Capacitated hierarchical clustering heuristic for multi depot location-routing problems
In this paper, the authors develop a hierarchical clustering-based heuristic for the multi-depot location-routing problem (MDLRP). They contribute to the literature in two ways. First, they develop two stopping rules for capacitated hierarchical clustering. Hierarchical clustering does not require a priori assumptions about the number of clusters. The authors test the performance of stopping rules that determine the number of clusters under various assumptions. Second, they compare the performance of their hierarchical clustering-based heuristic approach for the MDLRP with heuristics and meta-heuristics previously proposed in the literature. Their heuristic often finds lower costs on average than the heuristics proposed in the literature and generates solutions within 4%, on average, of various meta-heuristic approaches proposed in the literature. As a result of this level of performance, the authors suggest that this heuristic can be used as a simple solution or a warm start solution for any improvement procedure.
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Availability:
- Find a library where document is available. Order URL: http://worldcat.org/issn/13675567
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Supplemental Notes:
- Abstract reprinted with permission from Taylor & Francis
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Authors:
- Lam, Marco
- Mittenthal, John
- Publication Date: 2013-10
Language
- English
Media Info
- Media Type: Digital/other
- Features: References; Tables;
- Pagination: pp 433-444
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Serial:
- International Journal of Logistics Research and Applications
- Volume: 16
- Issue Number: 5
- Publisher: Taylor & Francis
- ISSN: 1367-5567
- EISSN: 1469-848X
- Serial URL: http://www.tandfonline.com/toc/cjol20/current
Subject/Index Terms
- TRT Terms: Costs; Heuristic methods; Intermodal terminals; Literature reviews; Logistics; Routing; Stopping
- Subject Areas: Freight Transportation; Planning and Forecasting; I70: Traffic and Transport;
Filing Info
- Accession Number: 01497448
- Record Type: Publication
- Files: TRIS
- Created Date: Oct 22 2013 11:48AM