clustering algorithm (FCM). 92
Chapter four: Data collection and analysis 95
4-1 Introduction. 96
4-2 software features. 96
4-3 Specifications of sample problems. 96
4-4 Determination of parameters 97
4-5 Calculation results. 97
4-6 summary. 102
Chapter Five: Conclusion. 103
5-1 Conclusion. 104
5-2 Future research. 104
Resources and sources. 106