The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This st...The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically.The generated schedule directs the project to be completed with the shortest critical path,at the minimum cost,while maintaining its quality.There are several real-world business constraints related to human resources,the similarity of the tasks added to the optimization model,and the literature’s traditional rules.To support the decision-maker to evaluate different decision strategies,we use compromise programming to transform multiobjective optimization(MOP)into a single-objective problem.We designed a genetic algorithm scheme to solve the transformed problem.The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’fitness function.The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives.These are achieved through a combination of nonpreference and preference approaches.The experimental results show that the proposed method worked well on the tested dataset.展开更多
文章以BY公司新建的数字化车间中自动化立体仓库(automated storage and retrieval system,AS/RS)货位分配为研究对象,通过实际调研和咨询,针对BY公司立体仓库提出了较合理货位分配策略和优化原则,据此建立了多目标优化模型(multi-objec...文章以BY公司新建的数字化车间中自动化立体仓库(automated storage and retrieval system,AS/RS)货位分配为研究对象,通过实际调研和咨询,针对BY公司立体仓库提出了较合理货位分配策略和优化原则,据此建立了多目标优化模型(multi-objective optimization,MOP)。基于遗传算法进行算子设计,结合Sheffield遗传算法工具箱,用Matlab进行编程实现,并用实例求解计算得出货位分配方案。研究结果表明,无论是在提高货架稳定性、使货架横向受力均匀,还是提高出库效率方面都有了较大的改善,能满足公司安全生产和高效作业的要求。展开更多
为了提升一种先进的新型机载传感器——嵌入式大气数据传感器(flush air data sensing,FADS)的测量精度,以正态云模型和多目标规划(multi-objective programming,MOP)为出发点,在原有的“三点法”基础上提出一种新的改进方法。基于CFD...为了提升一种先进的新型机载传感器——嵌入式大气数据传感器(flush air data sensing,FADS)的测量精度,以正态云模型和多目标规划(multi-objective programming,MOP)为出发点,在原有的“三点法”基础上提出一种新的改进方法。基于CFD软件得到的数据库和亚音速及超音速情况下的空气动力学知识建立高精度FADS系统模型,利用正态云模型对测量信号的不确定性和随机性进行量化分析,在对系统冗余信号的融合过程中,基于多目标规划中的松弛变量法和拉格朗日乘子法提出一种新的计算客观权重方法。仿真结果表明,与传统方法相比,新提出的基于云模型和多目标规划的方法可将测量精度提升3.2%,测量数据的离散程度降低68.88%。展开更多
文摘The scheduling process that aims to assign tasks to members is a difficult job in project management.It plays a prerequisite role in determining the project’s quality and sometimes winning the bidding process.This study aims to propose an approach based on multi-objective combinatorial optimization to do this automatically.The generated schedule directs the project to be completed with the shortest critical path,at the minimum cost,while maintaining its quality.There are several real-world business constraints related to human resources,the similarity of the tasks added to the optimization model,and the literature’s traditional rules.To support the decision-maker to evaluate different decision strategies,we use compromise programming to transform multiobjective optimization(MOP)into a single-objective problem.We designed a genetic algorithm scheme to solve the transformed problem.The proposed method allows the incorporation of the model as a navigator for search agents in the optimal solution search process by transferring the objective function to the agents’fitness function.The optimizer can effectively find compromise solutions even if the user may or may not assign a priority to particular objectives.These are achieved through a combination of nonpreference and preference approaches.The experimental results show that the proposed method worked well on the tested dataset.
文摘为了提升一种先进的新型机载传感器——嵌入式大气数据传感器(flush air data sensing,FADS)的测量精度,以正态云模型和多目标规划(multi-objective programming,MOP)为出发点,在原有的“三点法”基础上提出一种新的改进方法。基于CFD软件得到的数据库和亚音速及超音速情况下的空气动力学知识建立高精度FADS系统模型,利用正态云模型对测量信号的不确定性和随机性进行量化分析,在对系统冗余信号的融合过程中,基于多目标规划中的松弛变量法和拉格朗日乘子法提出一种新的计算客观权重方法。仿真结果表明,与传统方法相比,新提出的基于云模型和多目标规划的方法可将测量精度提升3.2%,测量数据的离散程度降低68.88%。
