Abstract
The goal of this paper is to investigate a decision support system for vehicle routing, where the routing engine learns from the subjective decisions that human planners have made in the past, rather than optimizing a distance-based objective criterion. This is an alternative to the practice of formulating a custom VRP for every company with its own routing requirements. Instead, we assume the presence of past vehicle routing solutions over similar sets of customers, and learn to make similar choices. The approach is based on the concept of learning a first-order Markov model, which corresponds to a probabilistic transition matrix, rather than a deterministic distance matrix. This nevertheless allows us to use existing arc routing VRP software in creating the actual route plans. For the learning, we explore different schemes to construct the probabilistic transition matrix. Our results on a use-case with a small transportation company show that our method is able to generate results that are close to the manually created solutions, without needing to characterize all constraints and sub-objectives explicitly. Even in the case of changes in the client sets, our method is able to find solutions that are closer to the actual route plans than when using distances, and hence, solutions that would require fewer manual changes to transform into the actual route plan.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Beldiceanu, N., Simonis, H.: A constraint seeker: finding and ranking global constraints from examples. In: Lee, J. (ed.) CP 2011. LNCS, vol. 6876, pp. 12–26. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-23786-7_4
Beldiceanu, N., Simonis, H.: A model seeker: extracting global constraint models from positive examples. In: Milano, M. (ed.) CP 2012. LNCS, pp. 141–157. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33558-7_13
Bessiere, C., Koriche, F., Lazaar, N., O’Sullivan, B.: Constraint acquisition. Artif. Intell. 244, 315–342 (2017)
Caceres-Cruz, J., Arias, P., Guimarans, D., Riera, D., Juan, A.A.: Rich vehicle routing problem: survey. ACM Comput. Surv. (CSUR) 47(2), 32 (2015)
Chen, S.F., Goodman, J.: An empirical study of smoothing techniques for language modeling. Comput. Speech Lang. 13(4), 359–394 (1999)
Dantzig, G.B., Ramser, J.H.: The truck dispatching problem. Manage. Sci. 6(1), 80–91 (1959)
Deguchi, Y., Kuroda, K., Shouji, M., Kawabe, T.: HEV charge/discharge control system based on navigation information. Technical report, SAE Technical Paper (2004)
Dragone, P., Teso, S., Passerini, A.: Constructive preference elicitation. Front. Robot. AI 4, 71 (2018)
Drexl, M.: Rich vehicle routing in theory and practice. Logistics Res. 5(1–2), 47–63 (2012)
Gama, J., Žliobaitė, I., Bifet, A., Pechenizkiy, M., Bouchachia, A.: A survey on concept drift adaptation. ACM Comput. Surv. (CSUR) 46(4), 44 (2014)
Johnson, W.E.: Probability: the deductive and inductive problems. Mind 41(164), 409–423 (1932)
Krumm, J.: A Markov model for driver turn prediction. In: Withrow, l.L. (eds.) SAE 2008 World Congress, Distinguished Speaker Award, April 2008
Laporte, G.: What you should know about the vehicle routing problem. Naval Res. Logistics (NRL) 54(8), 811–819 (2007)
Lau, H.C., Liang, Z.: Pickup and delivery with time windows: algorithms and test case generation. Int. J. Artif. Intell. Tools 11(03), 455–472 (2002)
Munari, P., Dollevoet, T., Spliet, R.: A generalized formulation for vehicle routing problems. arXiv preprint arXiv:1606.01935 (2016)
Picard-Cantin, É., Bouchard, M., Quimper, C.-G., Sweeney, J.: Learning parameters for the sequence constraint from solutions. In: Rueher, M. (ed.) CP 2016. LNCS, vol. 9892, pp. 405–420. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-44953-1_26
Potvin, J.Y., Dufour, G., Rousseau, J.M.: Learning vehicle dispatching with linear programming models. Comput. Oper. Res. 20(4), 371–380 (1993)
Wang, X., et al.: Building efficient probability transition matrix using machine learning from big data for personalized route prediction. Procedia Comput. Sci. 53, 284–291 (2015)
Ye, N., Wang, Z., Malekian, R., Lin, Q., Wang, R.: A method for driving route predictions based on hidden markov model. Math. Problems Eng. 2015, 12 (2015)
Yu, M., Nagarajan, V., Shen, S.: Minimum makespan vehicle routing problem with compatibility constraints. In: Salvagnin, D., Lombardi, M. (eds.) CPAIOR 2017. LNCS, vol. 10335, pp. 244–253. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-59776-8_20
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Switzerland AG
About this paper
Cite this paper
Canoy, R., Guns, T. (2019). Vehicle Routing by Learning from Historical Solutions. In: Schiex, T., de Givry, S. (eds) Principles and Practice of Constraint Programming. CP 2019. Lecture Notes in Computer Science(), vol 11802. Springer, Cham. https://doi.org/10.1007/978-3-030-30048-7_4
Download citation
DOI: https://doi.org/10.1007/978-3-030-30048-7_4
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-30047-0
Online ISBN: 978-3-030-30048-7
eBook Packages: Computer ScienceComputer Science (R0)