Decision support system considering risks in combined transport : with a case study of risk management in railway transport
Combined transport (CT) is characterised by highly environment-friendly traffic modes and minimal congestion. However, in the context of the reality of logistics, operators are not in favour of CT because of its high complexity. Therefore, finding a well-structured solution to quickly solve problems in CT is difficult, especially in the operational level. In other words, less-structured problems, whose solutions are not available in a short time, are common for CT participants.</br> Many reasons contribute to the less-structured problems. The dissertation focuses on the analysis of risks in CT that result in the inefficiency of processes and non-value-adding activities. Forecasting of risks and estimating the influence is an effective tool to enhance the service of CT. However, predicting risks alone is insufficient in enhancing the effectiveness of the decision making of CT. In order to explain the principles of the decision-maker support system in CT as a whole, this dissertation presents a decision support system (DSS) firstly. DSS is a tool for decision-makers to implement their own analysis of the less-structured problems and accelerate the process of decision-making.</br> As a fundamental component that supports decision making in CT, risk prediction of train transport is explained with a case study in the dissertation. Punctuality is a key performance indicator of train freight transport. In this dissertation, a prediction model is established on the base of artificial neural network (ANN). Due the endogen drawback of ANN, Genetic Algorithm is adopted to improve the performance of ANN. Consequently, an experiment is design to train and test the ANN-based model by a set of data from the practice. This algorithm simplifies the process of decision-making by imitating human decision-making behaviour. From the viewpoint of risk management, the ANN-based model provides a solution for the less-structured problems in CT. DSS is demonstrated as well as an effective tool for decision-maker in CT.