A Variable Neighborhood Search Integrated in the POPMUSIC Framework for Solving Large Scale Vehicle Routing Problems

Authors:
Alexander Ostertag;Karl F. Doerner;Richard F. Hartl
Affiliations:
Department of Business Administration, University of Vienna, Vienna, Austria 1210;Department of Business Administration, University of Vienna, Vienna, Austria 1210;Department of Business Administration, University of Vienna, Vienna, Austria 1210
Venue:
HM '08 Proceedings of the 5th International Workshop on Hybrid Metaheuristics
Year:
2008

Citing 6
Cited 3

D-Ants: savings based ants divide and conquer the vehicle routing problem

Computers and Operations Research
A Variable Neighborhood Search for the Multi Depot Vehicle Routing Problem with Time Windows

Journal of Heuristics
An efficient variable neighborhood search heuristic for very large scale vehicle routing problems

Computers and Operations Research
Active-guided evolution strategies for large-scale capacitated vehicle routing problems

Computers and Operations Research
Active guided evolution strategies for large-scale vehicle routing problems with time windows

Computers and Operations Research
A variable neighborhood search for the capacitated arc routing problem with intermediate facilities

Journal of Heuristics

A Hybrid Algorithm for Computing Tours in a Spare Parts Warehouse

EvoCOP '09 Proceedings of the 9th European Conference on Evolutionary Computation in Combinatorial Optimization
Spatial, temporal, and hybrid decompositions for large-scale vehicle routing with time windows

CP'10 Proceedings of the 16th international conference on Principles and practice of constraint programming
Survey: matheuristics for rich vehicle routing problems

HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper presents a heuristic approach based on the POPMUSIC framework for solving large scale Multi Depot Vehicle Routing Problems with Time Windows derived from real world data. A Variable Neighborhood Search is used as the optimizer in the POPMUSIC framework. POPMUSIC is a new decomposition approach for large scale problems. We compare our method with a pure VNS approach and a Memetic Algorithm integrated in a POPMUSIC framework. The computational results show that the integration of VNS in the POPMUSIC framework outperforms the other existing methods. Furthermore different distance metrics for the decomposition strategies are presented and the results are reported and analyzed.