Search Results for author:
Found 9 papers, 6 papers with code
PARCO: Learning Parallel Autoregressive Policies for Efficient Multi-Agent Combinatorial Optimization
Multi-agent combinatorial optimization problems such as routing and scheduling have great practical relevance but present challenges due to their NP-hard combinatorial nature, hard constraints on the number of possible agents, and hard-to-optimize objective functions.
Genetic Algorithms with Neural Cost Predictor for Solving Hierarchical Vehicle Routing Problems
For each higher-level decision candidate, we predict the objective function values of the underlying vehicle routing problems using a pre-trained graph neural network without actually solving the routing problems.
A Hybrid Genetic Algorithm for the min-max Multiple Traveling Salesman Problem
This paper proposes a hybrid genetic algorithm for solving the Multiple Traveling Salesman Problem (mTSP) to minimize the length of the longest tour.
A Neural Separation Algorithm for the Rounded Capacity Inequalities
We design a learning-based separation heuristic algorithm with graph coarsening that learns the solutions of the exact separation problem with a graph neural network (GNN), which is trained with small instances of 50 to 100 customers.
RL4CO: an Extensive Reinforcement Learning for Combinatorial Optimization Benchmark
To fill this gap, we introduce RL4CO, a unified and extensive benchmark with in-depth library coverage of 23 state-of-the-art methods and more than 20 CO problems.
A Hybrid Genetic Algorithm with Type-Aware Chromosomes for Traveling Salesman Problems with Drone
This study presents a hybrid genetic algorithm for solving TSPD and FSTSP by incorporating local search and dynamic programming.
A Deep Reinforcement Learning Approach for Solving the Traveling Salesman Problem with Drone
Reinforcement learning has recently shown promise in learning quality solutions in many combinatorial optimization problems.
An Attention-LSTM Hybrid Model for the Coordinated Routing of Multiple Vehicles
State-less attention-based decoder fails to make such coordination between vehicles.
A Reinforcement Learning Approach for Rebalancing Electric Vehicle Sharing Systems
This paper proposes a reinforcement learning approach for nightly offline rebalancing operations in free-floating electric vehicle sharing systems (FFEVSS).
