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Mixed with the dynamic programming search methodology, the proposed algorithm was used to acquire the allocation of demand points to different routes and to determine the frequency of departure in the first part. A two-phase multi-objective non-dominated sorting genetic (NSGA-II) algorithm was additional designed to resolve this NP-hard drawback. The whole travel time and transfer distance of the proposed model were lowered compared to the conventional mannequin underneath all scenarios. Compared with the traditional model, the entire transfer distance of the proposed model was lowered by 32.1 km. The objective operate (2) worth was decreased as the objective operate (1) g
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