基于混合果蝇优化算法的选址-库存-配送集成优化研究

本文面向企业运营管理实践，构建了一种基于联合补货策略的选址-库存-配送集成优化新模型。作为典型的NP-hard问题，传统算法难以高效稳定地求解，故本文设计了一种新的混合果蝇优化算法(Fruit Fly Optimization Algorithm, FOA),通过引入进化算法的信息交换、变异、选择操作来增强算法局部寻优能力，采取概率性飞行策略来平衡算法的全局寻优与局部寻优。算例结果表明，新混合FOA算法的准确性和稳定性较标准FOA有了明显的改善，与差分进化、自适应混合差分进化、粒子群优化相比也具有比较优势。

Integrated Optimization of Location-Inventory-Delivery Problem Using Hybrid Fruit Fly Optimization Algorithm

An integrated location-inventory-delivery optimization model using joint replenishment policy is proposed for practical operations management. However, the traditional solutions cannot solve this typical NP-hard problem efficiently and effectively. Therefore, a new hybrid fruit fly optimization algorithm is designed to deal with it. Firstly, the new algorithm introduces the information exchange, mutation and selection of evolutionary algorithm to enhance the local search ability. Secondly, a probability osphresis operation is adopted to balance the global search and local search. Numerical experiments results reveal the accuracy and robust of new algorithm improved observably. Compared to differential evolution, adaptive hybrid differential evolution and particle swarm optimization, it still has the comparative advantage.