自适应大邻域搜索求解带容量约束的模块化Hub选址问题OA

The capacitated modular hub location problem(CMHLP),as an important extension of the classical hub location problem,has significant application value in fields such as modern logistics systems,transportation network planning,and telecommunications infrastructure design.Existing research indicates that current solution algorithms for the CMHLP still have notable shortcomings in both solution quality and computational efficiency,making it difficult to meet practical engineering requirements.To address this challenge,this paper proposes an adaptive large neighborhood search(ALNS)algorithm.The algorithm innovatively integrates the following core components:an initialization method based on a randomized greedy strategy,a hybrid destroy-and-repair operator targeting both hub nodes and terminal nodes,a local improvement strategy combined with tabu search,and a dynamic adaptive operator selection mechanism.Computational experiments demonstrate that the proposed algorithm significantly outperforms four mainstream CMHLP algorithms reported in the existing literature in terms of solution quality.Specifically,for 25 standard test instances,the algorithm successfully improves the best-known solution from the existing literature.This paper provides a practical and effective optimization approach for the hub location problem in complex network environments.