Doyoung Lee | Searcharxiv

arXiv2026

Neural Embedded Mixed-Integer Optimization for Location-Routing Problems

We present a framework that combines machine learning with mixed-integer optimization to solve the Capacitated Location-Routing Problem (CLRP), a classical NP-hard problem that integrates strategic facility location with operational vehicle routing decisions. The proposed method trains a neural network to approximate the cost of a Capacitated Vehicle Routing Problem (CVRP) for serving any subset of customers from a candidate facility. The network is trained on an independently generated dataset of CVRP instances from the literature, entirely separate from any CLRP test instances, thereby avoiding the overfitting and information leakage that can affect learning-based methods. The trained network is then embedded as a surrogate within a mixed-integer model for location-allocation decisions, which is solved using off-the-shelf solvers, thus leveraging decades of advances in vehicle routing and the availability of mature solvers. Computational experiments across four benchmark sets show that the method delivers reasonable solution quality and scales well to large instances, where, after a one-time training cost, it reaches solutions close to the best known at a fraction of the runtime of state-of-the-art heuristics. Our results demonstrate the value of routing cost approximations from the neural surrogate in informing high-quality location-allocation decisions. Our code and data are publicly available.

math.OC↗

arXiv2020

Reinforcement Learning of Graph Neural Networks for Service Function Chaining

In the management of computer network systems, the service function chaining (SFC) modules play an important role by generating efficient paths for network traffic through physical servers with virtualized network functions (VNF). To provide the highest quality of services, the SFC module should generate a valid path quickly even in various network topology situations including dynamic VNF resources, various requests, and changes of topologies. The previous supervised learning method demonstrated that the network features can be represented by graph neural networks (GNNs) for the SFC task. However, the performance was limited to only the fixed topology with labeled data. In this paper, we apply reinforcement learning methods for training models on various network topologies with unlabeled data. In the experiments, compared to the previous supervised learning method, the proposed methods demonstrated remarkable flexibility in new topologies without re-designing and re-training, while preserving a similar level of performance.

cs.AI↗