大型仓储拣货路径优化算法研究

Research on Optimization of RFID-Based Large Warehouse Picking Path

基于RFID的大型仓储,具有仓储规模大、货物精确定位且信息实时反馈,快速无纸化订单传送等特点,使拣货路径优化难度加大,传统的拣货模式无法满足要求。对此,结合蚁群和粒子群算法的优点,给出了蚁群-粒子群优化算法在基于RFID的大型仓储拣货路劲优化中的应用,使"蚂蚁"具有"粒子性",改进粒子群算法中初始解的选取,蚁群算法中信息素的更新方式,提高了"蚂蚁"和"粒子"的学习能力,避免单个粒子过早收敛和陷入局部最优解的问题。仿真结果表明,算法收敛速度快,寻优能力强,适用于基于RFID大型仓储拣货路径优化。

A large warehousing system based on RFID, is characterized by large scale, precise positioning of car- go, real-time information, fast paperless in the order transmittal and so on. So the optimization of order-picking rou- ting problem is difficult, the traditional order-picking patterns can not meet with the requirements. By integrating the advantages of both ant colony algorithm and particle swarm optimization, the hybrid algorithm based on ant colony al- gorithms and particle swarm optimization was put forward to design a large warehousing system based on RFID prob- lem. An improved particle swarm optimization algorithm was used to select initial solution and pheromone concentra- tion update. The learning ability of ＂ants＂ and ＂particle＂ was improved to avoid the problem of single particle prema- ture convergence and local optimal solution. The simulation results show that this algorithm converges fast, had has high precision, and is suitable for RFID large warehouse picking path optimization.