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Efficient implementation of the genetic algorithm to solve rich vehicle routing problems

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Abstract

The aim of this paper is to further study the rich vehicle routing problem (RVRP), which is a well-known combinatorial optimization problem arising in many transportation and logistics settings. This problem is known to be subject to a number of real life constraints, such as the number and capacity limitation of vehicles, time constraints including ready and due dates for each customer, heterogeneous vehicle fleets and different warehouses for vehicles. A Genetic Algorithm (GA)-based approach is proposed to tackle this highly constrained problem. The proposed approach efficiently resolves the problem despite its high complexity. To the best of our knowledge, no GA have been used for solving multi-depot heterogeneous limited fleet VRP with time windows so far. The new algorithm has been tested on benchmark and real-world instances. In fact, promising computational results have shown its good cost-effectiveness.

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Notes

  1. http://neo.lcc.uma.es/vrp/vrp-instances/.

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Acknowledgements

We would like to thank the anonymous reviewers for their insightful and constructive comments that greatly contributed to improving the paper. Our many thanks go also to the editorial staff for their generous support and assistance during the review process.

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Authors and Affiliations

  1. Institut Supérieur de Gestion de Tunis, Université de Tunis, Cité Bouchoucha, 2000, Tunis, Tunisia

    Bochra Rabbouch

  2. Department of Statistics, Faculty of Sciences, King Abdulaziz University, P.O BOX 80203, Jeddah, 21589, Saudi Arabia

    Foued Saâdaoui

  3. Ecole Supérieure de Commerce de Tunis, Université de Manouba, Campus Universitaire de La Manouba, 2010, Tunis, Tunisia

    Rafaa Mraihi

Authors
  1. Bochra Rabbouch
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  2. Foued Saâdaoui
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  3. Rafaa Mraihi
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Correspondence to Foued Saâdaoui.

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Appendix: Optimal solutions for benchmark instances

Appendix: Optimal solutions for benchmark instances

See Tables 11 and 12.

Table 11 Best solutions found for pr01, pr02 and pr03, respectively
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Table 12 Best solutions found for pr04, pr07, pr11 and pr17, respectively
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Rabbouch, B., Saâdaoui, F. & Mraihi, R. Efficient implementation of the genetic algorithm to solve rich vehicle routing problems. Oper Res Int J 21, 1763–1791 (2021). https://doi.org/10.1007/s12351-019-00521-0

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