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Deep learning-based genetic algorithm for the robust hub allocation problem with discrete scenarios

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  • Achraf Berrajaa

Abstract

Over the last decade, big data have changed the work and research strategies of several areas, in particular the hub location problems (HLP). The HLP have been extended to handle uncertain data, giving rise to robust HLPs. In a RHLP with discrete scenarios, the unique set of requests is replaced by a set of discrete scenarios. In a robust optimisation approach, making appropriate decisions for all scenarios must be intelligent and optimal. The purpose of this study is to show that such problems can be solved in a reasonable computing time and with an intelligent solution, using a RNN based on GA to approximately solve the problem. The proposed RNN has been trained on a big dataset of 20,000 instances. The performances of the proposed RNN are very interesting such that the success rate is 91% and able to resolve large instances while the traditional approaches are fail to resolve them.

Suggested Citation

  • Achraf Berrajaa, 2024. "Deep learning-based genetic algorithm for the robust hub allocation problem with discrete scenarios," International Journal of Logistics Systems and Management, Inderscience Enterprises Ltd, vol. 48(4), pages 489-507.
    • Handle: RePEc:ids:ijlsma:v:48:y:2024:i:4:p:489-507
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