# The Development of a Cold-Chain-Packaging Risk Management Model Based on Fuzzy Bayesian Network
###### Abstract
Many factors can affect the overall performance of the cold chain, e.g., product types, transport route, transportation modes, packaging used, etc. [12]. Due to the complex and perishable nature of the cold chain, safety and risk management is needed to manage highly unpredictable and uncertain events during transportation and storage. Bayesian Network (BN) is a novel method for accessing uncertainties and representing links between variables and has been used widely in the field of risk management. BN originates from statistics and artificial intelligence and aims to study the uncertainty in knowledge-based systems. BN has gained increasing research attention in academia in recent years. Unlike other static techniques like faulty tree or bow tie, BN is proven to be more robust and flexible and thus has many applications, e.g., risk modelling, engineering safety and reliability management, medical diagnosis, etc. [13]. The casual relationships and probability dependencies can be explained using BN. BN can take multiple forms of information, e.g., datasets and expert opinions, which makes it a good option for cold chain risk analysis because it is difficult to obtain perfect data for cold chains. BN also has the merit of being able to update the system with new information. This is particularly useful for complex systems, since interdependencies between risk factors have a significant impact on the overall performance, which can be solved by BN [14]. Both forward and backward inference can be achieved using BN models. Despite all of BN's advantages, it is still subject to some limitations. Only crisp numbers, or numerical probabilities, can be inputted into BN based models. Sometimes it is hard to obtain full data, and some people are not very comfortable with giving out numerical numbers for probabilities. Fuzzy logic reasoning can be used to address the high uncertainties of the system. Fuzzy logic theories are often used to deal with issues with high level of vagueness and have been used widely in decision analysis, classification, approximation, etc. Fuzzy number sets were first thought up in the 1960s by [PERSON] [15] and aim to represent the degree of truth. Due to the high uncertainties of cold chains, researchers and practitioners sometimes struggle to provide adequate information or numerical data. To ease the process, fuzzy logic approaches are sometimes used to convert linguistic terms into numbers, since linguistic expressions are more acceptable than numerical values. In this way, uncertain information can be incorporated in the risk assessment. Fuzzy logic methods often include fuzzification, inference and defuzzification. Through fuzzification, experts can use linguistic terms like \"high\" and \"low\" to determine how likely a particular risk factor is to occur or to lead to the occurrence of other risk factors. Rather than using linear expressions, fuzzy membership functions and fuzzy-based rules are often used for the inference of models. Membership functions are often used to describe the fuzziness of the number based on degree of truth. Common forms include triangular, trapezoidal, Gaussian membership functions, etc. Fuzzy inference has pros of providing more flexibility and levels of probabilities when assessing experts' opinions. Then, in the stage of defuzzification, fuzzy numbers are converted back to crisp values. Common defuzzification methods include bisector and centroid defuzzification [14]. Fuzzy logic approaches are also subject to some weaknesses. Fuzzy reasoning techniques lack the ability to conduct inference inversely, and this can be alleviated by the BN technique, providing reasoning inference. The combination of fuzzy logic and Bayesian Network can compensate for each method's limitations, and fuzzy Bayesian Network (FBN) can address the randomness and vagueness of uncertainty problems. Therefore, this paper aims to investigate and analyze risks on cold chain packaging by developing a risk management model based on FBN.