多目标的供应链集成优化及数值仿真

针对供应链设计中的供应链优化这个关键问题,建立了一个多目标的供应链集成优化模型。该模型将供应链三个层次的优化内容集成在一起进行优化,采用成本、柔性、客户服务水平以及向用户承诺的产品交付时间四个度量指标。针对多目标优化模型,提出了一种改进的多群体遗传算法。最后,利用一个具体实例进行了数值仿真。     

某机载雷达静压腔应力仿真与多目标优化

某机载雷达正常工作时,应力仿真结果显示其静压腔焊缝位置的最大应力为133.62 MPa,不满足强度要求,需要对其结构进行优化。综合比较构造拱形结构和局部加强这两种常见优化方案,选择在静压腔前后两面各增加一条加强筋。借助ANSYS WorkBench求解加强筋的最优宽度和厚度时,依据各位置强度要求之间的关系,建立一个直接描述优化效果的输出参数,用于定义优化模型,开展多目标优化。优化后,静压腔焊缝位置的最大应力为82.821 MPa,其余位置的最大应力为59.921 MPa,均满足强度要求。结构的不同位置通常具有不同的指标要求,文中据此建立输出参数进而求解优化模型的处理方法在此类问题中具有借鉴意义。     

基于多目标优化的制造资源分配研究

为了实现智能制造环境下企业制造资源的有效利用,提出了一种基于多目标优化的制造资源分配优化方法.构建了考虑加工成本、加工时间、加工质量和制造单元适合度的多目标优化模型,采用一种基于时变速度和位置加权更新的改进的粒子群算法,对该优化模型进行求解.最后通过仿真验证了所提方法的可行性.     

资源约束下产品开发任务调度的多目标优化

鉴于产品开发任务调度过程中存在资源约束问题和学习与遗忘效应,需要对多个目标进行优化决策,通过定义资源平均利用率并提出学习遗忘效应矩阵,结合耦合设计的多阶段迭代模型,以各阶段资源利用率为约束条件,建立资源约束下考虑学习与遗忘效应的任务调度时间与成本的多目标优化数学模型。采用带精英策略的非支配排序遗传算法求解得出Pareto最优解集,并采用改进的多目标理想点法对该解集进行选优,得到最优任务调度方案。以某电动汽车的开发过程为例,验证了该优化模型能够减小产品开发时间,降低产品开发成本,提高总资源利用率。     

面向多目标决策的车间生产调度仿真与优化

车间生产调度问题是先进制造和现代管理的核心问题。针对某企业机械加工车间现状,运用Flexsim仿真软件对该车间生产调度过程进行建模和仿真,发现未经调度优化的生产任务排序仿真结果中总完工时间、总提前时间、总拖期时间、生产成本等各项指标均很差。为了解决该问题,提出从生产效率、客户满意度、生产成本三个方面建立优化目标,调度优化后,各项指标较未优化前均有大幅度改善。因此,使用仿真软件进行面向多目标决策的车间生产调度优化,能为企业高层的管理与决策提供科学的量化依据,从而提升企业竞争力。     

面向网络化制造的制造资源优化配置研究

在网络化制造环境下,根据生产企业在外部资源利用方面的三类需求,提出了两种可行的制造资源优化配置方法;详细论述了优化的步骤和过程,建立了优化模型,给出了模型求解的有效算法。最后,给出具体的计算实例,以验证优化配置方法及算法的有效性。     

虚拟企业供应链网络的模糊多目标优化

对虚拟企业供应链网络采用数学规划方法建立数学模型，并用模糊多目标优化方法实现了对虚拟企业供应链的成本、库存、时间等绩效的多目标优化，最后使用离散事件仿真工具检验了模型的鲁棒性，通过该方法寻找到最优的虚拟企业供应链子网。     

多工艺路线多资源多目标的作业调度优化

针对多工艺路线多加工资源多目标的作业调度问题,提出了一种启发式活动调度算法,将该方法与多目标遗传算法及模糊优选技术相结合,得到了一种新调度算法.基于工序的染色体编码方法和基于活动启发式算法的交叉算子的运用,有效地缩小了遗传算法的搜索空间.将随机产生的权系数与模糊优选技术相结合,有助于遗传算法搜索到多个优良的调度方案,这为决策者得到最满意的调度方案提供了保证.仿真结果表明该算法是可行的,与国外学者的同类研究相比,具有一定的优越性.     

