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Optimizing Station Selection and Routing Efficiency Using the Pickup and Delivery Problem Method with A-Star and Genetic Algorithm

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Innovations in Smart Cities Applications Volume 7 (SCA 2023)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 906))

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Abstract

As the popularity of hydrogen vehicles grows, so does the demand for an efficient and reliable refueling station infrastructure. Hydrogen vehicle (HV) owners face the challenge of finding the best refueling site to meet their specific requirements in terms of comfort, accessibility, and price. Finding the best refueling station for HVs is becoming increasingly important as hydrogen stations expand. This requires analysis of variables such as hydrogen availability, cost, and distance. In this piper, we have two global objectives. In the first one, we drew inspiration from the Pickup and Delivery Problem (PDP) to find the most optimal Hydrogen Refueling Station (HRS) for our HV using a Genetic Algorithm (GA). Secondly, we want to trace the shortest path between the found station and the current location of the vehicle, for which we use the A-Star Algorithm. The approaches proposed in this document have been tested on real data and lead to the conclusion that customers would be exponentially better served by saving time and energy through optimal selection of hydrogen station services rather than the traditional method. The study results indicate the validity of these methods.

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References

  1. Weinert, J., Shaojun, L., Ogden, J., Jianxin, M.: Hydrogen refueling station costs in Shanghai. Int. J. Hydrogen Energy 32(16), 4089–4100 (2007). https://doi.org/10.1016/j.ijhydene.2007.05.010

    Article  Google Scholar 

  2. Abohamzeh, E., Salehi, F., Sheikholeslami, M., Abbassi, R., Khan, F.: Review of hydrogen safety during storage, transmission, and applications processes. J. Loss Prev. Process Ind. 72, 104569 (2021). https://doi.org/10.1016/j.jlp.2021.104569

    Article  Google Scholar 

  3. Brown, T., Kisting, H.: Analysis of customer queuing at hydrogen stations. Int. J. Hydrogen Energy 47(39), 17107–17120 (2022). https://doi.org/10.1016/j.ijhydene.2022.03.211

    Article  Google Scholar 

  4. Brown, T., Shane Stephens-Romero, G., Samuelsen, S.: Quantitative analysis of a successful public hydrogen station. Int. J. Hydrogen Energy 37(17), 12731–12740 (2012). https://doi.org/10.1016/j.ijhydene.2012.06.008

    Article  Google Scholar 

  5. Wróbel, K., Wróbel, J., Tokarz, W., Lach, J., Podsadni, K., Czerwiński, A.: Hydrogen internal combustion engine vehicles: a review. Energies 15(23), 8937 (2022). https://doi.org/10.3390/en15238937

    Article  Google Scholar 

  6. Al Chami, Z., Manier, H., Manier, M.-A., Fitouri, C.: A hybrid genetic algorithm to solve a multi-objective pickup and delivery problem. IFAC-PapersOnLine 50(1), 14656–14661 (2017). https://doi.org/10.1016/j.ifacol.2017.08.1906

    Article  Google Scholar 

  7. Al Chami, Z., El Flity, H., Manier, H., Manier, M.-A.: A new metaheuristic to solve a selective pickup and delivery problem. In: 2018 4th International Conference on Logistics Operations Management (GOL), pp. 1–5. IEEE, Le Havre (2018). https://doi.org/10.1109/GOL.2018.8378089

  8. Peng, Z., Al Chami, Z., Manier, H., Manier, M.-A.: A particle swarm optimization for selective pickup and delivery problem. In: 2018 IEEE 30th International Conference on Tools with Artificial Intelligence (ICTAI), pp. 947–952. IEEE, Volos (2018). https://doi.org/10.1109/ICTAI.2018.00146

  9. Li, L., Feng, L., Manier, H., Manier, M.-A.: Life cycle optimization for hydrogen supply chain network design. Int. J. Hydrogen Energy 52, 491–520 (2024). https://doi.org/10.1016/j.ijhydene.2022.03.219

    Article  Google Scholar 

  10. Li, L., Chami, Z.A., Manier, H., Manier, M.-A., Xue, J.: Incorporating fuel delivery in network design for hydrogen fueling stations: Formulation and two metaheuristic approaches. Transp. Res. E Logistics Transp. Rev. 152, 102384 (2021). https://doi.org/10.1016/j.tre.2021.102384

    Article  Google Scholar 

  11. Kumar, V., Chhabra, J.K., Kumar, D.: Parameter adaptive harmony search algorithm for unimodal and multimodal optimization problems. J. Comput. Sci. 5(2), 144–155 (2014). https://doi.org/10.1016/j.jocs.2013.12.001

    Article  MathSciNet  Google Scholar 

  12. Michalewicz, Z., Schoenauer, M.: Evolutionary algorithms for constrained parameter optimization problems. Evol. Comput. 4(1), 1–32 (1996). https://doi.org/10.1162/evco.1996.4.1.1

    Article  Google Scholar 

  13. Piszcz, A., Soule, T.: Genetic programming: optimal population sizes for varying complexity problems. In: Proceedings of the 8th Annual Conference on Genetic and Evolutionary Computation, pp. 953–954. ACM, Seattle Washington USA (2006). https://doi.org/10.1145/1143997.1144166

  14. Pelikan, M., Goldberg, D.E., Cantu-Paz, E.: Bayesian OptiamnidzaTtiiomneAtolgoCroitnhvmer,gPenocpeulation Sizing.

    Google Scholar 

  15. Sommer, C.: Shortest-path queries in static networks. ACM Comput. Surv. 46(4), 1–31 (2014). https://doi.org/10.1145/2530531

