Evolving heuristics for Dynamic Vehicle Routing with Time Windows using genetic programming
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- @InProceedings{jacobsen-grocott:2017:CEC,
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author = "Josiah Jacobsen-Grocott and Yi Mei and Gang Chen2 and
Mengjie Zhang",
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booktitle = "2017 IEEE Congress on Evolutionary Computation (CEC)",
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title = "Evolving heuristics for Dynamic Vehicle Routing with
Time Windows using genetic programming",
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year = "2017",
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editor = "Jose A. Lozano",
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pages = "1948--1955",
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address = "Donostia, San Sebastian, Spain",
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month = "5-8 " # jun,
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publisher = "IEEE",
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keywords = "genetic algorithms, genetic programming, combinatorial
mathematics, mathematical programming, scheduling,
vehicle routing, combinatorial optimisation problem,
dynamic vehicle routing problem, evolutionary
algorithms, evolving heuristics, genetic
programming-based hyper-heuristic, manually designed
heuristics, meta-algorithm, optimisation methods,
scheduling horizon, static problems, time windows,
Optimization, Real-time systems, Time factors, Vehicle
dynamics",
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isbn13 = "978-1-5090-4601-0",
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URL = "https://homepages.ecs.vuw.ac.nz/~yimei/papers/CEC17-Josiah.pdf",
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DOI = "doi:10.1109/CEC.2017.7969539",
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abstract = "Dynamic vehicle routing problem with time windows is
an important combinatorial optimisation problem in many
real-world applications. The most challenging part of
the problem is to make real-time decisions (i.e.
whether to accept the newly arrived service requests or
not) during the execution of the routes. It is hardly
applicable to use the optimisation methods such as
mathematical programming and evolutionary algorithms
that are competitive for static problems, since they
are usually time-consuming, and cannot give real-time
responses. In this paper, we consider solving this
problem using heuristics. A heuristic gradually builds
a solution by adding the requests to the end of the
route one by one. This way, it can take advantage of
the latest information when making the next decision,
and give immediate response. In this paper, we propose
a meta-algorithm to generate a solution given any
heuristic. The meta-algorithm maintains a set of routes
throughout the scheduling horizon. Whenever a new
request arrives, it tries to re-generate new routes to
include the new request by the heuristic. It accepts
the new request if successful, and reject otherwise.
Then we manually designed several heuristics, and
proposed a genetic programming-based hyper-heuristic to
automatically evolve heuristics. The results showed
that the heuristics evolved by genetic programming
significantly outperformed the manually designed
heuristics.",
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notes = "IEEE Catalog Number: CFP17ICE-ART Also known as
\cite{7969539}",
- }
Genetic Programming entries for
Josiah Jacobsen-Grocott
Yi Mei
Aaron Chen
Mengjie Zhang
Citations