基于可用度的预防维修和均值控制图联合优化模型

针对高可用度要求的生产系统(即可修复系统,以下简称系统),提出一种以单位时间成本为约束,可用度最大化为目标的预防维修和均值控制图联合优化模型。首先,考虑到系统运行存在受控、失控两种状态以及均值控制图两类错误(漏报警和误报警)发生的概率,在完美维修假设前提下,构建了4种系统运行(更新)情景(S1~S4)。然后,建立了每种更新情景的可用度和经济模型,并在此基础上建立了系统的可用度目标函数和经济约束函数,以实现单位时间成本约束条件下预防维修和均值控制图联合优化的系统可用度最大化目标。针对所建立的联合优化模型,通过实例对比验证了模型的有效性,并利用遗传算法对决策变量进行优化,实例优化结果表明,该模型能够有效提高系统可用度,并能降低系统成本。最后,通过正交试验、回归分析等方法对模型进行了参数的灵敏度分析。     

多工序质量检验计划的多目标优化蚁群算法研究

针对常见的串行多工序抽样检验方式，建立了工序间质量水平传递模型和质量检验成本模型，提出基于Pareto解评价的多目标优化蚁群算法；通过定义多目标解与理想解的相对距离为蚁群算法的启发函数，激励蚁群搜索可行解空间并发现最优解集；应用多目标优化蚁群算法解决质量检验计划优化问题取得了较好效果。     

以可靠性为中心的维修成本优化模型及其应用

分析各种维修方法的特点,剖析以可靠性为中心的维修(RCM)与其它维修方法之间的内在联系,给出设备维修评价指标体系。以广义维修成本最低为目标,以设备可用度为约束条件,以维修周期为优化变量,提出了以可靠性为中心的设备维修成本优化模型。采用蒙特卡洛仿真和遗传算法进行模型求解,实现维修周期和维修成本的优化,以一液压-机械复合传动系统为例,验证方法的有效性。     

考虑可靠度和可用度的民机维修间隔优化研究

针对民机维修间隔优化的问题,运用可靠性分析和可用性分析的方法确定最优维修间隔.对于符合三参数威布尔分布的民机部件,提出采用改进的极大似然估计法求解分布参数估计值;并以可靠度阈值为约束条件,可用度最大为目标,建立了预防性维修间隔优化模型.利用遗传算法进行求解,制定出最优维修间隔.通过实例分析,讨论了部件在不同维修水平下的...     

整星隔振系统优化设计研究

整星隔振系统由若干个隔振器组成,均匀布置在适配器和星箭界面之间,能有效减小发射阶段卫星所承受的环境载荷,对提高卫星发射的可靠性和降低发射成本具有重要意义,是近十年来航天界发展的一项新兴技术。本文利用遗传算法对整星隔振系统进行动力优化,合理选择隔振器的刚度和阻尼,有效提高整星隔振系统的横向和纵向隔振性能,为整星隔振系统的设计提供了一种可靠、有效和易用的优化方法。     

基于劣化理论的寿命周期可靠性和性能并行预计

劣化理论采用称为相邻预修周期和大修周期等劣化率的两个参量,揭示了在一定使用、维修条件下寿命周期可靠性和性能的劣化规律.根据劣化理论,发展了大型机械产品寿命周期使用可靠性和性能的并行预计方法.方法包含了最大化寿命周期可达可用度和收益率的思想,借助同类产品的使用观察,可应用于设计阶段产品有效寿命及寿命周期费用和性能参量估计,设计可靠性指标、最佳预修和大修时机确定,以及使用可靠性和性能模拟.     

单纯形免疫算法及其在高维非凸函数优化中的应用

许多工程问题都可以归结为优化问题,而且其目标函数往往是局部最优点的复杂高维非凸函数,传统单一算法一般难以求得全局最优解。在深入分析免疫算法和单纯形法的基础上,将两种算法有机结合,提出单纯形免疫算法。免疫记忆、抽取疫苗、接种疫苗和自适应等免疫机制的引入有助于优良个体和基因的保留和利用,提高算法收敛性;通过反射、扩展、内缩、缩边等操作来改良劣解而调整邻域结构,不断逼近最优解。典型函数优化Benchmark问题的仿真试验表明,提出的单纯形免疫算法比单一算法性能更优,适合于存在许多局部最优点的复杂高维非凸函数优化。     

