A Hybrid Algorithm for the Vehicle Routing Problem with Soft Time Windows and Hierarchical Objectives

2015 ◽  
Vol 36 (3) ◽  
pp. 283-300 ◽  
Author(s):  
Tharinee Manisri ◽  
Anan Mungwattana ◽  
Gerrit K. Janssens ◽  
An Caris
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Time Windows ◽  
Routing Problem ◽  
Soft Time Windows

Recharge strategies for the electric vehicle routing problem with soft time windows and fast chargers

2021 ◽  
Vol 15 (4/5) ◽  
pp. 444
Author(s):  
Zhenping Li ◽  
Guohua Wu ◽  
Ke Zhang ◽  
Shuxuan Li ◽  
Chenglin Xiao ◽  
...  
Keyword(s):  
Vehicle Routing ◽  
Electric Vehicle ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Routing Problem ◽  
Soft Time Windows

Optimization of Multi-Depot Heterogeneous Incident Vehicle Routing Problem with Soft Time Windows Based on an Improved Ant Colony Optimization

2014 ◽  
Vol 1061-1062 ◽  
pp. 1108-1117
Author(s):  
Ya Lian Tang ◽  
Yan Guang Cai ◽  
Qi Jiang Yang
Keyword(s):  
Ant Colony Optimization ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Time Windows ◽  
Ant Colony ◽  
Genetic Operators ◽  
Solution Quality ◽  
Routing Problem ◽  
Soft Time Windows ◽  
Heterogeneous Vehicle Routing Problem

Aiming at vehicle routing problem (VRP) with many extended features is widely used in actual life, multi-depot heterogeneous vehicle routing problem with soft time windows (MDHIVRPSTW) mathematical model is established. An improved ant colony optimization (IACO) is proposed for solving this model. Firstly, MDHIVRPSTW was transferred into different groups according to nearest depot method, then constructing the initial route by scanning algorithm (SA). Secondly, genetic operators were introduced, and then adjusting crossover probability and mutation probability adaptively in order to improve the global search ability of the algorithm. Moreover, smooth mechanism was used to improve the performance of ant colony optimization (ACO). Finally, 3-opt strategy was used to improve the local search ability. The proposed IACO has been tested on a 32-customer instance which was generated randomly. The experimental results show that IACO is superior to other three algorithms in terms of convergence speed and solution quality, thus the proposed method is effective and feasible, and the proposed model is better than conventional model.

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