Abstract
A hybrid approach for bus driver scheduling is described, which incorporates Genetic Algorithms (GAs), a rule-based driver duties Estimator, and an integer linear programming method called IMPACS. IMPACS produces near-optimal schedules. However, a scheduling run might take hours, and larger problems may have to be decomposed into sub-problems. Earlier research indicates that GAs for directly producing driver schedules are quick, but so far they tend to converge at suboptimal solutions. In the hybrid approach, GAs take on an indirect role. They do not form schedules, but instead would yield a population of vastly condensed versions of the problem. IMP ACS is then applied using the elite population members to yield the optimal schedule almost trivially. The Estimator plays an essential role in the evaluation of population members during the evolution process. The resulting scheduling system captures the complementary strengths of its components and has potential for solving large problems optimally and quickly.
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© 1996 Springer-Verlag Berlin. Heidelberg
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Kwan, R.S.K., Wren, A. (1996). Hybrid Genetic Algorithms for Bus Driver Scheduling. In: Bianco, L., Toth, P. (eds) Advanced Methods in Transportation Analysis. Transportation Analysis. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-85256-5_27
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DOI: https://doi.org/10.1007/978-3-642-85256-5_27
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