10 research outputs found
Attention, Filling in The Gaps for Generalization in Routing Problems
Machine Learning (ML) methods have become a useful tool for tackling vehicle
routing problems, either in combination with popular heuristics or as
standalone models. However, current methods suffer from poor generalization
when tackling problems of different sizes or different distributions. As a
result, ML in vehicle routing has witnessed an expansion phase with new
methodologies being created for particular problem instances that become
infeasible at larger problem sizes.
This paper aims at encouraging the consolidation of the field through
understanding and improving current existing models, namely the attention model
by Kool et al. We identify two discrepancy categories for VRP generalization.
The first is based on the differences that are inherent to the problems
themselves, and the second relates to architectural weaknesses that limit the
model's ability to generalize. Our contribution becomes threefold: We first
target model discrepancies by adapting the Kool et al. method and its loss
function for Sparse Dynamic Attention based on the alpha-entmax activation. We
then target inherent differences through the use of a mixed instance training
method that has been shown to outperform single instance training in certain
scenarios. Finally, we introduce a framework for inference level data
augmentation that improves performance by leveraging the model's lack of
invariance to rotation and dilation changes.Comment: Accepted at ECML-PKDD 202
Routing Arena: A Benchmark Suite for Neural Routing Solvers
Neural Combinatorial Optimization has been researched actively in the last
eight years. Even though many of the proposed Machine Learning based approaches
are compared on the same datasets, the evaluation protocol exhibits essential
flaws and the selection of baselines often neglects State-of-the-Art Operations
Research approaches. To improve on both of these shortcomings, we propose the
Routing Arena, a benchmark suite for Routing Problems that provides a seamless
integration of consistent evaluation and the provision of baselines and
benchmarks prevalent in the Machine Learning- and Operations Research field.
The proposed evaluation protocol considers the two most important evaluation
cases for different applications: First, the solution quality for an a priori
fixed time budget and secondly the anytime performance of the respective
methods. By setting the solution trajectory in perspective to a Best Known
Solution and a Base Solver's solutions trajectory, we furthermore propose the
Weighted Relative Average Performance (WRAP), a novel evaluation metric that
quantifies the often claimed runtime efficiency of Neural Routing Solvers. A
comprehensive first experimental evaluation demonstrates that the most recent
Operations Research solvers generate state-of-the-art results in terms of
solution quality and runtime efficiency when it comes to the vehicle routing
problem. Nevertheless, some findings highlight the advantages of neural
approaches and motivate a shift in how neural solvers should be conceptualized
Solving the Traveling Salesperson Problem with Precedence Constraints by Deep Reinforcement Learning
This work presents solutions to the Traveling Salesperson Problem with
precedence constraints (TSPPC) using Deep Reinforcement Learning (DRL) by
adapting recent approaches that work well for regular TSPs. Common to these
approaches is the use of graph models based on multi-head attention (MHA)
layers. One idea for solving the pickup and delivery problem (PDP) is using
heterogeneous attentions to embed the different possible roles each node can
take. In this work, we generalize this concept of heterogeneous attentions to
the TSPPC. Furthermore, we adapt recent ideas to sparsify attentions for better
scalability. Overall, we contribute to the research community through the
application and evaluation of recent DRL methods in solving the TSPPC.Comment: This preprint has not undergone peer review or any post-submission
improvements or corrections. The Version of Record of this contribution is
published in KI 2022: Advances in Artificial Intelligence, and is available
online at https://doi.org/10.1007/978-3-031-15791-2_1