A memetic algorithm for the multi-compartment vehicle routing problem with stochastic demands

2010 ◽  
Vol 37 (11) ◽  
pp. 1886-1898 ◽  
Author(s):  
Jorge E. Mendoza ◽  
Bruno Castanier ◽  
Christelle Guéret ◽  
Andrés L. Medaglia ◽  
Nubia Velasco
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Routing Problem ◽  
Stochastic Demands

A memetic algorithm for the multiperiod vehicle routing problem with profit

2013 ◽  
Vol 229 (3) ◽  
pp. 573-584 ◽  
Author(s):  
Zizhen Zhang ◽  
Oscar Che ◽  
Brenda Cheang ◽  
Andrew Lim ◽  
Hu Qin
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Routing Problem

Metaheuristics for the Vehicle Routing Problem with Stochastic Demands

2004 ◽  
pp. 450-460 ◽  
Author(s):  
Leonora Bianchi ◽  
Mauro Birattari ◽  
Marco Chiarandini ◽  
Max Manfrin ◽  
Monaldo Mastrolilli ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
Stochastic Demands

scholarly journals A Memetic Algorithm for the Green Vehicle Routing Problem

2019 ◽  
Vol 11 (21) ◽  
pp. 6055 ◽  
Author(s):  
Bo Peng ◽  
Yuan Zhang ◽  
Yuvraj Gajpal ◽  
Xiding Chen
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Memetic Algorithm ◽  
Population Based ◽  
Longest Common Subsequence ◽  
Routing Problem ◽  
Highly Effective ◽  
Local Search Procedure ◽  
Common Subsequence ◽  
Population Based Algorithm

The green vehicle routing problem is a variation of the classic vehicle routing problem in which the transportation fleet is composed of electric vehicles with limited autonomy in need of recharge during their duties. As an NP-hard problem, this problem is very difficult to solve. In this paper, we first propose a memetic algorithm (MA)—a population-based algorithm—to tackle this problem. To be more specific, we incorporate an adaptive local search procedure based on a reward and punishment mechanism inspired by reinforcement learning to effectively manage the multiple neighborhood moves and guide the search, an effective backbone-based crossover operator to generate the feasible child solutions to obtain a better trade-off between intensification and diversification of the search, and a longest common subsequence-based population updating strategy to effectively manage the population. The purpose of this research is to propose a highly effective heuristic for solving the green vehicle routing problem and bring new ideas for this type of problem. Experimental results show that our algorithm is highly effective in comparison with the current state-of-the-art algorithms. In particular, our algorithm is able to find the best solutions for 84 out of the 92 instances. Key component of the approach is analyzed to evaluate its impact on the proposed algorithm and to identify the appropriate search mechanism for this type of problem.

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