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This paper investigates hybrid quantum-classical neural networks for the vehicle routing problem, finding that encoder feed-forward replacement can reduce model parameters by 56.6% while maintaining near-baseline performance for small to medium instances.
This paper presents a modified JAMPR deep reinforcement learning model to solve the Pickup and Delivery problem with Capacity and Time Window constraints (CPDPTW), offering fast optimal solutions for small to medium-sized instances and suboptimal solutions for larger ones.
This paper introduces Smart Routes, a platform for developing and comparing algorithms for vehicle routing problems with realistic constraints, showing that deep learning and heuristic methods can match exact solutions in quality with less time for larger problem sizes.
This paper explores Relative Positional Encoding (RPE) as an additive bias in Transformer architectures to solve the Team Orienteering Problem, demonstrating consistent improvements in collected rewards and optimality gaps over vanilla Transformer architectures.
This paper proposes a reward-density heuristic for dynamic multi-vehicle routing problems that matches the solution quality of metaheuristics like ALNS, GA, and SA while requiring two to three orders of magnitude less planning time, demonstrated on drone task allocation and urban taxi dispatch scenarios.
This paper introduces Collaborative Routing Constructors (CoRC), a framework that enables independently solved subproblems to exchange customers and vehicles during optimization, improving feasibility and scalability for large-scale Capacitated Vehicle Routing Problems.
Introduces RACL, a reasoning-agent control layer that improves metaheuristic optimization by learning to control internal search behavior from operational memory, showing cost improvements in vehicle routing tests.
Researchers from Beihang University and Baidu propose 'constraint injection,' a dual verification method for LLM-based optimization modeling that detects spurious or omitted constraints beyond objective equivalence. They develop VRPCoder, an 8B model for translating natural-language vehicle routing problems into Gurobi scripts, achieving 93% average Pass@1 and outperforming Claude Sonnet and prior OR-LLMs by large margins.
COAgents is a cooperative multi-agent framework for solving Vehicle Routing Problems that models search as a graph, using specialized agents for node selection, move selection, and jumps to escape local minima. It achieves state-of-the-art results on CVRP and VRPTW benchmarks, reducing the gap to best-known solutions by up to 44% compared to prior learning-based methods.
This paper proposes a unified knowledge-embedded reinforcement learning framework for generalized capacitated vehicle routing problems, combining route-first cluster-second heuristics with dynamic programming to achieve superior solution quality and strong generalization across diverse variants.