GPU-based Parallel Heuristics for Capacited Vehicle Routing Problem

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DC Field	Value	Language
dc.contributor.author	Yelmewad P.
dc.contributor.author	Talawar B.
dc.date.accessioned	2021-05-05T10:15:51Z	-
dc.date.available	2021-05-05T10:15:51Z	-
dc.date.issued	2020
dc.identifier.citation	Proceedings of CONECCT 2020 - 6th IEEE International Conference on Electronics, Computing and Communication Technologies , Vol. , , p. -	en_US
dc.identifier.uri	https://doi.org/10.1109/CONECCT50063.2020.9198667
dc.identifier.uri	http://idr.nitk.ac.in/jspui/handle/123456789/14835	-
dc.description.abstract	This paper presents the novel GPU-based parallel strategy for the heuristic algorithms to solve the large-scale Capacited Vehicle Routing Problem (CVRP). A combination of five improvement heuristic approaches has been used to improve the constructed feasible solution. It is noticed that a large amount of CPU time is spent in the solution improvement phase while improving a feasible solution. We aim to discover an independent part of the improvement heuristic approaches and make it run over the GPU platform simultaneously. The proposed parallel version has been tested on large-scale instances of up to 20000 customers. The parallel version offers speedup up to 176.12 × compared to the corresponding sequential version. © 2020 IEEE.	en_US
dc.title	GPU-based Parallel Heuristics for Capacited Vehicle Routing Problem	en_US
dc.type	Conference Paper	en_US
Appears in Collections:	2. Conference Papers

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