Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem

Abstract

The mixed fleet heterogeneous dial-a-ride problem (MF-HDARP) consists of designing vehicle routes for a set of users by using a mixed fleet including both heterogeneous conventional and alternative fuel vehicles. In addition, a vehicle is allowed to refuel from a fuel station to eliminate the risk of running out of fuel during its service. We propose an efficient hybrid adaptive large neighborhood search (hybrid ALNS) algorithm for the MF-HDARP. The computational experiments show that the algorithm produces high quality solutions on our generated instances and on HDARP benchmarks instances. Computational experiments also highlight that the newest components added to the standard ALNS algorithm enhance intensification and diversification during the search process.

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Acknowledgements

Thanks are due to two anonymous reviewers for their useful comments and for raising interesting points for discussion.

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Correspondence to Mohamed Amine Masmoudi.

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Appendix

Appendix

All parameters used in the CMEM model are given in Table 12 below.

Table 12 Parameters used in the CMEM of the MF-HDARP model

See Table 13.

Table 13 Parameters used in our proposed algorithm

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Masmoudi, M.A., Hosny, M., Demir, E. et al. Hybrid adaptive large neighborhood search algorithm for the mixed fleet heterogeneous dial-a-ride problem. J Heuristics 26, 83–118 (2020). https://doi.org/10.1007/s10732-019-09424-x

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Keywords

  • Dial-a-ride problem
  • Alternative fuel station
  • Adaptive large neighborhood search algorithm
  • Mixed vehicle fleet