Advances in Routing and Combinatorial Optimization

The field of routing and combinatorial optimization is witnessing significant advancements with the integration of machine learning and quantum computing techniques. Researchers are exploring novel approaches to tackle complex problems such as the Vehicle Routing Problem (VRP) and the Traveling Salesman Problem (TSP). The use of Graph Neural Networks (GNNs) and other learning-based methods is becoming increasingly popular for solving these problems. Additionally, the application of quantum algorithms to optimize robotic inspection trajectories and other industrial processes is showing promising results. Noteworthy papers in this area include 'Learning to Solve Multi-Objective Routing Problems on Multigraphs', which introduces two neural approaches to address multi-objective routing on multigraphs, demonstrating strong performance across various problems. Another notable paper, 'Learning to Segment for Vehicle Routing Problems', pioneers the formal study of the First-Segment-Then-Aggregate decomposition technique to accelerate iterative solvers, achieving up to 7x acceleration in state-of-the-art iterative solvers.

Sources

Learning to Solve Multi-Objective Routing Problems on Multigraphs

Towards a better approach to the Vehicle Routing Problem

When GNNs Met a Word Equations Solver: Learning to Rank Equations (Extended Technical Report)

Learning for routing: A guided review of recent developments and future directions

Temporal Orienteering with Changing Fuel Costs

Learning to Segment for Vehicle Routing Problems

PCPP-Based Reconfiguration Inapproximability: Query Complexity vs. Soundness Gap Trade-offs

Quantum-Assisted Automatic Path-Planning for Robotic Quality Inspection in Industry 4.0

Customized Exploration of Landscape Features Driving Multi-Objective Combinatorial Optimization Performance

MILP-SAT-GNN: Yet Another Neural SAT Solver

Multiple Watchman Routes in Staircase Polygons

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