## 2 Literature Review
[37] created a six-level sustainable dynamic reverse logistics network model for recycling retired new energy electric vehicle batteries, integrating economic, environmental, and social objectives, while evaluating environmental impacts through a non-life-cycle assessment and considering social factors like employee safety and community disturbances. [PERSON] et al. [38] applied the Best-Worst Method (BWM) to assess and control risk in the reverse logistics of medical waste, identifying key risk factors across five dimensions: logistics operations, emergency capacity, equipment, personnel, and management. Reverse logistics network research primarily concentrates on network design, node optimization, and uncertainty factors. Compared with forward logistics, reverse logistics comprises greater uncertainty, and complexity. Figure 3 illustrates how forward and reverse logistics work together. Forward logistics delivers parts and materials through procurement, fabrication, and assembly. At the same time, reverse logistics deals with returns such as damaged parts, leftover materials, or rework, sending them for reuse, recycling, repair, or disposal. As such, some scholars have studied uncertainty factors in the recycling process, with a focus on optimizing the economy of recycling costs, the timeliness of transport, and resource conservation [39]. [PERSON] et al. [40] developed a mixed-integer linear programming model (MILP) to minimize the total cost of wood recycling, accounting for uncertain factors such as the location of supply sources, the available quantity of recycled wood at collection sites, and the various quality grades of the collected wood. [PERSON] et al. [41] formulated a location inventory routing problem model without quality defect returns, proposing an effective hybrid genetic simulated annealing algorithm (HGSAA). [PERSON] et al. [42] presented a two-stage stochastic mixed-integer programming model that accounted for uncertainties in product quality and quantity, product variety, and bill of materials. With regards to the research of reverse supply chain networks, apart from focusing on cost minimization and uncertainty, the importance of environmental and social issues has been gradually emphasized. In recent years, many scholars have turned their attention to carbon emissions. [PERSON] et al. [43] proposed a mixed-integer linear programming (MILP) model to solve a multi-tier, multi-period green RLnetwork, integrating facility locations, vehicle type selection, and emissions produced during transportation and operations at various processing centers. [PERSON] and [PERSON] [44] established a location routing inventory model for a green reverse supply chain network, which minimized the total costs of the chain and lost demands simultaneously, utilizing a fuzzy multi-objective solution approach. [PERSON] et al. [44] investigated the Vehicle Routing Problem with Divisible Deliveries and Pickups (VRPDDP) in reverse logistics, focusing on economic and environmental impacts and how vehicle capacity and customer visit splitting influence performance. Table 1 summarizes the representative studies and their key areas, and highlights the research gaps. In summary, scholars are currently focusing on reverse logistics networks from a perspective of timing and economics, with attention to hazardous wastes mainly concentrated on electronic components, medical wastes, chemicals, and municipal hazardous waste, with a focus on the cost of recycling and the treatment process. Furthermore, some studies concentrate on recycling modes [55; 56; 57] and reverse supply chain evaluation [58; 59], among other topics [60; 61]. However, during the actual recycling process, certain hazardous waste materials pose environmental, health, and safety risks, and existing research rarely takes into account a comprehensive consideration of cost, efficiency, safety risks, green aspects, and other factors from the perspective of HSE management.