目的 提高电子标签拣选系统中拣选作业的效率与货位占有率。方法 以某电子拣选库为研究对象，提出以订单完成度、货位占有率以及货位聚集度为目标的拣选优化模型。设计基于二进制粒子群算法(BPSO)和遗传算法(GA)的模型求解仿生算法。结果 试验及优化结果表明，基于BPSO的电子拣选库订单的完成度、货位占有率以及货位聚集度较遗传算法更高。结论 基于二进制粒子群算法求解的优化模型较符合实际的电子拣选库人工拣选作业，同时仓储作业货位的利用率及拣选效率得到了提高。

The work aims to improve the picking efficiency and cargo space occupancy under the pick-to-light system (PTL). With certain PTL as the object of study, a picking optimization model aiming at the order completion, cargo space occupancy and concentration ratio was proposed. A bionic algorithm of model solution based on binary particle swarm optimization (BPSO) and genetic algorithm (GA) was designed. According to the test and optimization results, the order completion, cargo space occupancy and concentration ratio of the PTL based on BPSO were higher than those of the GA. In conclusion, the optimization model that gets the solution based on BPSO is more suitable for the actual PTL-based manual picking operation, and the utilization and picking efficiency of cargo space regarding warehousing operations have been improved.