基于遗传算法和模型仿真的调度规则决策方法

为了完成特定生产环境下的调度规则选择问题,提出一种将遗传算法和过程仿真相结合的调度规则求解方式。在该求解方式中,遗传算法采用分段整数编码,每个染色体都代表一组可用于描述具体调度方案的规则组合;遗传操作包括选择、交叉、变异三种类型;为获得适应度函数值,利用基于某扩展Petri网的生产过程模型进行仿真,以在每一代种群中,得到与每个染色体相对应的各项性能指标值,进而以一种集成层次分析法和方案模糊评判的决策优化方法求取相应的适应度函数值。另外,为了改善串行遗传算法不切实际的解答时间,用主从式并行遗传算法代替传统遗传算法,保证了解在时间上和质量上的可行性。     

基于遗传算法和延时Petri网的柔性装配系统的设备调度方法

为解决柔性装配系统的设备调度问题,提出了一种将基于延时Petri网的装配过程仿真与基于遗传算法相结合的调度方法。在该方法中,遗传算法使用的染色体是由延时Petri网模型中的部分选择库所名称排列而成,每个染色体都代表一种设备调度方案。遗传操作包括选择、交叉和变异3种类型,利用基于延时Petri网的装配过程模型进行仿真,得到每个染色体相对应的装配时间,进而将装配时间通过适应度函数转化为适应度。该方法融合了Petri网和遗传算法各自的优点,较好地解决了柔性装配系统中的装配建模和装配任务分配优化的问题。仿真实验证明该方法是有效的。     

免疫遗传算法求解工艺规程及作业调度协同优化

提出了基于工艺规程及作业调度协同优化的并行分布式工艺设计与调度集成模型,建立了工艺规程及作业调度协同优化的数学模型。提出了一种协同进化免疫遗传算法来同时优化零件的备选工艺规程组合以及调度方案,为各零件确定最优工艺规程及与之相对应的最优调度方案。通过工艺种群及调度种群的相互作用及相互促进,大大降低了计算复杂性并提高了搜索效率。通过对10台设备10种零件的实例仿真验证了算法的有效性。     

并行机作业车间等量分批多目标优化调度

为降低并行机作业车间等量分批多目标优化调度问题的复杂度,提高优化效率,提出了一种基于仿真技术和改进非支配排序遗传算法的分步优化方法.建立了一类以完工时间最短和总制造成本最低为优化目标的并行机作业车间等量分批多目标优化调度模型;将各产品进行等量分批,以Witness为仿真平台建立并行机作业车间等量分批生产仿真模型,通过组合仿真优化得到产品理想的等量分批方案,从而将原问题转化为并行机作业车间多目标优化调度问题;设计了一种改进的非支配排序遗传算法,对并行机作业车间多目标优化调度进行求解.通过算例分析验证了该方法的有效性.     

基于遗传算法与仿真联合的车间调度方案优化

以车间生产系统仿真模型为仿真器，以改进的遗传算法模块为优化器，通过Socket实现联合求解调度优化问题。结果表明，优化的调度方案相比实际完成时间减少19.4%；相比考虑实际因素的仿真时间减少14.3%。通过遗传算法与仿真模型的联合求解，找到了较优的调度方案，实现了生产能力的有效提升。     

基于遗传算法的订单式车间调度问题

针对订单式生产特点,提出了订单式专门机调度模型,给出了相应的解决问题的遗传算法具体实现过程.针对该模型,对算法进行适应度值标定、采用集合分割的交叉算子以及大变异策略等操作设计.最后,将该算法与启发式规则对不同规模问题的模拟仿真比较,证明该算法能更有效地取得较优解,并能用于较大规模的调度中.     

无缝钢管生产的多规则调度仿真优化研究

无缝钢管生产由热加工与冷加工组成,热加工有很强的连续性,而冷加工过程具有可重入现象,单一规则或局部规则调度很难解决此类问题。提出多规则组合仿真优化方法,从无缝钢管的生产特点入手,寻找到关键决策点,在Arena仿真环境下设计调度规则模块库和系统参数水平集合;采用柔性化仿真建模技术,将仿真模型分为功能模块、控制模块,构建无缝钢管生产系统的仿真模型;以平均生产周期为目标,利用TS算法优化无缝钢管生产系统的调度规则组合及相关参数水平,实例证明方法有效.     