基于动态差分元胞多目标遗传算法的混合作业车间布局改善与优化

针对一类离散作业、流水作业和特殊作业等多种作业单元共存的混合制造模式,提出了作业车间布局改善问题。以物料搬运费用最小、单元移动费用最小、作业单元包络矩形面积最小及非物流关系最大为目标,明确布局约束条件,构建车间布局多目标优化模型。在差分元胞多目标遗传算法的基础上,设计并引入动态变异策略以改善算法的全局搜索能力,提出用于解决布局模型的动态差分元胞多目标遗传算法,通过实例计算与结果分析验证了模型及算法的有效性。     

基于维修时间窗的柔性作业车间调度优化研究

针对柔性作业车间调度和预防性维护的单目标集成优化问题,以最大完工时间为优化指标,建立了基于维修时间窗的集成优化模型,设计了混合“教与学”优化（HTLBO）算法求解该模型。提出一种“基于工序加工时间最短”的机器序列初始化策略,对部分初始种群进行初始优化,以提高部分初始解的质量,使得算法能够以较短的时间收敛。对文献中柔性作业车间调度的基准问题进行求解并比较其计算结果,初步证明该混合算法的可行性;针对集成维修时间窗的柔性作业车间调度优化模型,借鉴文献中的数据生成实例进行求解,并与其他算法进行比较,证明该混合算法的有效性。     

Optimum design of short journal bearings by enhanced artificial life optimization algorithm

This paper presents an optimum design of high-speed short journal bearing using an enhanced artificial life algorithm (EALA) to compute the solutions of optimization problem. The proposed hybrid EALA algorithm is a synthesis of an artificial life algorithm (ALA) and the random tabu search method (R-tabu method) to solve some demerits of the ALA. The emergence is the most important feature of the artificial life which is the result of dynamic interaction among the individuals consisting of the system and is not found in an individual. The artificial life optimization algorithm is a stochastic searching algorithm using the feature of artificial life. The feature of R-tabu method, which prevents converging to the local minimum, is combined with the ALA. One of the features of the R-tabu method is to divide any given searching region into several sub-steps. As the result of the combination of the two methods, the EALA not only converges faster than the ALA, but also can lead to a more accurate solution. In addition, this algorithm can also find all global optimum solutions. We applied the hybrid algorithm to the optimum design of a short journal bearing. The optimized results were compared with those of ALA and successive quadratic programming, and identified the reliability and usefulness of the hybrid algorithm.     

A novel particle swarm optimization approach for product design and manufacturing

This paper presents a novel optimization approach that is a new hybrid optimization approach based on the particle swarm optimization algorithm and receptor editing property of immune system. The aim of the present research is to develop a new optimization approach and then to apply it in the solution of optimization problems in both the design and manufacturing areas. A single-objective test problem, tension spring problem, pressure vessel design optimization problem taken from the literature and two case studies for multi-pass turning operations are solved by the proposed new hybrid approach to evaluate performance of the approach. The results obtained by the proposed approach for the case studies are compared with a hybrid genetic algorithm, scatter search algorithm, genetic algorithm, and integration of simulated annealing and Hooke-Jeeves pattern search.     

A hybridized approach to optimal tolerance synthesis of clutch assembly

Specifying proper tolerances for manufactured goods results in greater savings and improved performance, which may ultimately determine whether a product succeeds or fails in the marketplace. In the past, tolerance specification has been more an art than a science, and is largely dependent upon experiences. A more scientific and reliable approach is presented in this paper. A hybrid of Nelder-Mead simplex method and particle swarm optimization (NM-PSO) is introduced for the design of tolerance of the machine elements of an overrunning clutch assembly. The objective is to obtain tolerances of the individual components so that the cost of manufacturing and quality loss is minimized. Experimental results demonstrate that hybrid NM-PSO is extremely effective and efficient in locating best-practice optimal solutions compared to geometric programming (GP), genetic algorithm (GA), and particle swarm optimization (PSO) methods.     

旋转货架拣选作业优化问题的新型混合遗传算法求解

给出了单拣选台分层水平旋转货架系统的数学模型,分析了单拣选台分层水平旋转货架拣选作业路径优化问题(MCS-OOP)的特点。在单层旋转货架中待拣选货物的最优拣选顺序将依次出现在对整个作业中所有待拣货物的最优拣选顺序中,针对该特点,提出了层序邻域的概念及其快速局部搜索算法,同时将其与遗传算法相结合设计了一种用于解决MCS-OOP的新型混合遗传算法。仿真结果说明了该算法能够快速、稳定的求取单拣选台MCS-OOP问题的最优解,充分满足了中大规模作业要求。     

Optimization of multi-pass turning operations using hybrid teaching learning-based approach

This paper presents a novel hybrid optimization approach based on teaching–learning based optimization (TLBO) algorithm and Taguchi’s method. The purpose of the present research is to develop a new optimization approach to solve optimization problems in the manufacturing area. This research is the first application of the TLBO to the optimization of turning operations in the literature The proposed hybrid approach is applied to two case studies for multi-pass turning operations to show its effectiveness in machining operations. The results obtained by the proposed approach for the case studies are compared with those of particle swarm optimization algorithm, hybrid genetic algorithm, scatter search algorithm, genetic algorithm and integration of simulated annealing, and Hooke–Jeeves patter search.     

