A Novel Hybrid Algorithm for Solving the Clustered Vehicle Routing Problem

2015 ◽  
pp. 679-689 ◽  
Author(s):  
Andrei Horvat Marc ◽  
Levente Fuksz ◽  
Petrică C. Pop ◽  
Daniela Dănciulescu
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Routing Problem

A Hybrid Algorithm for the Multi-Objective Time Dependent Vehicle Routing Problem

2014 ◽  
Vol 505-506 ◽  
pp. 1071-1075
Author(s):  
Yi Sun ◽  
Yue Chen ◽  
Chang Chun Pan ◽  
Gen Ke Yang
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Time Windows ◽  
Time Dependent ◽  
Ant Colony System ◽  
Optimal Solutions ◽  
Test Results ◽  
Routing Problem ◽  
Traffic Conditions

This paper presents a real road network case based on the time dependent vehicle routing problem with time windows (TDVRPTW), which involves optimally routing a fleet of vehicles with fixed capacity when traffic conditions are time dependent and services at customers are only available in their own time tables. A hybrid algorithm based on the Genetic Algorithm (GA) and the Multi Ant Colony System (MACS) is introduced in order to find optimal solutions that minimize two hierarchical objectives: the number of tours and the total travel cost. The test results show that the integrated algorithm outperforms both of its traditional ones in terms of the convergence speed towards optimal solutions.

A hybrid algorithm for vehicle routing problem with time windows

2011 ◽  
Vol 38 (1) ◽  
pp. 435-441 ◽  
Author(s):  
B. Yu ◽  
Z.Z. Yang ◽  
B.Z. Yao
Keyword(s):  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Time Windows ◽  
Routing Problem

A Hybrid Ant Algorithm for the Vehicle Routing Problem

2012 ◽  
Vol 182-183 ◽  
pp. 2118-2122
Author(s):  
Yu Li ◽  
Liang Ma
Keyword(s):  
Quantum Computing ◽  
Vehicle Routing ◽  
Vehicle Routing Problem ◽  
Hybrid Algorithm ◽  
Quantum Walk ◽  
Ant Algorithm ◽  
Quantum Superposition ◽  
Routing Problem ◽  
Numerical Examples ◽  
Search Capability

A hybrid algorithm for solving the vehicle routing problem is proposed based upon the combination of Ant Colony Optimization and quantum computing. The algorithm takes the advantage of the principles in quantum computing, such as the qubit, quantum gate, and the quantum superposition of states. It can search the best solution by quantum walk and can further improve the search capability of the algorithm for the best solution. Numerical examples are tested and verified, that show the good performances.

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