面向随机加工时间的车间作业调度

提出了面向随机加工时间的车间作业调度方法,认为在整个遗传进化过程中出现频率越高的个体对环境的适应能力越强,该个体对应的调度方案为较优方案,构造了用于解决加工时间为服从正态分布的随机变量的车间作业调度问题的扩展遗传算法.在算法中设计了考虑设备能力空间的解码算法以产生活动调度方案;在交叉/变异过程中通过设计的基因调整算法确保新个体的合法性,以满足工序约束;采用基于适应值的轮盘赌的选择策略控制遗传进化的方向,使算法快速收敛到最优解.仿真实验验证了该算法在企业实际随机车间作业调度中的有效性.     

服务型制造车间关键任务调度的Stackelberg博弈研究

针对服务型制造车间关键任务调度问题,提出了两层次嵌套的Stackelberg博弈调度模型。该博弈模型由Stackelberg子博弈与非合作静态子博弈构成。其中Stackelberg子博弈模型用于解决关键任务与非关键任务的之间的调度决策问题,非合作静态子博弈模型则用于实现非关键任务之间的调度决策。在该博弈调度模型中,将关键任务映射为领导者,将其余非关键任务映射为追随者,将与各任务包含的工序集所对应的可选加工设备映射为可行方案集,将各任务的综合成本指标映射为收益函数。为实现对模型的Stackelberg均衡点的有效求解,设计了基于爬山搜索的混合自适应遗传算法。算例仿真结果验证了所提出的模型与解算方法的正确性。     

基于Ansoft的通风机用永磁无刷直流电机设计与分析

采用Ansoft中RMxprt模块,对通风机用永磁无刷直流电机进行设计,得出了电机各部分的尺寸和相关参数,并采用Maxwell2D模块对该电机空载时的相关特性进行仿真和分析。最后对仿真结果与样机试验结果的各项性能指标进行了对比,验证了仿真模型和分析的准确性。     

GA-based synthesis approach for machining scheme selection and operation sequencing optimization for prismatic parts

To obtain global and near-global optimal process plans based on the combinations of different machining schemes selected from each feature, a genetic algorithm-based synthesis approach for machining scheme selection and operation sequencing optimization is proposed. The memberships derived from the fuzzy logic neural network (FL-NN), which contains the membership function of each machining operation to batch size, are presented to determine the priorities of alternative machining operations for each feature. After all alternative machining schemes for each feature are generated, their memberships are obtained by calculation. The proposed approach contains the outer iteration and nested genetic algorithm (GA). In an outer iteration, one machining scheme for each feature is selected by using the roulette wheel approach or highest membership approach in terms of its membership first, and then the corresponding operation precedence constraints are generated automatically. These constraints, which can be modified freely in different outer iterations, are then used in a constraints adjustment algorithm to ensure the feasibility of process plan candidates generated in GA. After that, GA obtains an optimal process plan candidate. At last, the global and near-global optimal process plans are obtained by comparing the optimal process plan candidates in the whole outer iteration. The proposed approach is experimentally validated through a case study.     

Integration of Process Planning and Scheduling Using Simulation Based Genetic Algorithms

Process planning and scheduling are traditionally regarded as separate tasks performed sequentially; but, if the two tasks are performed concurrently, greater performance and higher productivity of a manufacturing system can be achieved. Although several workers have addressed the process plan selection problem in recent years, their main approaches are to select process plans from plan alternatives by taking into account the similarities among process plans of the parts. In this paper, we propose a new approach to the integration of process planning and scheduling using simulation based genetic algorithms. A simulation module computes performance measures based on process plan combinations instead of process plan alternatives and those measures are fed into a genetic algorithm in order to improve the solution quality until the scheduling objectives are satisfied. Computational experiments show that the proposed method reduces significantly scheduling objectives such as makespan and lateness.     

