Genetic Programming for the Vehicle Routing Problem with Zone-Based Pricing
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{gil-gala:2023:GECCO,
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author = "Francisco Javier Gil-Gala and Sezin Afsar and
Marko Durasevic and Juan Jose Palacios and Murat Afsar",
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title = "Genetic Programming for the Vehicle Routing Problem
with {Zone-Based} Pricing",
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booktitle = "Proceedings of the 2023 Genetic and Evolutionary
Computation Conference",
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year = "2023",
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editor = "Sara Silva and Luis Paquete and Leonardo Vanneschi and
Nuno Lourenco and Ales Zamuda and Ahmed Kheiri and
Arnaud Liefooghe and Bing Xue and Ying Bi and
Nelishia Pillay and Irene Moser and Arthur Guijt and
Jessica Catarino and Pablo Garcia-Sanchez and
Leonardo Trujillo and Carla Silva and Nadarajen Veerapen",
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pages = "1118--1126",
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address = "Lisbon, Portugal",
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series = "GECCO '23",
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month = "15-19 " # jul,
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organisation = "SIGEVO",
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publisher = "Association for Computing Machinery",
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publisher_address = "New York, NY, USA",
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keywords = "genetic algorithms, genetic programming, vehicle
routing problem, zone-based pricing, routing policies,
hyper-heuristics",
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isbn13 = "9798400701191",
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DOI = "doi:10.1145/3583131.3590366",
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size = "9 pages",
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abstract = "The vehicle routing problem (VRP) is one of the most
interesting NP-Hard problems due to the multitude of
applications in the real world. This work tracks a VRP
with zone-based prices inwhich each customer belongs to
a particular zone, and the goal is to maximize the
profit. The particularity of this VRP variant is that
the provider needs to determine the prices for each
zone and routes for all vehicles. However, depending on
the selected zone prices, only a subset of customers
will have to be visited. In this work, we propose a
novel route generation scheme (RGS) that considers both
decisions simultaneously. The RGS is guided by a
priority function (PF), which determines the next
customer to visit. Since designing efficient PFs
manually is a difficult and time-consuming task,
hyper-heuristic methods, specifically genetic
programming (GP), have been used in this study to
generate them automatically. Furthermore, to test the
performance of the generated PFs, a genetic algorithm
is also used to exploit the RGS to construct the
solution. The experimental analysis shows that the
evolved heuristics provide reasonable quality solutions
quickly, in contrast with the current state-of-the-art.
Furthermore, GP produces better results than GA for
some problem instances.",
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notes = "GECCO-2023 A Recombination of the 32nd International
Conference on Genetic Algorithms (ICGA) and the 28th
Annual Genetic Programming Conference (GP)",
- }
Genetic Programming entries for
Francisco Javier Gil Gala
Sezin Afsar
Marko Durasevic
Juan Jose Palacios
Murat Afsar
Citations