Time-window-based Scheduling Strategy and Optimization for Maximizing the Income of Drop and Pull Transport

Abstract

Abstract As an intensive and efficient way of transportation, drop and pull transport has become the mainstream transportation organization of foreign developed countries and regions. Based on the freight demand vector and aging of a given initial state, this paper established a goal planning and scheduling model based on the maximization of time window revenue to get the most efficient deployment plan under the condition of high customer satisfaction. The genetic algorithm based on task urgency was used to solve this model to obtain the optimal allocation scheme of total income. Finally, the number of algorithm iterations was analyzed, which proved the effectiveness of the algorithm performance. Through the sensitivity analysis of the number of tractors, this paper gave suggestions for more efficient tractor configuration.