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Genetic Algorithms for Solving Vehicle Routing
Year: 2011
Type of publication: článek v odborném periodiku
Name of source: Scientific Papers of the University of Pardubice, Series B, The Jan Perner Transport Faculty
Publisher name: Univerzita Pardubice
Place: Pardubice
Page from-to: 75-84
Titles:
Language Name Abstract Keywords
cze SESTAVA OKRUŽNÍCH JÍZD GENETICKÝM ALGORITMEM Článek je zaměřen na možnosti řešení několika modifikací úlohy okružních jízd genetickým algoritmem. Konkrétně se jedná o klasickou kapacitně omezenou úlohu okružních jízd (CVRP), úlohu s časovými okny (VRP-TW), úlohu se současným svozem a rozvozem (VRPDP) a jejich vzájemné kombinace. Získané výsledky jsou suboptimální; jejich kvalita záleží jednak na typu a velikosti instance dané úlohy a jednak na parametrech genetického algoritmu. K ověření funkčnosti konceptu autor vytvořil software nazvaný GA GED Vehicle Routing, který k tvorbě modelu dopravní sítě využívá standardní geodata. Implementované algoritmy na řešení úloh CVRP, VRP-TW a VRPPD podávají uspokojivé výsledky. V současné době je GA GED VR stále vyvíjen a testován, v budoucnosti budou implementovány další typy úloh a algoritmů genetický algoritmus, sestava, okružní jízdy
eng Genetic Algorithms for Solving Vehicle Routing 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. Genetic Algorithms,Solving,Vehicle Routing

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