Abstract:
Hub location problems have been studied by researchers for three decades, yet, most algorithms do not perform well for large-scale networks because of their high computat...Show MoreMetadata
Abstract:
Hub location problems have been studied by researchers for three decades, yet, most algorithms do not perform well for large-scale networks because of their high computational complexities. Methods that scale up to large networks are usually tailored specifically towards a particular hub location problem instance and cannot be adapted easily to other problems without significant efforts. In this paper, we propose a General Contraction Method (GCM), which explores and exploits the idea of efficiently computing hub locations on a reduced network instance, so-called contracted network, and then rewriting the obtained solution back to the original network. If the contracted network preserves major flows and spatial properties, it can be used as a boilerplate for finding good solutions to the original network. In order to evaluate the performance of the contraction methods, three commonly-used datasets (CAB, TR and AP) are used as case studies. We find that GCM provides high-quality initial solutions within a few seconds even for very large-scale problems. GCM can be combined with specifically tailored solution techniques/heuristics and better solutions can be provided within much shorter time further. Moreover, we show that by varying the size of the contracted network, we can nicely explore a fine trade-off between highly efficient computation and close-to-optimal solutions. We believe that GCM can be adapted to many different types of hub location problems, and thus, our work contributes towards the development of scalable transportation network design.
Date of Conference: 27 November 2017 - 01 December 2017
Date Added to IEEE Xplore: 08 February 2018
ISBN Information: