## 5 Conclusions and Future Work  
A combined intelligent logistics service framework is proposed based on the Internet of Things technology, network communication technology, and coordinated delivery optimization technology in this paper. This study not only focuses on how to realize intelligent logistics services, but also is directly related to reducing customer waiting time, improving customer satisfaction, and the efficiency of completing tasks. The research significance of this article is mainly reflected in two aspects. (a) This article provides detailed ideas on how to design an intelligent distribution system, including how to realize the start-up time and path planning solutions of customers and corresponding robots. (b) This article uses the proposed methods to obtain the corresponding solutions in the form of a case study, which helps the operator to obtain a better reference in practice. At the departure service station, SR replenish their consumables, including battery charging and loading delivery products. In this way, through solving the problem of being unable to operate remotely for a long time due to the limitation of the power level and load capacity of the robot, we realize the cooperative and persistent delivery service of SR. Corresponding functions are developed for the different customer-oriented needs and the transportation characteristics of the robot itself, including the load weight effects, transportation time, and the behavior of the robot during the idle state. The constructed SR MILP is advanced and applied to the delivery service of large-scale smart communities. The S-GAIPSO algorithm and the solver CPLEX are tested with numerical examples of different scales, and the superiority of the proposed algorithm is proved by the comparison of optimality loss and CPU time. We hope that this research will contribute to existing and emerging robotic applications, improving customer satisfaction by shortening the time customers wait for payment. Although this article uses model data instead of the actual case data to verify the methods and algorithms we designed, the speed of the robot is based on the existing data, and all task data are based on realistic simulations. Therefore, it does not affect the theoretical and practical value of this paper. Due to the limitation of practical conditions, in the future, the SR of large smart communities can be considered for the experiment  
\begin{table}
\begin{tabular}{c c c c c c c} \hline \hline \multirow{2}{*}{**Experiment**} & \multirow{2}{*}{**Task Size**} & \multicolumn{3}{c}{**CPLEX**} & \multicolumn{3}{c}{**S-GAIPSO**} & \multirow{2}{*}{**GAP**} \\ \cline{3-3} \cline{5-7}  & & \begin{tabular}{c} **CPU Time** \\ **(s)** \\ \end{tabular}  & \begin{tabular}{c} **Obj.** \\ **Value** \\ \end{tabular}  & \begin{tabular}{c} **CPU Time** \\ **(s)** \\ \end{tabular}  & \ \begin{tabular}{c} **Obj.** \\ **Value** \\ \end{tabular}  & \begin{tabular}{c} **GAP** \\ **Value** \\ \end{tabular}  \\ \hline
1 & & & 0.63 & 2563 & 0.063 & 2563 & 0\% \\
2 & & 0.59 & 2436.8 & 0.065 & 2436.8 & 0\% \\
3 & 5 & 0.86 & 2957.7 & 0.062 & 2946.3 & 0.388\  
of logistics distribution. Our next goal is to consider the collision of obstacles in the driving path of the robot. The dynamic vehicle path optimization problem with changes in customer demand and time window will be more in line with the actual situation of future distribution. In addition, there may be more optimized and comprehensive algorithms for model resolution. Conceptualization, M.Z. and B.Y. ; methodology, M.Z. and B.Y. ; software, M.Z. ; validation, M.Z. and B.Y. ; formal analysis, M.Z. ; investigation, M.Z. and B.Y. ; resources, M.Z. ; data curation, M.Z. ; writing--original draft preparation, M.Z. and B.Y. ; writing--review and editing, M.Z. and B.Y. ; visualization, M.Z. ; supervision, B.Y. ; project administration, B.Y. ; funding acquisition, B.Y. All authors have read and agreed to the published version of the manuscript.