Deep Policy Dynamic Programming is proposed, which aims to combine the strengths of learned neural heuristics with those of DP algorithms, and prioritizes and restricts the DP state space using a policy derived from a deep neural network, which is trained to predict edges from example solutions.
A reinforcement learning method with a shared attention encoder and a hierarchical dual-decoder architecture, where truck–drone coordination is achieved by first decoding the truck’s next node and then conditionally decoding the drone action.
The problem of route optimization with realistic constraints is becoming extremely relevant in the face of global urban population growth. While we are aware of approaches that theoretically provide an exact optimal solution, their application becomes challenging as the problem size increases because of exponential com...
A. Soroka, German Mikhelson, A. Mescheryakov et al.· Automation and remote contro...· 0 citations
These findings demonstrate that reinforcement learning is a promising and scalable alternative to conventional heuristic and metaheuristic approaches for capacitated routing problems, particularly in dynamic logistics environments that require rapid and adaptive decision making.
Audrey Ariij Sya'imaa.HS, Hilda Azkiyah, Khandker Farid Uddin Ahmed· International Journal of Mat...· 0 citations
This work introduces an optimization framework where a reinforcement learning agent is trained on prior instances and quickly generates initial solutions, which are then further optimized by a genetic algorithm, enabling real-time and interactive routing at scale.
Ido Greenberg, P. Sielski, Hugo Linsenmaier et al.· Communications AI & Computin...· 1 citation
It is shown that the JAMPR+/L2D model, proposed in to solve large CPDPTW problems can be adopted in the case of substantial changes of graph distance matrix, and generalizes well for tasks with simpler constraints (CVRP, VRPTW), for different problem sizes and for moderate changes in distance matrixes.
A multi-scale deep optimization model based on an encoder-decoder architecture that validates the effectiveness of the multi-scale EMA and Triplet-Reasoning mechanisms, providing a new direction for deep learning-based graph optimization research.