OEM企业车间单行布局多目标优化研究

针对OEM企业面临的问题,在分析单行布局单目标优化缺点的基础上,建立了单行布局物流成本、加工时间和面积占用率的多目标优化模型,考虑到产品的几何形状、密度、易损程度等因素对物流成本的影响,以当量物流量替代物流量计算物流成本,文章最后以OEM企业B车间为例,通过计算和emplarlt软件仿真,得出U形布局是其最优布局方案.     

基于ASP的网络化制造平台资源优化配置的方法研究

针对网络化制造服务平台实现制造资源的共享和技术资源的优势互补的优点 ,分析了网络化制造模式下车间设备资源的特点 ,提出了一种改进的层次分析法用于实现资源优化与配置 ,阐述了该算法在基于网络化制造服务平台中如何实现资源优化选择 ,并用实例说明了该算法的有效性、方便性和实用性     

面向模具制造业网络化制造平台的研究与开发

为缩短模具设计、制造周期,保证可靠的模具设计、制造品质,适应制造业网络化发展趋势,研究开发面向模具制造业网络化制造平台.该平台系统是在计算机支持协同工作(CSCW)的基础上,在Windows2000 Professional的系统环境下,以产品数据库为构成框架,采用动态网页编程技术(ASP)和Java3D语言开发实现的.介绍平台系统实现的主要功能模块及关键技术.通过实例运行证明该平台系统能为模具网络化制造提供一个良好的平台.     

多目标模糊优化方法及其在可靠性设计中的应用

在基于模糊机会约束规划理论基础上,对多目标模糊优化方进行研究,通过模糊模拟求解模糊目标函数的理想极大值和极小值,建立了多目标优化问题的模糊机会约束规划模型,并给出了基于模糊模拟的遗传算法求解方法.通过对一个无约束处理的常规双目标模糊可靠性优化问题实例求解,验证了多目标模糊模拟遗传算法的有效性,并指出该算法的优点在于能较好处理多目标优化问题的分目标量纲不同及其函数值之间差距较大的情况.     

Multi-objective optimization for turning processes using neural network modeling and dynamic-neighborhood particle swarm optimization

In this paper, we introduce a procedure to formulate and solve optimization problems for multiple and conflicting objectives that may exist in turning processes. Advanced turning processes, such as hard turning, demand the use of advanced tools with specially prepared cutting edges. It is also evident from a large number of experimental works that the tool geometry and selected machining parameters have complex relations with the tool life and the roughness and integrity of the finished surfaces. The non-linear relations between the machining parameters including tool geometry and the performance measure of interest can be obtained by neural networks using experimental data. The neural network models can be used in defining objective functions. In this study, dynamic-neighborhood particle swarm optimization (DN-PSO) methodology is used to handle multi-objective optimization problems existing in turning process planning. The objective is to obtain a group of optimal process parameters for each of three different case studies presented in this paper. The case studies considered in this study are: minimizing surface roughness values and maximizing the productivity, maximizing tool life and material removal rate, and minimizing machining induced stresses on the surface and minimizing surface roughness. The optimum cutting conditions for each case study can be selected from calculated Pareto-optimal fronts by the user according to production planning requirements. The results indicate that the proposed methodology which makes use of dynamic-neighborhood particle swarm approach for solving the multi-objective optimization problems with conflicting objectives is both effective and efficient, and can be utilized in solving complex turning optimization problems and adds intelligence in production planning process.     

Multi-objective optimization of high-speed milling with parallel genetic simulated annealing

In this paper, the optimization of multi-pass milling has been investigated in terms of two objectives: machining time and production cost. An advanced search algorithm—parallel genetic simulated annealing (PGSA)—was used to obtain the optimal cutting parameters. In the implementation of PGSA, the fitness assignment is based on the concept of a non-dominated sorting genetic algorithm (NSGA). An application example is given using PGSA, which has been used to find the optimal solutions under four different axial depths of cut on a 37 SUN workstation network simultaneously. In a single run, PGSA can find a Pareto-optimal front which is composed of many Pareto-optimal solutions. A weighted average strategy is then used to find the optimal cutting parameters along the Pareto-optimal front. Finally, based on the concept of dynamic programming, the optimal cutting strategy has been obtained.     

