Existing neural heuristics often train a deep architecture from scratch for
each specific vehicle routing problem (VRP), ignoring the transferable
knowledge across different VRP variants. This paper proposes the cross-problem
learning to assist heuristics training for different downstream VRP variants.
Particularly, we modularize neural architectures for complex VRPs into 1) the
backbone Transformer for tackling the travelling salesman problem (TSP), and 2)
the additional lightweight modules for processing problem-specific features in
complex VRPs. Accordingly, we propose to pre-train the backbone Transformer for
TSP, and then apply it in the process of fine-tuning the Transformer models for
each target VRP variant. On the one hand, we fully fine-tune the trained
backbone Transformer and problem-specific modules simultaneously. On the other
hand, we only fine-tune small adapter networks along with the modules, keeping
the backbone Transformer still. Extensive experiments on typical VRPs
substantiate that 1) the full fine-tuning achieves significantly better
performance than the one trained from scratch, and 2) the adapter-based
fine-tuning also delivers comparable performance while being notably
parameter-efficient. Furthermore, we empirically demonstrate the favorable
effect of our method in terms of cross-distribution application and
versatility.

通过跨问题学习和细调不同变种目标问题的 Transformer 模型，本论文提出了一种用于改善车辆路线问题的神经启发式训练方法，展示了与从头开始训练相比较，全细调和适配器细调技术能够取得更好性能和参数效率的优势，并证明了该方法在交叉分布应用和通用性方面的有利影响。

解决车辆路径问题的跨问题学习

Cross-Problem Learning for Solving Vehicle Routing Problems

Recently, neural heuristics based on deep reinforcement learning have
exhibited promise in solving multi-objective combinatorial optimization
problems (MOCOPs). However, they are still struggling to achieve high learning
efficiency and solution quality. To tackle this issue, we propose an efficient
meta neural heuristic (EMNH), in which a meta-model is first trained and then
fine-tuned with a few steps to solve corresponding single-objective
subproblems. Specifically, for the training process, a (partial)
architecture-shared multi-task model is leveraged to achieve parallel learning
for the meta-model, so as to speed up the training; meanwhile, a scaled
symmetric sampling method with respect to the weight vectors is designed to
stabilize the training. For the fine-tuning process, an efficient hierarchical
method is proposed to systematically tackle all the subproblems. Experimental
results on the multi-objective traveling salesman problem (MOTSP),
multi-objective capacitated vehicle routing problem (MOCVRP), and
multi-objective knapsack problem (MOKP) show that, EMNH is able to outperform
the state-of-the-art neural heuristics in terms of solution quality and
learning efficiency, and yield competitive solutions to the strong traditional
heuristics while consuming much shorter time.

我们提出了一种高效的元神经启发式方法（EMNH），通过使用基于深度强化学习的神经启发式方法来解决多目标组合优化问题，以提高学习效率和解决质量。

多目标组合优化的高效元神经启发式算法

Efficient Meta Neural Heuristic for Multi-Objective Combinatorial  Optimization

Routing problems are a class of combinatorial problems with many practical
applications. Recently, end-to-end deep learning methods have been proposed to
learn approximate solution heuristics for such problems. In contrast, classical
dynamic programming (DP) algorithms guarantee optimal solutions, but scale
badly with the problem size. We propose Deep Policy Dynamic Programming (DPDP),
which aims to combine the strengths of learned neural heuristics with those of
DP algorithms. DPDP 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. We evaluate our framework on the travelling salesman problem
(TSP), the vehicle routing problem (VRP) and TSP with time windows (TSPTW) and
show that the neural policy improves the performance of (restricted) DP
algorithms, making them competitive to strong alternatives such as LKH, while
also outperforming most other 'neural approaches' for solving TSPs, VRPs and
TSPTWs with 100 nodes.

Deep Policy Dynamic Programming 优先和限制来自深度神经网络的策略，以结合学习嵌入式启发式和动态规划 (DP) 算法的优点，以优化旅行商问题 (TSP)、车辆路径问题 (VRP) 和带时间窗口的 TSP (TSPTW) 的性能，同时胜过大多数其他解决 TSPs、VRPs 和 TSPTWs 的 ' 神经方法 '。