## 6 Conclusions  
This paper studies the joint optimization of the threshold setting of large parcels, the location selection of multi-functional transit centers, and the allocation of terminal demand points under the implementation of multi-product (parcel) collaborative distribution by urban logistics enterprises. In the second part of this paper, the threshold setting of large parcels and standard parcels is introduced, and the differences in handling, sorting, and other processing links and related facilities and site configurations between oversized parcels and standard parcels are comprehensively considered, and the type variables of multi-functional transit centers are constructed in the second part of this paper. An integer programming model is constructed with the goal of minimizing the total cost of the logistics system. Firstly, in view of the shortcomings of the original honey badger algorithm, Chebyshev chaotic mapping is first introduced in the initial population processing stage, which effectively maintains the diversity of the population and avoids premature convergence of the algorithm. Then, the refractive mirror learning strategy (RML) is introduced in the foal position update formula to increase the diversity of the population to find better candidate solutions. Finally, the position update of honey badger is carried out by using the strategy of mixing moth fire fighting and golden sinusoid, which fundamentally changes the mining and search mechanism of the honey badger algorithm, so that the search space of the honey badger in the exploration stage is reduced, thereby greatly shortening the optimization time of the honey badger. Through comparative experiments, it is proved that the improved honey badger algorithm has good global and local optimization ability, but also has good robustness. Then, the logistics model established in the second part above was optimized, and the hybrid transit center was opened in a timely manner according to the solution of the lower and upper bound solutions. Finally, the effectiveness of the improved honey badger algorithm is verified by comparing with the CPLEX operation results. Then, according to the operation data of SF Jinzhou, a typical domestic express delivery company, the optimization results of the large parcel threshold, the location layout of the multi-functional transit center, and the allocation of terminal demand points were obtained, and the impact of the distribution mode, the volume of parcels delivered and received, and the service time were analyzed. From the above many experiments, it is not difficult to see that, on the one hand, the group intelligence algorithm has advantages in solving the problem of logistics site selection and path optimization. On the other hand, from the comparison results of the examples, it can also be obtained that SF Jinzhou Company's optimized large size threshold is 15 kg, which is different from the existing threshold of 20 kg, and the total cost of the optimization plan is compared with the existing situation, and the total logistics cost and average unit price cost are reduced by 9.6%. Therefore, the optimization scheme presented by the model and algorithm in this paper is robust, which can not only meet the needs of urban logistics enterprises of different parcel scales, but also apply to the daily fluctuation of parcel delivery and collection volume of logistics enterprises of the same type of city. For urban logistics enterprises that operate multiple types of parcels, there is no fixed standard for the selection of large item threshold; urban logistics enterprises need to reasonably select the large size threshold according to the distribution of parcel delivery and collection structure, and comprehensively consider relevant factors.