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 Hybrid Multi-objective Genetic Algorithm for Bi-objective Time Window Assignment Vehicle Routing Problem

2019 ◽  
Vol 31 (5) ◽  
pp. 513-525
Author(s):  
Manman Li ◽  
Jian Lu ◽  
Wenxin Ma
Keyword(s):  
Genetic Algorithm ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Window ◽  
Time Windows ◽  
Routing Problem ◽  
Multi Objective ◽  
Objective Time ◽  
Multi Objective Genetic Algorithm ◽  
Positive Effect

Providing a satisfying delivery service is an important way to maintain the customers’ loyalty and further expand profits for manufacturers and logistics providers. Considering customers’ preferences for time windows, a bi-objective time window assignment vehicle routing problem has been introduced to maximize the total customers’ satisfaction level for assigned time windows and minimize the expected delivery cost. The paper designs a hybrid multi-objective genetic algorithm for the problem that incorporates modified stochastic nearest neighbour and insertion-based local search. Computational results show the positive effect of the hybridization and satisfactory performance of the metaheuristics. Moreover, the impacts of three characteristics are analysed including customer distribution, the number of preferred time windows per customer and customers’ preference type for time windows. Finally, one of its extended problems, the bi-objective time window assignment vehicle routing problem with time-dependent travel times has been primarily studied.

A New Multi Objective Genetic Algorithm: Fitness Aggregated Genetic Algorithm (FAGA) for Vehicle Routing Problem

2014 ◽  
Vol 984-985 ◽  
pp. 1261-1268 ◽  
Author(s):  
V. Sivaram Kumar ◽  
M.R. Thansekhar ◽  
R. Saravanan
Keyword(s):  
Genetic Algorithm ◽  
Genetic Algorithms ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Suggested Algorithm ◽  
Multi Objective ◽  
Total Distance ◽  
Multi Objective Genetic Algorithm ◽  
Assignment Approach

This paper presents multi objective vehicle routing problem in which the total distance travelled by the vehicles and total number of vehicles used are minimized. In general, fitness assignment procedure, as one of the important operators, influences the effectiveness of multi objective genetic algorithms. In this paper genetic algorithm with different fitness assignment approach and specialized crossover called Fitness Aggregated Genetic Algorithm (FAGA) is introduced for solving the problem. The suggested algorithm is investigated on large number of popular benchmarks for vehicle routing problem. It is observed from the results that the suggested new algorithm is very effective and the solutions are competitive with the best known 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.

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