A fuzzy simulated evolution algorithm for the driver scheduling problem

The paper presents a fuzzy simulated evolution algorithm for the public transport driver scheduling problem, which involves solving a set covering model. The novel scheduling algorithm incorporates the idea of fuzzy evaluation into simulated evolution, combining the features of iterative improvement and constructive perturbation, to explore solution space effectively and obtain superior schedules. Experiments with benchmark tests using data from the transportation industry demonstrate the strengths of the proposed algorithm in solving large size real-world driver scheduling problems. It is suggested that this approach might be suitable for other large-scale set covering problems.

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