Multi-objective optimization of air bearings using hypercube-dividing method

The commonly used genetic algorithm (GA) in solving a multi-objective optimization problem (MOOP) is replaced by the hypercube-dividing method (HDM) in this air bearing optimization study. In the new method the dividing of hypercubes in the design space is conducted based on the size and Pareto rank of hypercube. A comparison of the HDM- and GA-based method for the MOOP is performed. The results show that the solution obtained by the HDM is improved with more selections and less computing load. The search in the HDM can also be confined to some useful resolution to improve its global search capability.     

网络化制造中ASP与CPC的集成分析

ASP(应用服务提供商)模式在网络化制造扮演着重要的角色,而CPC(协同产品商务)又是面向核心企业及其上下游供应链协作的重要网络化制造模式.ASP和CPC有着各自的优缺点,在实际操作中的状况也是不同的.通过研究CPC和ASP各自的特征和发展状况及原因,分析相互可以集成互用的因素,探讨ASP和CPC之间的集成.     

Multi-objective optimization for model predictive control

This paper presents a technique of multi-objective optimization for Model Predictive Control (MPC) where the optimization has three levels of the objective function, in order of priority: handling constraints, maximizing economics, and maintaining control. The greatest weights are assigned dynamically to control or constraint variables that are predicted to be out of their limits. The weights assigned for economics have to out-weigh those assigned for control objectives. Control variables (CV) can be controlled at fixed targets or within one- or two-sided ranges around the targets. Manipulated Variables (MV) can have assigned targets too, which may be predefined values or current actual values. This MV functionality is extremely useful when economic objectives are not defined for some or all the MVs. To achieve this complex operation, handle process outputs predicted to go out of limits, and have a guaranteed solution for any condition, the technique makes use of the priority structure, penalties on slack variables, and redefinition of the constraint and control model. An engineering implementation of this approach is shown in the MPC embedded in an industrial control system. The optimization and control of a distillation column, the standard Shell heavy oil fractionator (HOF) problem, is adequately achieved with this MPC.     

多目标质量的覆盖件成形工艺参数优化

覆盖件冲压是复杂的塑性变形过程,成形工艺参数较多,难以精确地建立工艺参数与成形质量之间的关系。选取优化合理的工艺参数匹配是保证无破裂、无起皱和板料厚度均匀分布等成形质量的关键。通过将压边力、拉深筋高度、凸筋圆角半径、凹筋圆角半径、摩擦因数作为自变量的条件下,以正交试验为设计方案,模拟轮包覆盖件成形,获得成形质量目标破裂、起皱、厚度最大变薄率的数据。通过层次分析法计算多质量目标的权重,利用灰色系统理论,分别计算成形质量对理想单目标值的关联系数和多目标函数的关联度,进一步计算获得各成形工艺参数的平均关联度,用优化的工艺参数进行有限元模拟验证,最终指导设计、试模,板料冲压结果表明成形的质量明显提高。     

Multi-objective optimization of surface grinding process with the use of evolutionary algorithm with remembered Pareto set

In this paper the multi-objective optimization of a surface grinding process making use of an evolutionary algorithm is presented. Such factors as wheel speed, workpiece speed, depth of dressing and lead of dressing are optimized in order to minimize production cost and surface roughness or to minimize production cost and maximize production rate. In the algorithm, the optimization is introduced in Pareto’s sense, all acceptable and non-dominated solutions are remembered, and therefore the final result is not a single solution, but a whole set. The proposed method based on an example chosen from literature is tested, and the results obtained are compared with the results obtained by the use of other methods.     

ASP平台计费系统发展趋势及关键技术分析

计费系统作为网络化制造ASP平台的重要组成部分,其发展方向日益受到ASP运营商的关注。通过对计费系统的研究,首先介绍了当前网络化制造ASP平台计费系统的现状以及存在的问题,然后对若干关键技术进行了分析,最后提出未来计费系统的特征及对其发展趋势的展望。