scholarly journals Self-imposed time windows in vehicle routing problems

OR Spectrum ◽  
2013 ◽  
Vol 37 (2) ◽  
pp. 331-352 ◽  
Author(s):  
Ola Jabali ◽  
Roel Leus ◽  
Tom Van Woensel ◽  
Ton de Kok
Keyword(s):  
Vehicle Routing ◽  
Time Windows ◽  
Vehicle Routing Problems ◽  
Routing Problems

scholarly journals A solution approach for multi‐trip vehicle routing problems with time windows, fleet sizing, and depot location

Networks ◽  
2021 ◽  
Author(s):  
Paula Fermín Cueto ◽  
Ivona Gjeroska ◽  
Albert Solà Vilalta ◽  
Miguel F. Anjos
Keyword(s):  
Vehicle Routing ◽  
Time Windows ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Solution Approach ◽  
Fleet Sizing

Selecting meta-heuristics for solving vehicle routing problems with time windows via meta-learning

2019 ◽  
Vol 118 ◽  
pp. 470-481 ◽  
Author(s):  
Andres E. Gutierrez-Rodríguez ◽  
Santiago E. Conant-Pablos ◽  
José C. Ortiz-Bayliss ◽  
Hugo Terashima-Marín
Keyword(s):  
Vehicle Routing ◽  
Time Windows ◽  
Vehicle Routing Problems ◽  
Routing Problems ◽  
Meta Learning

An Intelligent Water Drop Algorithm for Solving Multi-Objective Vehicle Routing Problems With Mixed Time Windows

2019 ◽  
Vol 10 (1) ◽  
pp. 82-104 ◽  
Author(s):  
Tao Wang ◽  
Jing Ni ◽  
Yixuan Wang
Keyword(s):  
Vehicle Routing ◽  
Hybrid Algorithm ◽  
Water Drop ◽  
Time Windows ◽  
Continuous Optimization ◽  
Vehicle Routing Problems ◽  
Basic Algorithm ◽  
Routing Problems ◽  
Multi Objective ◽  
Minimum Number

This article proposes an Intelligent Water Drop Algorithm for solving Multi-Objective Vehicle Routing Problems by considering the constraints of vehicle volume, delivery mileage, and mixed time windows and minimizing the cost of distribution and the minimum number of vehicles. This article improves the basic Intelligent Water Drop Algorithm and show the improved intelligent water droplet genetic hybrid algorithm is an effective method for solving discrete problems. The authors present a practical example and show the applicability of the proposed algorithm. The authors compare the algorithms with the basic algorithm and show the improved intelligent droplet genetic hybrid algorithm has higher computing efficiency and continuous optimization capability.

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