## 1 Introduction  
Fresh agricultural products are essential for daily life and constitute a significant market sector [1]. The logistics of these products involve a dynamic circulation process that encompasses early production, processing, integrated packaging, warehouse storage, and subsequent distribution to consumers, accompanied by continuous information exchange [2]. However, the logistics chain for fresh produce faces various risks during implementation, including costs and potential damage to the goods. Therefore, it is imperative to adopt cold chain transportation and distribution solutions to improve the current situation.  
Fresh agricultural products are characterized by their variety, short life cycles, perishability, high logistics demands, substantial volume, and stringent storage requirements [3]. Consequently, the logistics service quality standards for these products are distinct from those for other goods, necessitating elevated service levels. Furthermore, the primary consumption areas for these products are spread across various urban locations. The need to deliver to numerous customer points, coupled with uneven distribution requirements, complicates the delivery process. In the traditional supply chain, fresh agricultural products pass through several stages, including distributors and retailers, before reaching consumers [4]. However, in modern supply chain systems, the process is streamlined to just three levels: producer, fresh agricultural product platform, and consumer. This simplification significantly reduces both the intermediary cost and the time needed for purchasing.  
Establishing a cold chain distribution system for fresh agricultural products requires meticulous attention to proper storage and distribution from platform warehouses to consumers. Representing the agricultural aspect of Industry 4.0, modern agriculture differs from agricultural industrialization by embracing smart agriculture. This approach is characterized by automation, personalization, artistic integration, ecological considerations, large-scale operations, and precision agriculture, all driven by the smart economy with a primary focus on the big health industry [5]. Ensuring the timely delivery of agricultural products is crucial to maintaining freshness and minimizing losses due to improper storage. In accordance with the central government's \"double carbon\" policy, the newly established logistics and distribution system employs new energy vehicles, thereby setting higher standards for cold chain distribution and transportation solutions [6].  
The main contributions of this article are summarized as follows:  
1. This paper is the first to construct a mathematical model of the Location-Routing Problem with Time Windows (LRPTWs) and introduces a multi-objective framework for LRPTW based on three distinct objectives.
2. A multi-objective swarm intelligence algorithm framework utilizing deep reinforcement learning was developed to minimize operating costs, emissions, and product losses. The DQNAgent module, trained with optimized network parameters via reward and punishment functions, incorporates various local search strategies to enhance solution quality.
3. The effectiveness of the MODRL-SIA in addressing the multi-objective LRPTW problem was demonstrated by superior performance metrics, specifically through Hypervolume (HV) and Inverted Generational Distance (IGD) indicators.  
The remaining structure of this paper is as follows: Section 2 reviews the relevant literature, Section 3 outlines the definition of the LRPTW problem, and Section 4 introduces the details of the MODRL-SIA algorithm framework. Section 5 describes the experiments and results analysis. The final section summarizes the entire paper, discussing its impact and limitations.