On the road to better routes: Five decades of published research on the vehicle routing problem

Networks ◽  
2020 ◽  
Vol 77 (1) ◽  
pp. 66-87 ◽  
Author(s):  
Xingyin Wang ◽  
Edward Wasil
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Routing Problem ◽  
The Road ◽  
On The Road ◽  
Published Research

scholarly journals Multi-Depot Joint Distribution Vehicle Routing Problem Considering Energy Consumption with Time-Dependent Networks

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2082
Author(s):  
Dengkai Hou ◽  
Houming Fan ◽  
Xiaoxue Ren
Keyword(s):  
Energy Consumption ◽  
Vehicle Routing ◽  
Fuel Consumption ◽  
Vehicle Routing Problem ◽  
Joint Distribution ◽  
Road Network ◽  
Time Dependent ◽  
Neighborhood Search ◽  
Routing Problem ◽  
The Road

This paper studies the multi-depot joint distribution vehicle routing problem considering energy consumption with time-dependent networks (MDJDVRP-TDN). Aiming at the multi-depot joint distribution vehicle routing problem where the vehicle travel time depends on the variation characteristics of the road network speed in the distribution area, considering the influence of the road network on the vehicle speed and the relationship between vehicle load and fuel consumption, a multi-depot joint distribution vehicle routing optimization model is established to minimize the sum of vehicle fixed cost, fuel consumption cost and time window penalty cost. Traditional vehicle routing problems are modeled based on symmetric graphs. In this paper, considering the influence of time-dependent networks on routes optimization, modeling is based on asymmetric graphs, which increases the complexity of the problem. A hybrid genetic algorithm with variable neighborhood search (HGAVNS) is designed to solve the model, in which the nearest neighbor insertion method and Logistic mapping equation are used to generate the initial solution firstly, and then five neighborhood structures are designed to improve the algorithm. An adaptive neighborhood search times strategy is used to balance the diversification and depth search of the population. The effectiveness of the designed algorithm is verified through several groups of numerical instances with different scales. The research can enrich the relevant theoretical research of multi-depot vehicle routing problems and provide the theoretical basis for transportation enterprises to formulate reasonable distribution schemes.

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