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C. Blum, D. Thiruvady, A. Ernst, M. Horn, G. Raidl:
"A Biased Random Key Genetic Algorithm with Rollout Evaluations for the Resource Constraint Job Scheduling Problem";
Talk: Advances in Artificial Intelligence, North Terrace, Adelaide, South Australia; 2019-12-02 - 2019-12-05; in: "AI 2019: Advances in Artificial Intelligence", LNCS, 11919 (2019), ISBN: 978-3-030-35288-2; 549 - 560.

The resource constraint job scheduling problem considered
in this work is a difficult optimization problem that was defined in
the context of the transportation of minerals from mines to ports. The
main characteristics are that all jobs share a common limiting resource
and that the objective function concerns the minimization of the total
weighted tardiness of all jobs. The algorithms proposed in the literature
for this problem have a common disadvantage: they require a huge
amount of computation time. Therefore, the main goal of this work is
the development of an algorithm that can compete with the state of the
art, while using much less computational resources. In fact, our experimental
results show that the biased random key genetic algorithm that
we propose significantly outperforms the state-of-the-art algorithm from
the literature both in terms of solution quality and computation time.

http://dx.doi.org/10.1007/978-3-030-35288-2_44

https://publik.tuwien.ac.at/files/publik_284987.pdf