Automatic Task Matching and Negotiated Vehicle Scheduling for Intelligent Logistics System

Abstract

The decision-making of logistics vehicle scheduling is difficult under varying constraints, multiple disturbances and strong time-variation. The multi-agent system (MAS) is a new approach to investigate the real-time decision-making of logistics vehicle scheduling. It satisfies the various requirements of the logistics system, such as the geographical distribution of vehicles, the dynamic changes of information, and the constant changes in consumer orders. In view of the theoretical and practical significance of the MAS, this paper explores the decision-making of logistics vehicle scheduling based on the MAS, and relies on two-level planning modelling method to construct the mathematical model of outsourcing-based container port vehicle scheduling problem. Then, an effectively exchange neighbourhood tabu search algorithm was designed to solve the model. Through the research, it is concluded that the proposed hierarchical decomposition method of logistics distribution task can reduce the overall scheduling difficulty and reduce the actual planning error effectively; the established MAS-based intelligent logistics scheduling model can minimize the total distribution cost through continuous adjustment of resources according to the distribution task. Finally, the feasibility of the proposed algorithm was verified by the results of a calculation example.