基于超启发式算法的备件供应网络结构优化

为了通过对供应网络结构进行优化从而提高备件供应的效率和效益,分别对传统正向供应网络、应急横向供应网络以及考虑抢修任务的闭环供应网络3种备件供应网络结构进行研究。以供应成本最小和供应时间最短为目标,以备件满足度、库存等为约束,构建了带约束的多目标优化模型。提出了一种基于排序选择函数的超启发式多目标进化算法,同时可以对不同网络结构模型进行求解。在ZDT系列测试函数上将该算法与其他进化算法进行对比测试,验证了所提出的超启发式算法在收敛性和分布性上的优越性。算例表明,一方面,与传统前向供应网络相比,横向和闭环供应网络能够提高备件供应的时效性和经济性;另一方面,超启发式算法在求解模型时取得的解优于其他元启发式算法。

Structure optimization of spare parts supply network based on hyper heuristic algorithm

In order to improve the efficiency and effectiveness of spare parts supply by optimizing the structure of supply network, three kinds of network structures of spare parts supply are studied, which are the traditional forward supply network, emergency lateral supply network and closed-loop supply network considering maintenance. A multi-objective optimization model with the objectives of the minimum supply cost and the shortest supply time and the constraints of satisfaction rate and inventory is proposed. A hyper-heuristic multi-objective evolutionary algorithm based on the ordering choice function is proposed to solve models in different network structures. By comparing the proposed algorithm with other evolutionary algorithms on the ZDT benchmarks, the superiority of the proposed hyper heuristic algorithm in convergence and distribution is verified. The numerical example shows that, on the one hand, compared with the traditional forward supply network, the lateral and closed-loop supply network can improve the timeliness and economy of spare parts supply. On the other hand, the hyper heuristic algorithm is superior to other meta heuristic algorithms in solving the models.