    Article  Google Scholar 

  16. Zhang, D., Liu, Y., Liu, A., Mao, X., Li, Q.: Efficient path query processing through cloud-based mapping services. IEEE Access 5, 12963–12973 (2017). https://doi.org/10.1109/ACCESS.2017.2725308

    Article  Google Scholar 

  17. Akiba, T., Iwata, Y., Yoshida, Y.: Fast exact shortest-path distance queries on large networks by pruned landmark labeling. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, in SIGMOD 2013, pp. 349–360. Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2463676.2465315

  18. Zhu, A.D., Ma, H., Xiao, X., Luo, S., Tang, Y., Zhou, S.: Shortest path and distance queries on road networks: towards bridging theory and practice. In: Proceedings of the 2013 ACM SIGMOD International Conference on Management of Data, in SIGMOD 2013, pp. 857–868. Association for Computing Machinery, New York, NY, USA (2013). https://doi.org/10.1145/2463676.2465277

  19. Rezvanian, A., Meybodi, M.R.: Sampling social networks using shortest paths. Phys. A Stat. Mech. Appl. 424, 254–268 (2015). https://doi.org/10.1016/j.physa.2015.01.030

    Article  Google Scholar 

  20. Chen, C.-H.: A semi-supervised feature selection method using a non-parametric technique with pairwise instance constraints. J. Inf. Sci. 39(3), 359–371 (2013). https://doi.org/10.1177/0165551512456502

    Article  Google Scholar 

  21. Han, J., Zhenhui Li, L., Tang, A.: Mining moving object, trajectory and traffic data. In: Kitagawa, H., Ishikawa, Y., Li, Q., Watanabe, C. (eds.) Database Systems for Advanced Applications, pp. 485–486. Springer, Berlin, Heidelberg (2010). https://doi.org/10.1007/978-3-642-12098-5_56

    Chapter  Google Scholar 

  22. Youxi, W., Wang, L., Ren, J., Ding, W., Xindong, W.: Mining sequential patterns with periodic wildcard gaps. Appl. Intell. 41(1), 99–116 (2014). https://doi.org/10.1007/s10489-013-0499-4

    Article  Google Scholar 

  23. Khosravi-Farsani, H., Nematbakhsh, M., Lausen, G.: SRank: shortest paths as distance between nodes of a graph with application to RDF clustering. J. Inf. Sci. 39(2), 198–210 (2013). https://doi.org/10.1177/0165551512463994

    Article  Google Scholar 

  24. Kuperstein, I., Grieco, L., Cohen, D.P.A., Thieffry, D., Zinovyev, A., Barillot, E.: The shortest path is not the one you know: application of biological network resources in precision oncology research. Mutagenesis 30(2), 191–204 (2015). https://doi.org/10.1093/mutage/geu078

    Article  Google Scholar 

  25. Sun, Y., Xinpei, Y., Bie, R., Song, H.: Discovering time-dependent shortest path on traffic graph for drivers towards green driving. J. Network Comput. Appl. 83, 204–212 (2017). https://doi.org/10.1016/j.jnca.2015.10.018

    Article  Google Scholar 

  26. Quercia, D., Schifanella, R., Aiello, L.M.: The shortest path to happiness: recommending beautiful, quiet, and happy routes in the city. arXiv (2014). Consulté le: 19 juin 2023. Disponible sur: http://arxiv.org/abs/1407.1031

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Authors and Affiliations

  1. LISAC Laboratory, Department of Computer Sciences, Faculty of Sciences Dhar El Mahraz, Sidi Mohammed Ben Abdellah University, 30003, Fez, Morocco

    Soukayna Abibou, Dounia El Bourakadi & Ali Yahyaouy

  2. LUSAC Laboratory, University of Caen Normandy, Caen, France

    Hamid Gualous & Hussein Obeid

Authors
  1. Soukayna Abibou
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  2. Dounia El Bourakadi
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  3. Ali Yahyaouy
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  4. Hamid Gualous
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  5. Hussein Obeid
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Corresponding author

Correspondence to Soukayna Abibou .

Editor information

Editors and Affiliations

  1. Computer Science and Smart systems Laboratory, Faculty of Science and Techniques of Tangier, Abdelmalek Essaadi University, Tangier, Morocco

    Mohamed Ben Ahmed

  2. Computer Science and Smart systems Laboratory, Faculty of Science and Techniques of Tangier, Abdelmalek Essaadi University, Tangier, Morocco

    Anouar Abdelhakim Boudhir

  3. École Spéciale des Travaux Publics, Paris, France

    Rani El Meouche

  4. Computer Engineering Department, Karabük University, Karabük, Türkiye

    İsmail Rakıp Karaș

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Abibou, S., Bourakadi, D.E., Yahyaouy, A., Gualous, H., Obeid, H. (2024). Optimizing Station Selection and Routing Efficiency Using the Pickup and Delivery Problem Method with A-Star and Genetic Algorithm. In: Ben Ahmed, M., Boudhir, A.A., El Meouche, R., Karaș, İ.R. (eds) Innovations in Smart Cities Applications Volume 7. SCA 2023. Lecture Notes in Networks and Systems, vol 906. Springer, Cham. https://doi.org/10.1007/978-3-031-53824-7_18

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  • DOI: https://doi.org/10.1007/978-3-031-53824-7_18

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-53823-0

  • Online ISBN: 978-3-031-53824-7

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