Adaptive Loading Plan Decision Based upon Limited Transport Capacity

Cargo distribution is one of most critical issues for steel logistics industry, whose core task is to determine cargo loading plan for each truck. Due to cargos far outnumber available transport capacity in steel logistics industry, traditional policies treat all cargos equally and distribute them to each arrived trucks with the aim of maximizing the load for each truck. However, they ignore timely delivering high-priority cargos, which causes a great loss to the profit of the steel enterprise. In this paper, we first bring forward a data-driven cargo loading plan decision framework based on the target of high-priority cargo delivery maximization, called as ALPD. To be specific, through analyzing historical steel logistics data, some significant limiting rules related to loading plan decision process are extracted. Then a two-step online decision mechanism is designed to achieve optimal cargo loading plan decision in each time period. It consists of genetic algorithm-based loading plan generation and breadth first traversal-based loading plan path searching. Furthermore, adaptive time window based solution is introduced to address the issue of low decision efficiency brought by uneven distribution of number of arrived trucks within different time periods. Extensive experimental results on real steel logistics data generated from Rizhao Steel’s logistics platform validate the effectiveness and practicality of our proposal.