AI Solver Optimizes Joint Routing-Assignment Problems

A new AI solver has been developed to address the Joint Routing-Assignment (JRA) problem, delivering near-optimal solutions for large-scale optimization challenges. This innovative approach combines Partial Path Reconstruction (PPR) and a Large-α constraint to improve efficiency in robotics, logistics, and manufacturing.

The Joint Routing-Assignment Problem

The JRA problem involves optimizing the assignment of items to placeholders while determining the most efficient route for visiting each node pair. Previous methods struggled with scalability and accuracy, particularly for large datasets. The new solver overcomes these limitations by achieving highly accurate solutions with minimal computational overhead.

Innovations in the Solver

The proposed method introduces a Partial Path Reconstruction (PPR) solver, which identifies key item-placeholder pairs to form a reduced subproblem. This subproblem is solved efficiently to refine the global solution. Additionally, a global Large-α constraint is incorporated into the JRA model to enhance solution optimality.

The PPR framework allows initial heuristic merging solutions to be iteratively improved, resulting in highly accurate tours. Experimental evaluations on benchmark datasets with sizes up to n=1000 demonstrate that the solver consistently delivers almost optimal solutions, with an average deviation of 0.00% from the ground truth.

Applications and Potential

Beyond the JRA problem, the PPR method and its variations show strong potential for broader applications, including the classical Traveling Salesman Problem (TSP) and related routing and logistics optimization problems. The solver addresses challenges in robotics, logistics, and manufacturing, where transporting discrete objects requires interdependent decisions related to assignment, sequencing, and routing.

The JRA solver provides a foundation for complex problems in robot task planning and execution, with applications in item packaging, screw assembly, robot planting, and room tidying. Its ability to handle large-scale optimization makes it a valuable tool for industries seeking to improve operational efficiency.

Conclusion

The new AI solver represents a significant advancement in routing and assignment optimization. By leveraging PPR and Large-α constraints, it delivers near-optimal solutions for complex problems, paving the way for more efficient and effective operations in robotics, logistics, and manufacturing.