Prediction of vehicle-cargo matching probability based on dynamic Bayesian network

Abstract

ABSTRACT Matching status (failure or success) between logistics vehicles and cargoes during transport operation influences the decisions of the owners of vehicles and cargoes on scheduling. It is therefore essential to predict the specific matching status probability of vehicle-cargo matching (VCM). This paper defines the VCM probability, formulates the VCM probability problem, and proposes a method to predict this probability based on Bayesian network. The business (vehicle and goods resources) distribution, VCM degree, and business priority are introduced. By mapping business distribution and matching results to network nodes and matching degree as conditional probability, static and dynamic Bayesian networks for single-time sequence and multi-time sequence VCM probability prediction are constructed. A recursive algorithm is also developed to efficiently solve the dynamic Bayesian network model. The results of a prediction example demonstrate that the proposed method and model are valid and efficient. The model shows that VCM probability increases with time, but there is no obvious rule and uncertainty exists, whereas the increase rate gradually decreases with time. Further, adjusting business distribution and priorities can change the VCM probability. The proposed method provides support for the logistics information platform for scheduling decision and controlling strategy and logistics resource selection services.