Assignment-Routing Optimization : Efficient Heuristic Solver with Shaking Algorithm

TL;DR

This paper introduces an efficient heuristic solver for joint assignment and routing problems, combining Hungarian algorithm initialization, shaking algorithms, and simulated annealing to outperform traditional exact solvers on large instances.

Contribution

The paper presents a novel heuristic approach that significantly improves solution efficiency and quality for large-scale assignment-routing problems compared to existing methods.

Findings

Solver solves 1000-node problems within 1 minute

Solution accuracy is within 1% of ground truth

Outperforms Gurobi in large problem instances

Abstract

This paper works on heuristic solver for joint assignment and routing optimization problem. Study on previous works shows that MIP based exact solvers can only provide efficient solutions for small to moderate size problems, due to exponentially growing computational complexity. This paper proposes to start with high quality initial guess through Hungarian algorithm based assignment and heuristic cycle merging algorithm. Subsequently, the solution is improved based on a proposed shaking algorithm to improve the assignment and routing sequence. In addition, the shaking approach also enables the Simulated Annealing algorithm to further improve the solution, which is very difficult if it is purely based on random sampling updates of item and placeholder sequences. Extensive experimental validation comparing with ground truth from the previously shared database shows that the introduced solver is much more efficient than the Gurobi solver especially for large size problems, with a 1000 node pair problem being solved within 1 min in Python implementation. The solution accuracy is within a percent in general as compared with ground truth in database. Although there are spaces for the proposed solver to be further improved with better accuracy, it works for practical applications with acceptable path quality and sufficient solver efficiency. Such shaking algorithms based solvers can also be applied to more general joint assignment and routing optimization problem with multiple type of items and corresponding placeholders. GitHub repository: https://github.com/QL-YUAN/Joint-Assignment-Routing-Optimization-Heuristic.git