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Neural Combinatorial Optimization has been researched actively in the lasteight years. Even though many of the proposed Machine Learning based approachesare compared on the same datasets, the evaluation protocol exhibits essentialflaws and the selection of baselines often neglects State-of-the-Art OperationsResearch approaches. To improve on both of these shortcomings, we propose theRouting Arena, a benchmark suite for Routing Problems that provides a seamlessintegration of consistent evaluation and the provision of baselines andbenchmarks prevalent in the Machine Learning- and Operations Research field.The proposed evaluation protocol considers the two most important evaluationcases for different applications: First, the solution quality for an a priorifixed time budget and secondly the anytime performance of the respectivemethods. By setting the solution trajectory in perspective to a Best KnownSolution and a Base Solver's solutions trajectory, we furthermore propose theWeighted Relative Average Performance (WRAP), a novel evaluation metric thatquantifies the often claimed runtime efficiency of Neural Routing Solvers. Acomprehensive first experimental evaluation demonstrates that the most recentOperations Research solvers generate state-of-the-art results in terms ofsolution quality and runtime efficiency when it comes to the vehicle routingproblem. Nevertheless, some findings highlight the advantages of neuralapproaches and motivate a shift in how neural solvers should be conceptualized.

Routing Arena: A Benchmark Suite for Neural Routing Solvers