## 6 Conclusions and Look Forward  
In this paper, we present a novel DRL model tailored to solve the LRP for fresh produce. This model employs actors to generate and refine delivery solutions, with decreased training variance achieved through enhancements to the reward and punishment mechanisms. We developed mathematical models addressing multiple optimization objectives, including total operating costs, carbon emissions, and product losses. These models support the training of the DRL model using a fresh produce distribution simulator. Building on this foundation, we introduced the MODRL-SIA framework, integrating DRL with swarm intelligence algorithms to tackle the LRTTW. The framework offers superior accuracy, stability, and convergence speed compared to existing advanced hybrid algorithms, handling complex, large-scale challenges effectively.  
Future research will progress in several directions: First, we aim to investigate advanced reinforcement learning techniques, such as attention mechanisms for multi-agent collaboration, to augment the MODRL-SIA framework's performance. Second, we intend to extend the range of objectives and constraints to encompass not only total distribution costs, carbon emissions, and product losses but also environmental impacts and traffic congestion. Furthermore, acknowledging the uncertainties in the agricultural product supply chain, including demand variability and climatic changes, upcoming studies will integrate uncertainty modeling and decision-making strategies. We also plan to leverage emerging technologies like the Internet of Things and artificial intelligence to craft innovative solutions for optimizing cold chain storage site selection and logistics routing decisions.  
Figure 5: Comparison of Pareto frontiers at four scales.  
Additionally, given the prevalent risks in logistics systems, we will formulate efficient risk assessment models and product loss algorithms to manage risks and minimize economic losses effectively. Incorporating data analysis and machine learning techniques, future research will aim to elevate the intelligence and automation capabilities of logistics systems, providing more robust risk management and loss prevention strategies for logistics enterprises. These initiatives will propel the development of solutions for complex logistics routing and combinatorial optimization problems toward more comprehensive and efficient outcomes.  
Conceptualization, H.L. and J.Z.; methodology, H.L.; software, J.Z.; validation, Z.Z., Y.D. and L.Q.; formal analysis, Y.D.; investigation, L.Q.; resources, Y.D.; data curation, J.Z.; writing--original draft preparation, J.Z.; writing--review and editing, H.L.; visualization, Z.Z.; supervision, Y.D.; project administration, H.L.; funding acquisition, Y.D. All authors have read and agreed to the published version of the manuscript.  
This study was financially supported by the Project of Gansu Natural Science Foundation (21)R7 RA204 and 1506 RJZA007) and the Gansu Province Higher Education Innovation Foundation (2022B-107 and 2019A-056).  
Not applicable.  
The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.  
The authors have no relevant financial or non-financial interests to disclose.