Effective and interpretable dispatching rules for dynamic job shops via guided empirical learning
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- @Article{FERREIRA:2022:omega,
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author = "Cristiane Ferreira and Goncalo Figueira and
Pedro Amorim",
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title = "Effective and interpretable dispatching rules for
dynamic job shops via guided empirical learning",
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journal = "Omega",
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volume = "111",
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pages = "102643",
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year = "2022",
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ISSN = "0305-0483",
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DOI = "doi:10.1016/j.omega.2022.102643",
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URL = "https://www.sciencedirect.com/science/article/pii/S0305048322000512",
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keywords = "genetic algorithms, genetic programming, Scheduling,
Dynamic Job Shop, Dispatching Rules",
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abstract = "The emergence of Industry 4.0 is making production
systems more flexible and also more dynamic. In these
settings, schedules often need to be adapted in
real-time by dispatching rules. Although substantial
progress was made until the '90s, the performance of
these rules is still rather limited. The machine
learning literature is developing a variety of methods
to improve them. However, the resulting rules are
difficult to interpret and do not generalise well for a
wide range of settings. This paper is the first major
attempt at combining machine learning with domain
problem reasoning for scheduling. The idea consists of
using the insights obtained with the latter to guide
the empirical search of the former. We hypothesise that
this guided empirical learning process should result in
effective and interpretable dispatching rules that
generalise well to different scenarios. We test our
approach in the classical dynamic job shop scheduling
problem minimising tardiness, one of the most
well-studied scheduling problems. The simulation
experiments include a wide spectrum of scenarios for
the first time, from highly loose to tight due dates
and from low use conditions to severely congested
shops. Nonetheless, results show that our approach can
find new state-of-the-art rules, which significantly
outperform the existing literature in the vast majority
of settings. Overall, the average improvement over the
best combination of benchmark rules is 19percent.
Moreover, the rules are compact, interpretable, and
generalise well to extreme, unseen scenarios.
Therefore, we believe that this methodology could be a
new paradigm for applying machine learning to dynamic
optimisation problems",
- }
Genetic Programming entries for
Cristiane Maria Santos Ferreira
Luis Goncalo Rodrigues Reis Figueira
Pedro Sanches Amorim
Citations