测试性增长中资源优化配置模型及求解
发布时间：2018-10-21 17:13
【摘要】：良好的测试性设计对系统维修性具有重要意义,测试性增长试验通过一系列测试性设计缺陷发现和纠正措施,可保证系统测试性指标达到设计要求。针对基于延缓纠正的测试性增长过程中的资源配置问题进行研究,基于增长试验目标是否明确和试验资源是否受限制问题构建资源优化配置模型,并提出一种基于拉格朗日松弛和本地搜索的快速优化算法。仿真结果表明:该模型能够有效指导测试性增长中的资源优化配置问题,所提混合优化方法能够高效、准确地求解整数规划问题。
[Abstract]:Good testability design is very important to the maintainability of the system. Through a series of testability design defects detection and corrective measures, testability growth test can ensure that the testability index of the system can meet the design requirements. The problem of resource allocation in the process of experimental growth based on delayed correction is studied. The optimal allocation model of resources is constructed based on whether the objectives of the growth experiment are clear and whether the experimental resources are restricted. A fast optimization algorithm based on Lagrange relaxation and local search is proposed. Simulation results show that the proposed model can effectively guide the optimal allocation of resources in testability growth, and the proposed hybrid optimization method can efficiently and accurately solve the integer programming problem.
【作者单位】： 国防科技大学机电工程与自动化学院;国防科技大学装备综合保障技术重点实验室;
【基金】：国家自然科学基金资助项目(51175502)
【分类号】：TH17
本文编号：2285799
[Abstract]:Good testability design is very important to the maintainability of the system. Through a series of testability design defects detection and corrective measures, testability growth test can ensure that the testability index of the system can meet the design requirements. The problem of resource allocation in the process of experimental growth based on delayed correction is studied. The optimal allocation model of resources is constructed based on whether the objectives of the growth experiment are clear and whether the experimental resources are restricted. A fast optimization algorithm based on Lagrange relaxation and local search is proposed. Simulation results show that the proposed model can effectively guide the optimal allocation of resources in testability growth, and the proposed hybrid optimization method can efficiently and accurately solve the integer programming problem.
【作者单位】： 国防科技大学机电工程与自动化学院;国防科技大学装备综合保障技术重点实验室;
【基金】：国家自然科学基金资助项目(51175502)
【分类号】：TH17
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