Dealing with vehicle routing problem under multi-objective using improved genetic algorithm

2014 ◽  
Author(s):  
Hui Liu ◽  
Yongduan Song
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Improved Genetic Algorithm ◽  
Routing Problem ◽  
Multi Objective

Solving vehicle routing problem by using improved genetic algorithm for optimal solution

2017 ◽  
Vol 21 ◽  
pp. 255-262 ◽  
Author(s):  
Mazin Abed Mohammed ◽  
Mohd Khanapi Abd Ghani ◽  
Raed Ibraheem Hamed ◽  
Salama A. Mostafa ◽  
Mohd Sharifuddin Ahmad ◽  
...  
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Optimal Solution ◽  
Improved Genetic Algorithm ◽  
Routing Problem

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.

Optimization of Vehicle Routing Problem Based on Multi-Objective Genetic Algorithm

2012 ◽  
Vol 253-255 ◽  
pp. 1356-1359
Author(s):  
Ru Zhong ◽  
Jian Ping Wu ◽  
Yi Man Du
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Heuristic Algorithm ◽  
Efficient Method ◽  
Vehicle Routing Problem ◽  
Multiple Objectives ◽  
Routing Problem ◽  
Multi Objective ◽  
Multi Objective Genetic Algorithm

When there are multiple objectives co-existent in Vehicle routing problem(VRP), it is difficult to achieve optical status simultaneously. To solve this issue, it introduces a method of improved multi-objective Genetic Algorithm (MOGA). It adopts an approach close to heuristic algorithm to cultivate partial viable chromosomes, route decoding to ensure that all individuals meet constraints and uses relatively efficient method of arena contest to construct non-dominated set. Finally programme to fulfill the multi-objective algorithm and then apply it in the standard example of VRP to verity its effectiveness by comparison with the existing optimal results.

scholarly journals A complexity task of optimization in logistic distribution: A new approach to the green multi-objective vehicle routing problem

2022 ◽  
Vol 38 (1) ◽  
Author(s):  
Ferreira J. ◽  
Steiner M.
Keyword(s):  
Genetic Algorithm ◽  
Objective Function ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Logistic Distribution ◽  
Nsga Ii ◽  
Routing Problem ◽  
Multi Objective ◽  
The Difference ◽  
Stage 1

Logistic distribution involves many costs for organizations. Therefore, opportunities for optimization in this respect are always welcome. The purpose of this work is to present a methodology to provide a solution to a complexity task of optimization in Multi-objective Optimization for Green Vehicle Routing Problem (MOOGVRP). The methodology, illustrated using a case study (employee transport problem) and instances from the literature, was divided into three stages: Stage 1, “data treatment”, where the asymmetry of the routes to be formed and other particular features were addressed; Stage 2, “metaheuristic approaches” (hybrid or non-hybrid), used comparatively, more specifically: NSGA-II (Non-dominated Sorting Genetic Algorithm II), MOPSO (Multi-Objective Particle Swarm Optimization), which were compared with the new approaches proposed by the authors, CWNSGA-II (Clarke and Wright’s Savings with the Non-dominated Sorting Genetic Algorithm II) and CWTSNSGA-II (Clarke and Wright’s Savings, Tabu Search and Non-dominated Sorting Genetic Algorithm II); and, finally, Stage 3, “analysis of the results”, with a comparison of the algorithms. Using the same parameters as the current solution, an optimization of 5.2% was achieved for Objective Function 1 (OF{\displaystyle _{1}}; minimization of CO{\displaystyle _{2}} emissions) and 11.4% with regard to Objective Function 2 (OF{\displaystyle _{2}}; minimization of the difference in demand), with the proposed CWNSGA-II algorithm showing superiority over the others for the approached problem. Furthermore, a complementary scenario was tested, meeting the constraints required by the company concerning time limitation. For the instances from the literature, the CWNSGA-II and CWTSNSGA-II algorithms achieved superior results.

Sign in / Sign up

Export Citation Format

Share Document