This study introduces the Iterative Chainlet Partitioning (ICP) algorithm and
its neural acceleration for solving the Traveling Salesman Problem with Drone
(TSP-D). The proposed ICP algorithm decomposes a TSP-D solution into smaller
segments called chainlets, each optimized individually by a dynamic programming
subroutine. The chainlet with the highest improvement is updated and the
procedure is repeated until no further improvement is possible. The number of
subroutine calls is bounded linearly in problem size for the first iteration
and remains constant in subsequent iterations, ensuring algorithmic
scalability. Empirical results show that ICP outperforms existing algorithms in
both solution quality and computational time. Tested over 1,059 benchmark
instances, ICP yields an average improvement of 2.75% in solution quality over
the previous state-of-the-art algorithm while reducing computational time by
79.8%. The procedure is deterministic, ensuring reliability without requiring
multiple runs. The subroutine is the computational bottleneck in the already
efficient ICP algorithm. To reduce the necessity of subroutine calls, we
integrate a graph neural network (GNN) to predict incremental improvements. Noi
demonstrate that the resulting Neuro ICP (NICP) achieves substantial
acceleration while maintaining solution quality. Compared to ICP, NICP reduces
the total computational time by 49.7%, while the objective function value
increase is limited to 0.12%. The framework’s adaptability to various
operational constraints makes it a valuable foundation for developing efficient
algorithms for truck-drone synchronized routing problems.

Questo articolo esplora i giri e le loro implicazioni.

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