A Biased Random Key Genetic Algorithm with Rollout Evaluations for the Resource Constraint Job Scheduling Problem
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.
KeywordsJob scheduling Genetic algorithm Rollout evaluation
This work was partially funded by the Doctoral Program “Vienna Graduate School on Computational Optimization”, Austrian Science Foundation (FWF) Project No. W1260-N35. Moreover, this work was partially supported by the EU H2020 Research and Innovation Program under the LOGISTAR project (Grant Agreement No. 769142).
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