Abstract
Modern machine learning, including deep learning models and reinforcement learning techniques, have proven effective for solving difficult combinatorial optimization problems without relying on handcrafted heuristics. In this work, we present NOFSS, a Neural Order-First Split-Second deep reinforcement learning approach for the Capacity Constrained Vehicle Routing Problem (CVRP). NOFSS consists of a hybridization between a deep neural network model and a dynamic programming shortest path algorithm (Split). Our results, based on intensive experiments with several neural network model architectures, show that such a two-step hybridization enables learning of implicit algorithms (i.e. policies) producing competitive solutions for the CVRP.
S. Harispe—This work used HPC resources of IDRIS (allocation 2022-AD011011309R2) made by GENCI.
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Notes
- 1.
A tour is the ordering of clients the vehicle will visit before returning back to the depot. The optimal number of tours will therefore depend on client’s demands and vehicle capacity.
- 2.
Our implementation and results will be available on the following repository https://github.com/AYaddaden/NOFSS.
- 3.
\(softmax(s_i) = \frac{exp(s_i)}{\sum _{j=1}^{K}exp(s_j)}\).
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- 5.
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Yaddaden, A., Harispe, S., Vasquez, M. (2022). Neural Order-First Split-Second Algorithm for the Capacitated Vehicle Routing Problem. In: Dorronsoro, B., Pavone, M., Nakib, A., Talbi, EG. (eds) Optimization and Learning. OLA 2022. Communications in Computer and Information Science, vol 1684. Springer, Cham. https://doi.org/10.1007/978-3-031-22039-5_14
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