基于交叉效率排序多目标进化算法的备件供应优化

Spare Parts Supply Optimization and Decision-making Based on Cross-efficiency Multi-objective Sorting Evolutionary Algorithm

为了对备件供应网络进行优化并制定最优供应方案,以缩短总供应时间、减少供应成本和降低中断风险为目标,以备件满足度、库存容量等为约束建立了多目标优化模型。基于交叉效率排序多目标进化算法求得模型的非支配解集,同时决策出最优解。优化过程中采用改进数据包络分析计算各最优解的二次目标交叉效率,指导算法朝最优效率个体收敛,对求得的非支配解进行排序从而选择出最优方案。算例表明:通过交叉效率排序多目标进化算法优化得到了13个互不支配的备件供应方案,且确定了交叉效率为0.9278的方案为最优方案;新算法优于未采用排序和采用自评效率排序的多目标进化算法。

A multi-objective optimization model is established with the constraints of spare parts satisfaction,lead time and inventory capacity for the shortest total supply time,the lowest risk and the minimum cost.The proposed model is used to optimize the spare parts supply and make an optimal supply scheme.The set of non-dominant solutions are obtained by using the proposed secondary goal cross-efficiency sort multi-objective evolutionary algorithm(SGCES-MOEA).During optimization process,the improved data envelopment analysis(DEA)is used to calculate the cross-efficiencies of the non-dominant solutions.On the one hand,the algorithm is guided to converge to the optimal efficiency individuals.On the other hand,the non-dominant solutions are sorted to select an optimal one.The example shows that 13 non-dominate spare parts supply schemes are obtained by using SGCES-MOEA algorithm,and the scheme with cross-efficiency of 0.9278 is determined as the optimal scheme.The new algorithm is superior to the multi-objective evolutionary algorithm without efficiency sorting strategy and that with self-evaluation efficiency sorting strategy.