Controlling flexible manufacturing systems based on a dynamic selection of the appropriate operational criteria and scheduling policy

This study presents the development of a multi-criteria control methodology for flexible manufacturing systems (FMSs). The control methodology is based on a two-tier decision making mechanism. The first tier is designed to select a dominant decision criterion and a relevant scheduling rule set using a rule-based algorithm. In the second tier, using a look-ahead multi-pass simulation, a scheduling rule that best advances the selected criterion is determined. The decision making mechanism was integrated with the shop floor control module that comprises a real-time simulation model at the top control level and RapidCIM methodology at the low equipment control level.A factorial experiment was designed to analyze and evaluate the two-tier decision making mechanism and the effects that the main design parameters have on the system’s performance. Next, the proposed control methodology was compared to a selected group of scheduling rules/policies using DEA. The results demonstrated the superiority of the suggested control methodology as well as its capacity to cope with a fast changing environment.     

用遗传算法优化制造设备的预防性维修周期模型

建立了预防性维修周期间故障的递推关系式，给出了有限时间区间的设备预防性维修策略的非线性优化模型。该模型综合考虑了维修成本、预防性维修成本和生产损失成本，克服了无限时间区间稳态分析操作性差的缺点，并以故障分布形式为威布尔分布的设备为例，用遗传算法进行优化。计算结果显示，遗传算法能以极快的收敛速度达到全局最优，具有较高的计算效率。模型可为维修计划的制定和现场的作业调度提供决策支持和信息支持。     

工业设备预防性维修策略及其效果评估

采用随机变量评估工业设备预防性维修的效果,以单位时间平均维修费用最低为目标,并假定评估预防性维修效果的参数服从均匀分布,构建维修策略的优化模型并进行数值仿真.仿真结果表明,采用随机参数的预防性维修模型能有效降低故障率增长趋势,减少单位时间平均维修费用,具有更好的优化结果,可以为维修计划的制定和现场的作业调度提供决策支持.     

基于仿真和正交试验的优化调度研究

针对生产系统多点协调调度问题,提出了一种基于仿真和正交试验的优化调度方法。在动态生产环境中,为了支持快速仿真调度分析,以模型元素和控制逻辑分离的原则为指导,采用面向对象方法,实现了基于模块化控制逻辑的快速仿真建模。以仿真模型作为系统目标求解函数,各种决策参数作为系统输入变量,通过正交试验优化仿真实验,确定最佳调度决策参数组合,将其应用于生产系统的调度控制。以某研究所的数控车间为例,验证了该调度方法的可行性和有效性。     

工程迭代设计中产品族开发过程的研究与建模

为了在工程迭代设计中减少新产品开发的提前期,提出了一种基于设计结构矩阵的产品族开发过程模型.在该模型中,基于设计参数间的依赖关系建立设计结构矩阵,并运用路径搜索算法将其整理为包含耦合子矩阵的解耦矩阵.对于其中的耦合子矩阵,运用基于马尔可夫链的串行迭代模型计算设计总时间,并运用基因算法优化排序.运用迭代设计的并行模型识别出对耦合关系贡献大的设计模态和设计参数,根据设计模态模块化设计参数,而对耦合关系贡献小的设计参数进行标准化.最后,将设计参数层的标准化和模块化映射到零件层,建立相应的产品族结构,以汽车离合器的设计为例阐述了该过程模型的有效性和应用前景.     

基于遗传算法的多资源作业车间智能优化调度

提出一种基于遗传算法的调度算法，用于解决作业车间的加工受到机床、操作工人和机器人等多种生产资源制约条件下的优化调度。以生产周期为目标进行的优化调度，将遗传算法和分派规则相结合，通过交叉、交异等遗传操作，得到目标的最优或次优解。最后对算法进行了仿真研究，并给出了算法运行结果，仿真结果表明该算法是可行的。     

Concurrent Optimisation of Parameter and Tolerance Design via Computer Simulation and Statistical Method

This study optimises component parameters and component tolerances simultaneously via computer simulation and response surface methodology (RSM). The approach first generates a set of experimental data through computer simulation, then the data are converted to a total cost as a response value before applying RSM for statistical analysis and mathematical optimisation. The response value (total cost) includes quality and related costs which reflect the combined effect of the parameter and tolerance values being assigned. The results provide designers with the optimal component design values, the critical components, and the response function of a product or process design, which are very important to know during design activities as they give designers information about repeated applications, accurate feedback and appropriate suggestions, particularly under uncertain design conditions. Three examples are provided: They are mechanical assembly design, machining process planning, and electronic circuit design.