Bi-objective optimization of maintenance scheduling for power systems

Enhance the quality of energy production in power generating stations and reducing its cost have become of paramount importance. One of the methods to reach that goal is by minimizing the maintenance scheduling time. For this purpose, a new competitive mechanism, based on a modified genetic algorithm (MGA), has been proposed to perform the preventive maintenance (PM) scheduling. Firstly, a mono-objective optimization (makespan) has implemented, and the results were quite good. Secondly, and in order to benefit from the waste time, a bi-objective optimization was developed to find a trade-off between makespan and training time of operators. Finally, the MGA-based maintenance scheduling was tested on a hybrid renewable power system (HRPS), that uses photovoltaic modules and a fuel cell (PV/FC) as sources and the telecommunication platform as load, the obtained results have proved the high efficiency of the proposed MGA-based maintenance scheduling.     

基于混合遗传算法的齿轮传动优化设计

以体积最小为目标函数,建立了齿轮传动优化设计数学模型,并用外部惩罚函数法将该问题转化为无约束优化问题。针对遗传算法的局限性,采用整数编码和实数编码结合的混合编码,并调整了适应函数,采用随机多父辈适应函数值加权交叉和自适应变异操作,结合了模拟退火算法,给出了初温的确定方法,从而形成了混合遗传算法。该算法能够有效地减少不可行解的产生,提高收敛速度,避免早熟收敛。算例说明,该优化方法有效、实用。     

A hybrid GA–PSO approach for reliability optimization in redundancy allocation problem

This paper presents a novel hybrid genetic algorithm (GA)-particle swarm optimization (PSO) approach for reliability redundancy allocation problem (RRAP) in series, series–parallel, and complex (bridge) systems. The proposed approach maximizes overall system reliability while minimizing system cost, system weight and volume, simultaneously, under nonlinear constraints. To meet these objectives, an adaptive hybrid GA–PSO approach is developed to identify the optimal solutions and improve computation efficiency for these NP-hard problems. An illustrative example is applied to show the capability and effectiveness of the proposed approach. According to the results, in all three cases, reliability values are improved. Moreover, computational time and variance are decreased compared to the similar studies. The proposed approach could be helpful for engineers and managers to better understand their system reliability and performance, and also to reach a better configuration.     

一种求解集成生产计划的混合协同进化算法

讨论了一类带有序列相关的机器调整时间和有限缓冲空间的流水车间批量计划与调度的集成优化问题,给出了该问题的非线性混合整数规划模型,提出了一种求解混合协同进化问题的算法.模型的目标函数是使库存费用、缺货费用和加班费用之和最小,约束函数考虑了库存平衡约束和需求平衡约束.算法采用协同进化算法与遗传算法的并行混合搜索结构,通过迁移算子把协同进化的子种群和独立进化的公共种群有机联系起来,同时算法采用基于邻域的进化策略,以提高算法性能.最后,对三种不同规模的问题进行了数值仿真实验,结果验证了算法的有效性.     

Optimizing the functional design and life cycle cost of mechanical systems using genetic algorithms

This paper presents a genetic algorithm-based approach for automatic functional design of mechanical systems. The proposed method automatically finds a set of optimal or near-optimal design solutions with respect to desired functional requirements stored in a pre-built device database. Although, typically, the design solution space of a given mechanical system is very large, the proposed strategy can generate high-quality design solutions in a timely manner. The method considers life cycle cost (LCC) factors as one of the design optimization criteria, since LCC factors have an important impact on product competency in the current market. Fuzzy logic is used to transform linguistic design requirements into numerical values. A case study is presented to illustrate the operation of the genetic algorithm-based functional design method and the efficiency of the method for optimizing the final design.     

基于混合退火遗传算法的圆柱蜗杆传动模糊优化设计

针对基本遗传算法在优化设计中遇到的局部搜索能力不强、早熟收敛等问题,提出一种将模拟退火、Powell搜索方法与遗传算法相结合的混合遗传算法.在此基础上对普通圆柱蜗杆传动模糊优化设计进行了研究;数值计算表明,该混合退火遗传算法可以有效地克服基本遗传算法的上述缺陷,可以加速算法的收敛,具有良好的优化性能.并用该算法较好地解决了普通圆柱蜗杆传动的模糊优化设计.