Literature Review on Risk and Risk Management of Logistics Projects
2.2 Risk Management in Logistics Projects
2.2.3 Models of Risk Assessment
According to the argument of Bernard et al. (2018), Michael et al. (2018) and Song
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(2003), a mathematical model is a structure that abstracts and simplifies the actual prototype for a certain purpose. It is an essential description of the simplification of the actual prototype using for example mathematical symbols, mathematical expressions and quantitative relationships. It uses a formal language to express the basic characteristics of processes. The idea of a mathematical model is to describe and solve the problem through mathematical language and tools. It uses the concept of mathematics and applies it in the real world. Mathematical models are widely used in risk assessment and they can be classified from different perspectives for use in risk assessment as follows (Bernard et al., 2018; Chen et al., 2013; Dufour et al., 2017;
Richey et al., 2010; Song, 2003).(1)Classified by function. A risk assessment model is a mathematical expression that represents the relationship among the numbers of variables in an event. According to function, mathematical models can be divided into quantitative and qualitative models. Quantitative assessment methods include risk mapping, network models, fuzzy comprehensive evaluation and some economic evaluation models in risk assessment. A qualitative risk assessment model is a graph, symbol or language that represents the relationship among objects, including subjective scoring methods, analytical hierarchical processes and decision trees, diagnostic charts, and other methods (Carter et al., 2015; Chen et al., 2013; Dufour et al., 2017). In this thesis, two types of methods are used together to assess the risk of large-scale logistics projects. (2) Classified by the characteristics of problems. Depending on the nature of the problem being studied, a risk assessment model can be divided into different types of deterministic and random (or uncertain), dynamic and static, continuous and discrete (Mena et al., 2013; Xu et al., 2018). For project economic risk analysis involving many uncertain factors, the Monte Carlo method and fuzzy evaluation model can be used, which can solve the risk assessment brought by uncertainty. In addition, dynamic models often study the transition from one state to another. The static model only describes the relationship between two stable states, does not care about the transition from one state to another and can usually be described by algebraic equations. Although dynamic models are more complex and difficult than static models, they often reveal the nature and influence of static models. A continuous model refers to the temporal or spatial distribution without interrupting processes. However, for continuous calculations or
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actual needs, continuous variables can be discretised, such as using economic risk analysis to calculate intervals, where continuous and discrete variables can usually be converted. (3)Classified by programming level. Models that are processed by computer programs can be divided into programmatic and nonprogrammatic models (Narayanan et al., 2015). A programmatic model is used based on their certain program, such as a Monte Carlo model, neural network model, and so on. Meanwhile, a nonprogrammatic model does not indicate that no program can be followed but only depends on general programs (Hartmann and de Grahl, 2011; Zacharia et al., 2009). On the basis of the actual needs of man-machine dialogue, not only databases and libraries exist but also knowledge bases (Chen et al., 2013; Dufour et al., 2017; Xu et al., 2018).
Nonprogramming models are primarily used for high-level policy decisions that are often combined with intuitive judgment and exploratory problem-solving techniques.
Numerous nonprogramming models are required in artificial intelligence systems and expert decision systems (Xu et al., 2018).
We then briefly introduce neural network model here, which could be considered as the future study for this thesis. Note that there are many successful neural network models and algorithms, but the most commonly used ones are the former artificial neural network model and the BP network model. The former artificial neural network model has good function approximation ability, which can well reflect the complex non-linear relationship between the input and output of the object by learning the training samples.
The former artificial neural network is divided into input layer, hidden layer and output layer. All layers are connected by layers, and there is no interconnection between units on the same layer. The learning process of BP network model consists of forward transmission and error reverse transmission, which embodies the essence of artificial neural network. Due to its good self-learning and self-associative functions, BP network model has become the most widely used artificial neural network. BP network can approximate any continuous function with arbitrary precision, so it is widely used in nonlinear modeling, function approximation and pattern classification (Zhou, 2009).
Besides the above discussions, there are a large number of models that can be used to assess the risk in the logistics projects, such as the Fuzzy Comprehensive Measurement
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Method (FCMM), AHP and PSO-AHP methods. For example, Giaglis (2004) discussed the operational risk of a logistics park construction project by using the AHP, and divided it into five categories of risk: market competition, economic change, technological development, nature and management. Barkuizen et al. (2011) used the fuzzy method in their systems approach to risk management. Chapter 3 will summarise the commonly used risk assessment models from quantitative methods and demonstrates the advantages of each selected model in more detail. Note that risk evaluation is a critical decision for logistics projects in today’s competitive environment. Regardless how the project plan is properly decided, risk is still one of the the main issues people need to consider. Risk assessment often presents a complex structure composed of tangible and intangible factors. Hence, FCMM is a convenient way to solve the problem of risk analysis. In this thesis, the risk evaluation of the logistics projects will also be addressed according to FCMM. PSO is the new method used in a novel and integrated manner in this thesis. To overcome some limitations of the existing research methods, such as FCMM, this thesis proposes and tests to some extent a logistic alliance risk identification and analysis method based on PSO algorithm and penalty function method. The two methods are compared in appropriate detail to analyse and calculate the optimisation problem in Chapter 5, which assist in a novel manner to make the result of logistics project risk assessment more accurate and realistic.
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Figure 2- 4 Risk Management in Logistics Projects