不确定需求下容量限制工厂选址的改进蜂群算法

研究了需求不确定下容量限制工厂选址问题。在考虑需求点的实际情况后，根据各点不确定需求的变化而改变工厂的生产规模限制，建立设置分段的生产规模的容量限制工厂选址模型。使用联系数和区间灰数理论对不确定的需求进行预测。为了求解该问题，对传统人工蜂群算法的编码、更新、搜索和追随策略进行了改进。最后进行数值实验，将新算法与遗传算法、萤火虫算法、粒子群算法和海鸥算法进行对比。结果表明，改进后的人工蜂群算法有更好的优化效果，在求解容量限制的选址问题上具有可行性和有效性。

Improved bee colony algorithm for capacitated facility location problem under uncertain demand

The problem of the site selection of capacitated facility under uncertain demand was studied. After considering the actual situation of the demand point, the production scale limit of the factory was varied according to the uncertain demand changes at each point. A new capacitated facilities location model with segmented production scale was established. Uncertain demand was predicted using the number of connections and grey number of intervals theory. In order to solve this problem, this article improved the coding, updating, searching and following strategies of the traditional artificial bee colony algorithm. Finally, numerical experiments were performed to compare the new algorithm with genetic algorithm, firefly algorithm, particle swarm optimization and seagull optimization algorithm. The results show that the improved artificial bee colony algorithm has a better optimization effect, and is feasible and effective in solving the location problem of capacity constraints.