# The Development of a Cold-Chain-Packaging Risk Management Model Based on Fuzzy Bayesian Network
## 2 Literature Review
The ultimate goal of cold chains is to transport and store certain products while maintaining the product quality and safety. The quality and safety of the cold chain products are therefore of significant importance, and many risks can lead to quality and safety deteriorations, e.g., contamination with other substances, the presence of bacteria, mechanical damage, etc. This can lead to adverse health hazard events if quality and safety are not ensured [29; 32; 33; 34; 43; 44; 47; 48; 49; 50; 51; 52; 53]. [PERSON] et al. (2020) conducted a risk assessment for a medical provider and emphasized the importance of quality assurance and information on cold chains [35]. Another factor that may cause adverse effects on cold chains is the packaging- and processing-related risk. Packaging plays an important role along the whole cold chain, and temperature control and monitoring may be disrupted because of unsuitable packaging materials, incorrect processing and packing and damaged packaging. Also, sometimes the packaging design may lack sustainability considerations along the cold chain, leading to waste and contamination. Packaging is especially essential during loading and unloading since the products are not protected by refrigerators. Proper packages can keep the product temperature within desired ranges during between-links. In addition to selecting suitable packages, the handling of the packages also affects the cold chain performance. Insufficient training and instructions can lead to the incorrect handling of the packages, causing temperature disruptions. Unsafe packaging materials or a lack of sanitation may cause harm to personnel safety [1; 29; 32; 35; 37; 40; 41; 46; 48; 49]. To increase the sustainability of the cold chain, the application of new technologies and the use of energy-efficient refrigeration systems have become the new trend [1]. Many decision-making techniques have been selected for risk assessment in cold chain risk management studies, such as Bayesian Network, analytical hierarchical process (AHP), fuzzy inference system (FIS), etc. For studies concentrating on supply chain risks, the method of supply chain operation system (SCOR) is sometimes used so that the activities of the supply chain can be mapped before risk assessment, and house of risk (HOR) and pareto diagrams are often used to prioritize the risk factors. Bayesian Network is used in some fraud risk management studies on food cold chains [33; 44]. Some studies analyze the casual relationships and probabilities of the cold chain risk factors using the Bayesian Network method [36; 40; 46]. Other popular methods include AHP, ISM, TOPSIS, FIS, machine learning algorithms, etc. AHP is good for prioritizing risk factors or mitigation strategies, as well, and has pros of high flexibility and simplicity. [PERSON] et al. (2017), [PERSON] et al. (2021), [PERSON] et al. (2022) and [PERSON] et al. (2021) used AHP for their cold chain risk management models [2; 37; 41; 51]. [PERSON] and [PERSON] (2020) used interpretative structure modeling (ISM) for their risk control model, in which a hierarchical structure is built through adjacency and reachability matrixes [35]. Most methods require the ranking or defining of probabilities based on human judgement and historical data; however, sometimes people may not be very comfortable assigning numbers to probabilities of occurrence for events, and nor it is easy to obtain perfect data. Therefore, fuzzy logic reasoning is sometimes used in risk assessment to deal with system uncertainties. Many cold chain risk management studies have incorporated fuzzy logic into their risk assessment systems [17; 28; 31; 36; 37; 38; 40; 43; 47]. Experts' judgements are needed to decide which fuzzy membership shapes and rules are used. Trapezoidal and triangular membership functions are the most frequently used methods due to their simplicity. Rule-based fuzzy inference is often used to determine the correlation probabilities between parameters. [PERSON] et al.