This HTML5 document contains 38 embedded RDF statements represented using HTML+Microdata notation.

The embedded RDF content will be recognized by any processor of HTML5 Microdata.

Namespace Prefixes

PrefixIRI
dctermshttp://purl.org/dc/terms/
n10http://localhost/temp/predkladatel/
n11http://linked.opendata.cz/resource/domain/vavai/riv/tvurce/
n15http://linked.opendata.cz/resource/domain/vavai/subjekt/
n3http://linked.opendata.cz/ontology/domain/vavai/
n18http://linked.opendata.cz/resource/domain/vavai/vysledek/RIV%2F00216275%3A25510%2F11%3A39894519%21RIV12-MSM-25510___/
n17http://linked.opendata.cz/resource/domain/vavai/zamer/
shttp://schema.org/
n6http://linked.opendata.cz/ontology/domain/vavai/riv/
skoshttp://www.w3.org/2004/02/skos/core#
n2http://linked.opendata.cz/resource/domain/vavai/vysledek/
rdfhttp://www.w3.org/1999/02/22-rdf-syntax-ns#
n7http://linked.opendata.cz/ontology/domain/vavai/riv/klicoveSlovo/
n14http://linked.opendata.cz/ontology/domain/vavai/riv/duvernostUdaju/
xsdhhttp://www.w3.org/2001/XMLSchema#
n19http://linked.opendata.cz/ontology/domain/vavai/riv/aktivita/
n12http://linked.opendata.cz/ontology/domain/vavai/riv/jazykVysledku/
n13http://linked.opendata.cz/ontology/domain/vavai/riv/obor/
n9http://linked.opendata.cz/ontology/domain/vavai/riv/druhVysledku/
n16http://reference.data.gov.uk/id/gregorian-year/

Statements

Subject Item
n2:RIV%2F00216275%3A25510%2F11%3A39894519%21RIV12-MSM-25510___
rdf:type
n3:Vysledek skos:Concept
dcterms:description
Author in this paper describes the possibilities of solving some vehicle routing variants by genetic algorithm. Specifically, it is a classical capacitated vehicle routing problem (CVRP), vehicle routing problem with time windows (VRP-TW), vehicle routing problem with simultaneous deliveries and pick-ups (VRPDP) and their mutual combinations. Genetic algorithms are a search method used to find suboptimal solutions of complicated combinatorial problems including vehicle routing. Genetic algorithm (GVR) is quite universal due to the two-level representation of the problem - without major modifications it enables successful solving of CVRP, VRP-TW and possibly also other variants of the problem. GVR enables also fast search for new solutions - operators of crossover and mutations provide solutions whose adjustment is not time demanding, and quality of provided solutions is very good, GVR verified with standard data. Parameters of genetic algorithm can be modified in the program environment, the output routes can be presented as texts or/and in map. Each solution is described as the sequence of all customers, while every customer data is stored in binary form. The results calculated by genetic algorithm were verified with standard data. The program can also load selected file formats with standard data in addition to its own text file format. Author in this paper describes the possibilities of solving some vehicle routing variants by genetic algorithm. Specifically, it is a classical capacitated vehicle routing problem (CVRP), vehicle routing problem with time windows (VRP-TW), vehicle routing problem with simultaneous deliveries and pick-ups (VRPDP) and their mutual combinations. Genetic algorithms are a search method used to find suboptimal solutions of complicated combinatorial problems including vehicle routing. Genetic algorithm (GVR) is quite universal due to the two-level representation of the problem - without major modifications it enables successful solving of CVRP, VRP-TW and possibly also other variants of the problem. GVR enables also fast search for new solutions - operators of crossover and mutations provide solutions whose adjustment is not time demanding, and quality of provided solutions is very good, GVR verified with standard data. Parameters of genetic algorithm can be modified in the program environment, the output routes can be presented as texts or/and in map. Each solution is described as the sequence of all customers, while every customer data is stored in binary form. The results calculated by genetic algorithm were verified with standard data. The program can also load selected file formats with standard data in addition to its own text file format.
dcterms:title
Genetic Algorithms for Solving Vehicle Routing Genetic Algorithms for Solving Vehicle Routing
skos:prefLabel
Genetic Algorithms for Solving Vehicle Routing Genetic Algorithms for Solving Vehicle Routing
skos:notation
RIV/00216275:25510/11:39894519!RIV12-MSM-25510___
n3:predkladatel
n15:orjk%3A25510
n6:aktivita
n19:Z
n6:aktivity
Z(MSM0021627505)
n6:cisloPeriodika
16
n6:dodaniDat
n16:2012
n6:domaciTvurceVysledku
n11:4487559
n6:druhVysledku
n9:J
n6:duvernostUdaju
n14:S
n6:entitaPredkladatele
n18:predkladatel
n6:idSjednocenehoVysledku
200981
n6:idVysledku
RIV/00216275:25510/11:39894519
n6:jazykVysledku
n12:eng
n6:klicovaSlova
Genetic Algorithms,Solving,Vehicle Routing
n6:klicoveSlovo
n7:Vehicle%20Routing n7:Genetic%20Algorithms n7:Solving
n6:kodStatuVydavatele
CZ - Česká republika
n6:kontrolniKodProRIV
[962EBDD95017]
n6:nazevZdroje
Scientific Papers of the University of Pardubice, Series B, The Jan Perner Transport Faculty
n6:obor
n13:JO
n6:pocetDomacichTvurcuVysledku
1
n6:pocetTvurcuVysledku
1
n6:rokUplatneniVysledku
n16:2011
n6:svazekPeriodika
2010
n6:tvurceVysledku
Široký, Jaromír
n6:zamer
n17:MSM0021627505
s:issn
1211-6610
s:numberOfPages
10
n10:organizacniJednotka
25510