Pareto-optimality solution recommendation using a multi-objective artificial wolf-pack algorithm

In practical applications, multi-objective optimisation is one of the most challenging problems that engineers face. For this, Pareto-optimality is the most widely adopted concept, which is a set of optimal trade-offs between conflicting objectives without committing to a recommendation for decision-making. In this paper, a fast approach to Pareto-optimal solution recommendation is developed. It recommends an optimal ranking for decision-makers using a Pareto reliability index. Further, a mean average precision and a mean standard deviation are utilised to gauge the trend of the evolutionary process. A multi-objective artificial wolf-pack algorithm is thus developed to handle the multi-objective problem using a non-dominated sorting method (MAWNS). This is tested in a case study, where the MAWNS is employed as an optimiser for a widely adopted standard test problem, ZDT6. The results show that the proposed method works valuably for the multi-objective optimisations

考虑产品切换的客车混流装配线排序问题

针对主客观因素交互影响下混合装配线的排序问题,建立以最小化工作站堵塞时间与最小化产品切换次数为目标的交互排序模型,并依据客车在实际加工过程中的传统排序方案与模型所得排序方案的结果进行对比分析。模型构建从客观因素出发,优化堵塞时间即“重构”作业框架,以达到对负荷高峰时期进行削峰处理的目的,并进一步在主观因素层面上,考虑线上操作者的作业惯性,降低线上操作者对频繁切换产品的出错率以及保持较高熟练度时操作的方便与流畅性。引入主客观优化评价算法对模型进行计算,决策出最优任务调度方案。对比分析结果表明,模型获得的最优排序改善了装配线的生产堵塞时间,同时兼顾在主观因素主导下的产品切换频率问题,对于节省装配过程中有限的时间资源以及释放有限的空间资源起到很大作用。福建省高校产学合作项目(2017H6020);;福建省科技重大专项(2016HZ0001-9