An Amortized Efficiency Threshold for Comparing Neural and Heuristic Solvers in Combinatorial Optimization

TL;DR

This paper introduces the Amortized Efficiency Threshold (AET), a framework to compare neural and heuristic solvers in combinatorial optimization based on deployment volume and energy efficiency, with an empirical case study on CVRP.

Contribution

The paper presents the AET framework, an open measurement protocol, and an end-to-end benchmarking pipeline for evaluating neural versus heuristic solvers in terms of energy and deployment volume.

Findings

The cumulative-energy ratio tends to a constant below one when the neural solver wins per instance.

The measured operational crossover point is approximately 4,560 instances for the CVRP case study.

The neural-to-heuristic energy ratio at crossover is about 0.0023.

Abstract

A common critique of neural combinatorial-optimization solvers is that they are less energy-efficient than CPU metaheuristics, given the operational energy cost of training them on GPUs. This paper examines the inferential step from "training is expensive" to "neural solvers are net-inefficient", which is where the critique actually goes wrong. Training the network costs a large fixed amount of GPU energy; running the metaheuristic costs a small amount of CPU energy on every instance, repeated as long as the solver is deployed. The two are not commensurable until a deployment volume is fixed. We define the Amortized Efficiency Threshold (AET) as the deployment volume above which a neural solver breaks even with a heuristic baseline in total energy or carbon, under an explicit constraint on solution quality. We show that the cumulative-energy ratio between the two solvers tends to a constant strictly below one whenever the network wins per instance, and that this limit does not depend on how the training cost was measured. An embodied-carbon term amortizes hardware fabrication symmetrically on both sides. We instantiate the framework on the CVRP environment at n=50 customers with the attention-based autoregressive solver of Kool et al. (2019), trained for 100 epochs on 20,000 instances over five random seeds, and HGS via PyVRP as the heuristic baseline. The measured operational crossover sits near 4.56e3 deployed instances at the median of a six-point baseline-budget sweep; the per-instance neural-to-heuristic ratio is 2.29e-3. The contribution is the framework, the open instrumentation, and the end-to-end measurement protocol. Code and benchmark pipeline are available at https://github.com/sohaibafifi/aet.