Research On Process Mining In Port Logistics And Intelligent Design For The Process Improvement

ABSTRACT:This dissertation sets up a comprehensive framework for the methodology of applying process mining in port logistics in support of the port smart logisitcs. The cutting-edge techniques concering process knowledge discovery are integrated with managerial problems in practice dealing with smart logistics by a combination approach using theories and methods from multiple disciplines including smart logistics, port logistics, business process management, workflow modeling, process mining and data mining. The methodology consists of three main parts including port logistics process modeling, port logistics control flow analysis and port logistics process diagnosis. On this basis, an integrative method for the intelligent design of port logistics processes is proposed combining the IoT sensing techniques, the process knowledge discovery techniques, and the logistics simulation techniques.Firstly, a research review of logistics intelligence, knowledge management, data mining, business process management and process mining is carried out. This makes the basis for proposing the concept framework of the port logistics process knowledge, composing of logistics control flow knowledge, data flow knowledge and logistics risk knowledge. The paper points out that the aim for smart logistics is to reduce the large amount of uncertainties and risks in the logistics processes caused by human-centric activities. The limitations of current logistics process analysis method are then analyzed with respect to the smart logistics development. This highlights the necessity and significance of discovering hidden knowledge in the port logistics processes for support of smart logistics.Secondly, the paper divides the port logistics processes into two parts as loosely-structured and highly-structured, based on the concept and characteristics of port logistics processes analysis. The limitations of imperative workflow modeling for the processes requiring high flexibility are analyzed, and an approach integrating the declarative and the imperative workflow modeling method is thereby presented for the port logistics process modeling.Thirdly, fuzzy mining technique is applied to the process event log to reveal the main flow of the port logistics processes. Using port logistics domain information, the complex event log can be regrouped into several groups. This makes the basis for the detailed control-flow analysis. A comprehensive methodology for the port logistics process control flow analysis is accordingly presented, including event log extraction, pre-processing, main flow exploration and sub process division, instance regrouping, and control flow discovery. A case study is carried out using real data set from an important Chinese port. The result proves the effectiveness of the method in improving the accuracy and reducing the complexity of the model. Consequently, the control-flow analysis is able to provide effective decision support for realizing the smart port logistics.Fourthly, the paper investigates the method for discovering the data flow knowledge and risk knowledge in port logistics processes. The methodology for the port logistics process performance analysis and risk diagnosis is then presented accordingly. By making use of domain information, the trace clustering method is improved and applied for regrouping the cases according to the process behavior. An instance profile generation algorithm is proposed to make the basis for further analysis of the relation between the process behavior and the performance. Data mining techniques are then applied for discovering the knowledge concerning the relationship between the port logistics elements, the process behaviors and the process performance. In addition, a quantitative method is proposed for the port logistics process risk analysis by applying the conformance checking technique. Through’replaying’the event log which records the’real’process behavior in the workflow model which describes the’ideal’behavior, the deviation degree and the activities involved can be revealed. This is a novel approach for risk analysis of port logistics processes.Finally, the problems within the port logistics processes are summarized through the process mining results. The IoT techniques are applied for the improvement of port logistics processes, supporting the real time monitoring of the cargo status information throughout the logistics supply chain. What’s more, an integrative method for the port logistics process improvement is proposed, combining the IoT techniques, the process knowledge discovery techniques, and the data mining techniques.