一种基于混合优化算法的物流园车辆取货调度方法
By combining discrete particle swarm optimization and variable neighborhood search algorithms to optimize vehicle pickup scheduling in logistics parks, the problems of lag and local optima in existing methods are solved, achieving efficient vehicle scheduling and meeting the high throughput requirements of logistics parks.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- FUJIAN SANGANG MINGUANG
- Filing Date
- 2023-02-28
- Publication Date
- 2026-07-17
AI Technical Summary
Existing vehicle pickup and dispatching methods in logistics parks are outdated and cannot meet the demands of high throughput. Furthermore, traditional metaheuristic algorithms rely on experience and cannot guarantee a globally optimal solution, leading to scheduling chaos and low customer satisfaction.
A hybrid optimization algorithm approach is adopted, which combines discrete particle swarm optimization with variable neighborhood search algorithm. The initial population is generated through K-means clustering, global search, local search and random search, and the scheduling scheme is optimized by chromosome crossover, mutation and variable neighborhood search.
It effectively avoids the problem of algorithms getting trapped in local optima and initial solution dependency, improves the global and local search capabilities of scheduling, significantly reduces vehicle waiting time, and improves logistics throughput and yard work efficiency.
Smart Images
Figure HDA0004100285320000011 
Figure HDA0004100285320000021 
Figure HDA0004100285320000031