Applying Genetic Algorithm for Min-Max Vehicle Routing Problem

2011 ◽  
Vol 97-98 ◽  
pp. 640-643 ◽  
Author(s):  
Chun Yu Ren
Keyword(s):  
Genetic Algorithm ◽  
Natural Number ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Mutation Operator ◽  
Routing Problem ◽  
Insertion Method ◽  
Searching Ability ◽  
Improved Algorithm

The present study is focused on the Min-Max Vehicle Routing Problem (MMVRP). Genetic algorithm is used to get the optimization solution. First of all, use natural number coding so as to simplify the problem; apply insertion method so as to improve the feasibility; retain the best selection so as to guard the diversity of group; adopt 2- exchange mutation operator to strengthen the partial searching ability of chromosome. Secondly, the improved route crossover operation can avoid destroying good gene parts. Finally, the good performance of improved algorithm can be proved by experiment calculation and concrete examples

Study on Improved Genetic Algorithm for Min-Max Vehicle Routing Problem

2011 ◽  
Vol 225-226 ◽  
pp. 1266-1269 ◽  
Author(s):  
Chun Yu Ren
Keyword(s):  
Genetic Algorithm ◽  
Natural Number ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Adaptive Method ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Insertion Method ◽  
Searching Ability ◽  
Self Adaptive

The present study is focused on the Min-Max Vehicle Routing Problem (MMVRP). Improved genetic algorithm is used to get the optimization solution. First of all, use natural number coding so as to simplify the problem; apply insertion method so as to improve the feasibility; retain the best selection so as to guard the diversity of group; adopt self-adaptive method to strengthen the partial searching ability of chromosome. Finally, the good performance of improved algorithm can be proved by experiment calculation and concrete examples.

Research on Vehicle Routing Problem Models and Algorithms of Batteries Logistics Distribution for Electric Vehicle

2013 ◽  
Vol 791-793 ◽  
pp. 1409-1414 ◽  
Author(s):  
Meng Wang ◽  
Kai Liu ◽  
Zhu Long Jiang
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Distribution Model ◽  
Initial Population ◽  
Mutation Operator ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Short Period

The battery quick exchange mode is an effective solution to resolve the battery charging problem of electric vehicle. For the electric vehicle battery distribution network with the battery quick exchange mode, the distribution model and algorithm are researched; the general mathematical model to take delivery of the vehicle routing problem with time window (VRP-SDPTW) is established. By analyzing the relationship between the main variables, structure priority function of the initial population, a new front crossover operator, swap mutation operator and reverse mutation operator are designed, and an improved genetic algorithm solving VRP-SDPTW is constructed. The algorithm could overcome the traditional genetic algorithm premature convergence defects. The example shows that the improved genetic algorithm can be effective in the short period of time to obtain the satisfactory solution of the VRP-SDPTW.

A Kind of Improved Genetic Algorithm for Multi-Depot Vehicle Routing Problem

2013 ◽  
Vol 347-350 ◽  
pp. 3273-3277
Author(s):  
Wan Xiang Lian ◽  
Can Shi Zhu ◽  
Jiang Hua Hu ◽  
Dong Feng Zhang ◽  
Duan Liu
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Degree Of Polymerization ◽  
Search Efficiency ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Np Hard Problem ◽  
Algorithm Search ◽  
Improved Algorithm

Multi-Depot Vehicle routing problem is an NP-HARD problem. Because the genetic algorithm is easy premature convergence and search efficiency is not high, this paper established the defects of polymerization degree model, and based on this, this paper proposes an improved algorithm, this algorithm can change the mutation rate according to their own chromosome degree of polymerization size to avoid the prematurity of genetic algorithm, and improved genetic algorithm search efficiency. Through the contrast, the results showed that the algorithm has good search efficiency and stability, which demonstrates that the improved algorithm is effective and feasible for multi-depot vehicle routing problem.

Research on Improved Genetic Algorithm for Heterogeneous Open Vehicle Routing Problem

2011 ◽  
Vol 55-57 ◽  
pp. 859-862
Author(s):  
Chun Yu Ren
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Open Vehicle Routing Problem ◽  
The Individual ◽  
Searching Ability ◽  
Standard Genetic Algorithm ◽  
Open Vehicle Routing

This paper studies heterogeneous open vehicle routing problem. Since the standard genetic algorithm is short of convergent speed and partial searching ability as well as easily premature, improved genetic algorithm is then adopted as an optimized solution. Firstly, sequence of real numbers coding is used to simplify the problem; it may construct the initial solution pertinently in order to improve the feasibility. The individual amount control choice strategy can guard the diversity of group. The adopting some arithmetic crossover operator can enhance local search ability of the chromosome. Finally, comparing to standard genetic algorithm, simulation results demonstrate the effectiveness and good quality.

Improved Genetic Algorithm for Capacitated Vehicle Routing Problem

2012 ◽  
Vol 253-255 ◽  
pp. 1459-1462
Author(s):  
Chun Yu Ren
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hill Climbing ◽  
Test Problems ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Capacitated Vehicle Routing Problem ◽  
Searching Ability ◽  
Capacitated Vehicle

This paper studies capacitated vehicle routing problem. Since the standard genetic algorithm is short of convergent speed and partial searching ability as well as easily premature, improved genetic algorithm is then adopted as an optimized solution. Firstly, sequence of real numbers coding is used to simplify the problem; it may construct the initial solution pertinently in order to improve the feasibility. The individual amount control choice strategy can guard the diversity of group. The combined hill-climbing algorithm can strengthen the partial searching ability of chromosome. Finally, comparing to a set of standard test problems, simulation results demonstrate the effectiveness and good quality.

scholarly journals EODVGA: An Enhanced ODV Based Genetic Algorithm for Multi-Depot Vehicle Routing Problem

2019 ◽  
Vol 6 (21) ◽  
pp. 159099
Author(s):  
Prabu U ◽  
Ravisasthiri P ◽  
Sriram R ◽  
Malarvizhi N ◽  
Amudhavel J
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem
Sign in / Sign up

Export Citation Format

Share Document