# A reinforcement learning guided hybrid evolutionary algorithm for the latency location routing problem

arXiv: https://arxiv.org/abs/2403.14405 · HTML: https://mlsanity.com/paper/2403.14405/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in RL for games and combinatorial optimization, one of 15 directions in Deep reinforcement learning methods.
- Builds on 2 of its 35 references that are in the map.
- First posted on arXiv 21 Mar 2024.

## Placed in

- Deep reinforcement learning methods / [RL for games and combinatorial optimization](https://mlsanity.com/deeprl/rl-for-games-and-combinatorial-optimization/)

## Builds on (most cited first, 2 of 2)

- [Hybrid Genetic Search for the CVRP: Open-Source Implementation and SWAP* Neighborhood](https://mlsanity.com/paper/2012.10384/) (2020)
- [An effective hybrid search algorithm for the multiple traveling repairman problem with profits](https://mlsanity.com/paper/2111.05017/) (2021)

## Built on by (most cited first, 2 of 2)

- [Bandit based Dynamic Candidate Edge Selection in Solving Traveling Salesman Problems](https://mlsanity.com/paper/2505.15862/) (2025)
- [An End-to-End Learning Approach for Solving Capacitated Location-Routing Problems](https://mlsanity.com/paper/2511.02525/) (2025)

## Abstract (arXiv)

The latency location routing problem integrates the facility location problem and the multi-depot cumulative capacitated vehicle routing problem. This problem involves making simultaneous decisions about depot locations and vehicle routes to serve customers while aiming to minimize the sum of waiting (arriving) times for all customers. To address this computationally challenging problem, we propose a reinforcement learning guided hybrid evolutionary algorithm following the framework of the memetic algorithm. The proposed algorithm relies on a diversity-enhanced multi-parent edge assembly crossover to build promising offspring and a reinforcement learning guided variable neighborhood descent to determine the exploration order of multiple neighborhoods. Additionally, strategic oscillation is used to achieve a balanced exploration of both feasible and infeasible solutions. The competitiveness of the algorithm against state-of-the-art methods is demonstrated by experimental results on the three sets of 76 popular instances, including 51 improved best solutions (new upper bounds) for the 59 instances with unknown optima and equal best results for the remaining instances. We also conduct additional experiments to shed light on the key components of the algorithm.