## 2 Related Work  
To ensure the delivery of fresh agricultural products to consumers with enhanced quality and to align with the \"double carbon\" policy while reducing distribution costs, researchers have implemented various optimization strategies. These strategies focus on minimizing delivery distance, time, costs, and carbon emissions. In fundamental research, the distribution challenges of fresh agricultural products within a region are modeled as a Location-Routing Problem (LRP). This model takes into account factors such as vehicle load constraints, travel distance limitations, and time window constraints at customer points and distribution warehouses. [PERSON], [PERSON] et al. [7] suggest integrating Variable Neighborhood Search (VNS) with Path Relinking (PR) to address the LRPTW, considering its extensive solution pools. [PERSON], [PERSON] et al. [8] have designed an evolutionary multitasking optimization algorithm to simultaneously solve multiple routing tasks. [PERSON], [PERSON] et al. [9] developed a Mixed-Integer Linear Programming (MILP) model for the LRPTW. Sutrpropose the CSNS, which considers a probabilistic mechanism for facility selection and utilizes the k-means clustering algorithm to generate routing solutions with simultaneous neighborhood search. [PERSON], [PERSON] et al. [11] summarize and discuss the multi-objective LRP, demonstrating its relevance to practical application environments. They introduce improved methods for solving multi-objective LRP, including a weighted summing technique and a Genetic Algorithm (GA) [12]. Additional Developments: The uncertainty of some key parameters in the LRP is addressed through the application of a robust fuzzy optimization model [13]. Furthermore, fuzzy chance-constrained programming is proposed to tackle demand uncertainty and resolve the uncertainty problem [14]. Domestic and international scholars have extensively explored the LRP in terms of model construction, extraction of real production constraints, and development of scheduling solution algorithms. The LRP integrates vehicle routing and location problems, both recognized as NP-hard, which explains the relative scarcity of research in this field. Notably, to ensure the timely delivery of fresh agricultural products, time window constraints are incorporated into the LRP. Traditionally, LRP research often omits environmental facility constraints to simplify the mathematical model and ease the difficulty of solving it. However, real-world constraints are crucial in actual distribution environments. For example, new energy trucks face energy limitations, and delivery points must operate within specific time windows. Consequently, studying the multi-constrained LRPTW is of significant practical research value. Domestic and foreign scholars have extensively explored and researched LRP in terms of model construction, actual production constraint extraction, and scheduling solution algorithms. LRP combines the vehicle route planning problem and location problem. Since both are NP-hard problems, there are relatively few research results in this area. Particularly, to ensure the timely delivery of fresh agricultural products, time window constraints are added to the LRP problem. In existing LRP research, environmental facilities are typically not constrained to simplify the mathematical model and reduce solving difficulty. However, real-world constraints need consideration in actual distribution environments. For instance, new energy trucks have energy restrictions, and delivery points operate within specified time windows. Therefore, studying the multi-constrained LRPTW holds significant practical research value. [PERSON], [PERSON] et al. [15] have developed a general framework for solving various combinatorial optimization problems by integrating deep reinforcement learning (RL) agents into the Adaptive Large Neighborhood Search (ALNS) framework. [PERSON], [PERSON] et al.