Abstract: Glowworm Swarm Optimization（GSO） is a new swarm intelligence optimization algorithm, but now it has few applications in the field of combinatorial optimization. GSO is presented to solve the cross dock scheduling problem in the paper. Cross dock scheduling problem is a kind of highly complex optimization problem, and it is the critical issue for the cross docking logistics. The two-stage Largest Order Value（LOV） based on random key encoding method is designed for the solution. In order to enhance accuracy and convergence rate of the GSO, the best acceptance and moving by dimensional strategies are proposed. Based on the principle of social psychology, the position update equation is improved. Simulation results show that the proposed GSO algorithm is efficient for the cross dock scheduling problem.

Key words: glowworm swarm optimization, cross dock scheduling problem, logistics

摘要： 萤火虫群优化算法是一种新兴的群体智能优化算法，目前在组合优化领域中的应用比较少。提出萤火虫群优化算法（Glowworm Swarm Optimization，GSO）求解越库调度问题的优化方法。越库调度问题是一类极为复杂的NP难题，是影响越库配送效率的关键问题。依据算法和问题特点，设计基于随机键的两段式最大顺序值编码方法。为了解决GSO算法优化精度低、收敛速度慢等问题，提出逐维移动，贪婪接受的搜索策略。基于社会心理学原理，对位置更新公式进行改进。通过实验仿真，结果表明改进的GSO算法是求解越库调度问题的有效方法。

关键词: 萤火虫群优化算法, 越库调度, 物流