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Modelling freight logistics is challenging due to the variable consignments and diverse customers. Discrete-event Simulation (DES) is an approach that can model freight logistics and incorporate stochastic events. However, the flexible delivery routes of Pickup and Delivery (PUD) are still problematic to simulate. This research aims to develop last-mile delivery architecture in DES and evaluate the credibility of the model. A two-tier architecture was proposed and integrated with a DES model to simulate freight operations. The geographic foundation of the model was determined using Geographic Information Systems (GIS), including identifying customer locations, finding cluster centres, and implementing Travelling Salesman Problem (TSP) simulation. This complex model was simplified to the two-tier architecture with stochastic distances, which is more amenable to DES models. The model was validated with truck GPS data. The originality of the work is the development of a novel and simple methodology for developing a logistics model for highly variable last-mile delivery.

The Nash equilibrium method is studied in this paper, and a new motion prediction algorithm based on Nash equilibrium is proposed. This method includes 3 main contents: 1. Analyze and abstract the influencing factors of object motion. 2. Analyze the effects of each factor and list the Nash equilibrium equation. 3. Find the optimal solution of the system of equations. Use Nash equilibrium method to attain the purpose of overall considerate all influencing factors of motion prediction. This algorithm can solve the problem that the current motion prediction algorithm can’t take all the influence factors into account. To verify this method, we introduce this method to the ocean drift motion prediction and prove the accuracy of the method through three sets of comparison experiments.