A variable neighborhood search approach for the two-echelon location-routing problem

Authors:
Martin Schwengerer;Sandro Pirkwieser;Günther R. Raidl
Affiliations:
Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna, Austria;Destion --- IT Consulting OG, Vienna, Austria;Institute of Computer Graphics and Algorithms, Vienna University of Technology, Vienna, Austria
Venue:
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2012

Citing 3
Cited 1

Variable neighborhood search coupled with ILP-based very large neighborhood searches for the (periodic) location-routing problem

HM'10 Proceedings of the 7th international conference on Hybrid metaheuristics
A multi-start iterated local search with tabu list and path relinking for the two-echelon location-routing problem

Engineering Applications of Artificial Intelligence
A metaheuristic for a two echelon location-routing problem

SEA'10 Proceedings of the 9th international conference on Experimental Algorithms

Variable neighborhood search and GRASP for three-layer hierarchical ring network design

PPSN'12 Proceedings of the 12th international conference on Parallel Problem Solving from Nature - Volume Part I

Quantified Score

Hi-index	0.00

Visualization

Abstract

We consider the two-echelon location-routing problem (2E-LRP), a well-known problem in freight distribution arising when establishing a two-level transport system with limited capacities. The problem is a generalization of the location routing problem (LRP), involving strategic (location), tactical (allocation) and operational (routing) decisions at the same time. We present a variable neighborhood search (VNS) based on a previous successful VNS for the LRP, accordingly adapted as well as extended. The proposed algorithm provides solutions of high quality in short time, making use of seven different basic neighborhood structures parameterized with different perturbation size leading to a total of 21 specific neighborhood structures. For intensification, two consecutive local search methods are applied, optimizing the transport costs efficiently by considering only recently changed solution parts. Experimental results clearly show that our method is at least competitive regarding runtime and solution quality to other leading approaches, also improving upon several best known solutions.