Large-scale itinerary planning is a variant of the traveling salesman problem, aiming to determine an optimal path that maximizes the collected point-of-interest (POI) scores while minimizing travel time and cost, subject to travel duration constraints. This paper analyzes inter-component dependence in large-scale itinerary planning and establishes sufficient conditions for weak decomposability, under which an optimal path can visit each cluster as a contiguous subpath. Guided by this weak decomposability, we propose a novel multi-objective cooperative coevolutionary algorithm for large-scale itinerary planning, addressing the challenges of component imbalance and interactions. Specifically, we design a dynamic decomposition strategy based on the normalized fitness within each component, define optimization potential considering component scale and contribution, and develop a computational resource allocation strategy. Finally, we evaluate the proposed algorithm on 18 real-world test cases, eight large-scale semi-synthetic test cases, and eight adapted public clustered orienteering problem benchmark cases. Comparative results show that the proposed algorithm achieves the highest mean hypervolume across all 34 test cases and significantly outperforms the compared algorithms. Ablation and optimizer-decoupling analyses further validate the effectiveness of its core mechanisms and the general applicability of the proposed framework, with particularly pronounced performance gains on large-scale benchmark instances.

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