Abstract
We present a large neighborhood search (LNS) as optimization core for a cooperative optimization approach (COA) to optimize locations of service points for mobility applications. COA is an iterative interactive algorithm in which potential customers can express preferences during the optimization. A machine learning component processes the feedback obtained from the customers. The learned information is then used in an optimization component to generate an optimized solution. The LNS replaces a mixed integer linear program (MILP) that has been used as optimization core so far. A particular challenge for developing the LNS is that a fast way for evaluating the non-trivial objective function for candidate solutions is needed. To this end, we propose an evaluation graph, making an efficient incremental calculation of the objective value of a modified solution possible. We evaluate the LNS on artificial instances as well as instances derived from real-world data and compare its performance to the previously developed MILP. Results show that the LNS as optimization core scales significantly better to larger instances while still being able to obtain solutions close to optimality.
Thomas Jatschka acknowledges the financial support from Honda Research Institute Europe.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
Jatschka, T., Rodemann, T., Raidl, G.R.: A cooperative optimization approach for distributing service points in mobility applications. In: Liefooghe, A., Paquete, L. (eds.) EvoCOP 2019. LNCS, vol. 11452, pp. 1–16. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-16711-0_1
Jatschka, T., Raidl, G., Rodemann, T.: A general cooperative optimization approach for distributing service points in mobility applications. Technical report AC-TR-21-006, TU Wien, Vienna, Austria (2021, submitted)
Jatschka, T., Rodemann, T., Raidl, G.R.: VNS and PBIG as optimization cores in a cooperative optimization approach for distributing service points. In: Moreno-Díaz, R., Pichler, F., Quesada-Arencibia, A. (eds.) EUROCAST 2019. LNCS, vol. 12013, pp. 255–262. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-45093-9_31
Meignan, D., Knust, S., Frayret, J.M., Pesant, G., Gaud, N.: A review and taxonomy of interactive optimization methods in operations research. ACM Trans. Interact. Intell. Syst. 5, 17:1–17:43 (2015)
Sun, X., Gong, D., Jin, Y., Chen, S.: A new surrogate-assisted interactive genetic algorithm with weighted semisupervised learning. IEEE Trans. Cybern. 43, 685–698 (2013)
Sun, X.Y., Gong, D., Li, S.: Classification and regression-based surrogate model-assisted interactive genetic algorithm with individual’s fuzzy fitness. In: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pp. 907–914. ACM (2009)
Llorà, X., Sastry, K., Goldberg, D.E., Gupta, A., Lakshmi, L.: Combating user fatigue in iGAs: partial ordering, support vector machines, and synthetic fitness. In: Proceedings of the 7th Annual Conference on Genetic and Evolutionary Computation, pp. 1363–1370. ACM (2005)
Kim, H.S., Cho, S.B.: Application of interactive genetic algorithm to fashion design. Eng. Appl. Artif. Intell. 13, 635–644 (2000)
Dou, R., Zong, C., Nan, G.: Multi-stage interactive genetic algorithm for collaborative product customization. Knowl.-Based Syst. 92, 43–54 (2016)
Jatschka, T., Rodemann, T., Raidl, G.R.: Exploiting similar behavior of users in a cooperative optimization approach for distributing service points in mobility applications. In: Nicosia, G., Pardalos, P., Umeton, R., Giuffrida, G., Sciacca, V. (eds.) LOD 2019. LNCS, vol. 11943, pp. 738–750. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-37599-7_61
Bell, R.M., Koren, Y., Volinsky, C.: Matrix factorization techniques for recommender systems. Computer 42, 30–37 (2009)
Ekstrand, M.D., Riedl, J.T., Konstan, J.A.: Collaborative filtering recommender systems. Found. Trends Hum.-Comput. Interact. 4, 81–173 (2011)
Frade, I., Ribeiro, A., Gonçalves, G., Antunes, A.: Optimal location of charging stations for electric vehicles in a neighborhood in Lisbon, Portugal. Transp. Res. Rec.: J. Transp. Res. Board 2252, 91–98 (2011)
Kloimüllner, C., Raidl, G.R.: Hierarchical clustering and multilevel refinement for the bike-sharing station planning problem. In: Battiti, R., Kvasov, D.E., Sergeyev, Y.D. (eds.) LION 2017. LNCS, vol. 10556, pp. 150–165. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-69404-7_11
Gendreau, M., Potvin, J.Y., et al.: Handbook of Metaheuristics, vol. 3. Springer, Cham (2019). https://doi.org/10.1007/978-3-319-91086-4
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Jatschka, T., Rodemann, T., Raidl, G.R. (2022). A Large Neighborhood Search for a Cooperative Optimization Approach to Distribute Service Points in Mobility Applications. In: Dorronsoro, B., Yalaoui, F., Talbi, EG., Danoy, G. (eds) Metaheuristics and Nature Inspired Computing. META 2021. Communications in Computer and Information Science, vol 1541. Springer, Cham. https://doi.org/10.1007/978-3-030-94216-8_1
Download citation
DOI: https://doi.org/10.1007/978-3-030-94216-8_1
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-94215-1
Online ISBN: 978-3-030-94216-8
eBook Packages: Computer ScienceComputer Science (R0)