## 2 Literature Review
They proposed a multi-objective non-dominated ranking genetic algorithm to solve a multi-objective model and used the multi-objective non-dominated ranking genetic algorithm to solve a supply chain problem in the medical sector in Iran, proving that the proposed method can provide a more reliable solution in a real pharmaceutical supply chain. [PERSON] et al. (2017) proposed a pharmaceutical supply chain network design mathematical model. They discussed a new robust likelihood optimization method to validate the effectiveness of the proposed mathematical model. [PERSON] (2018) constructed a time-reliability calculation method for pharmaceutical cold chain logistics without intersecting the minimum set of paths, established a time-reliability-based optimization model for pharmaceutical cold chain logistics networks, and designed a stochastic simulation genetic algorithm to solve the model to solve the pharmaceutical cold chain logistics distribution problem that satisfies node reliability. [PERSON] et al. (2019) determined the number of shipments and vessel deployments in a multi-period planning environment with independent perishable product demand to reduce the cost of transporting perishable products such as in the cold pharmaceutical chain. [PERSON] et al. (2020) developed two new meta-heuristic algorithms for the pharmaceutical supply chain network (PSCN) design problem and improved the social engineering optimization (ISEO) and the hybrid firefly and simulated annealing algorithm (HFFA-SA) to solve a new mixed-integer nonlinear programming model. This new mixed-integer nonlinear programming model is solved to minimize the total cost and delivery time of pharmaceutical products to hospitals and pharmacies, while maximizing the reliability of the transportation system. In addition to the optimization of the path optimization in the pharmaceutical cold chain, many researchers simultaneously studied the refrigeration materials and new devices used in the pharmaceutical cold chain. [PERSON] et al. (2018) proposed a hybrid method based on an improved bidirectional gated cyclic unit (IBGRU) network and unscented Kalman filter (UFK), which achieves battery stability at different temperatures and has good estimation accuracy and robustness. [PERSON] et al. (2020) proposed two kinds of Li-ion battery estimation models and a variety of equivalent circuit models for different practical applications and designed an improved particle swarm optimization algorithm (IPSO) to optimize the model. The experimental results show that the proposed method is more flexible for Li-ion battery health state estimation. [PERSON] et al. (2020) proposed a hybrid method based on the CNN-BWGRU network to solve the problem of the accurate estimation of the battery State of Charge (SOC), which provides useful guidance for the safe and stable operation of batteries in the natural environment. [PERSON] et al. (2020) designed a time convolution model of a supercapacitor to more accurately predict its remaining service life. At the same time, they used the Adam convolution algorithm to optimize the time network parameter adjustment process; through the experiment, it is concluded that the time convolution network model has stronger robustness and higher accuracy for the prediction of the residual service life of the supercapacitor. [PERSON] et al. (2020) assessed refrigerators in the air thermal behavior of two kinds of situations; throughout the operation cycle (charging and discharging) used in the different phase change materials, the results show that their study system of the integrated PCM can significantly reduce the consumption of cooling needed for when the door opened, thereby reducing greenhouse gas emissions and providing significant energy savings. [PERSON] et al. (2018) analyzed the effects of the melting point and position of phase change materials (PCM), the quality and load of PCM, insulation materials, external temperature, and other conditions on product temperature and energy consumption and applied PCM in refrigeration equipment.