制造协作组织形成过程不确定性任务性能参数的区间优化

针对制造协作组织形成过程中产品制造任务的优化问题,提出制造任务逆向优化过程模型,进一步形式化描述制造任务性能参数的逆向优化过程,构建任务性能参数优化的一般数学模型.针对不确定性制造任务性能参数,探讨不确定性制造任务性能参数的处理方法,建立逆向优化过程中区间矢量的距离模型,从而确定制造任务性能参数的区间优化模型.利用基于实数编码的遗传算法对区间优化模型进行求解,用实例验证优化方法与算法的有效性.     

型号协作任务与资源的集成优化

基于型号协作过程的双向优化结构,探讨协作任务参数与资源的集成优化过程。提出协作任务参数距离与距离方差模型,建立任务参数逆向优化目标函数。基于向量范数理论探讨优化目标函数的性质,利用基于实数编码的遗传算法对模型进行求解。基于任务参数的优化过程,建立协作资源优化配置的模型,从而实现协作任务与资源优化的集成。     

基于粒度模型的型号协作任务结构优化

针对型号任务协作过程中任务结构的优化,建立协作任务粒度模型,确定影响任务粒度优化设计的关键因素。基于市场协作成本、任务过程周期、任务技术方案等三个方面的影响,建立其与任务粒度之间的数学模型,得出了在相应条件下的任务粒度最佳值。归纳、分析这三个方面关键因素的影响,探讨最佳任务粒度的现实意义,总结型号任务协作过程中任务结构优化设计的方法,特别强调在市场协作环境中各方协作的历史表现与积累的宝贵经验对任务结构优化设计具有重要的影响。     

网络化协作环境下机械产品加工任务粒度分析

面向网络化协作制造环境，分析了产品任务规划与制造资源配置的关系，建立了机械产品加工任务粒度模型。针对机械产品网络化协作制造成本、产品功能组件和工艺方法、任务制造周期等3方面的影响因素，从宏观角度定性、定量地分析了产品制造任务粒度的规划问题，建立了它们与制造任务粒度之间的数学关系式，得出了在相应条件下任务粒度的最佳值。从这些数量化的结果与关系中，得出一些在网络化制造环境中对机械产品制造任务设计有意义的结论，为进一步的制造资源优化配置打下了理论基础。     

水平型制造协作联盟订单分配多目标优化模型研究

针对水平型制造协作联盟的订单分配问题,引入了生产负荷参数,建立了最小化综合成本与生产负荷均衡的多目标优化模型。应用改进的非支配排序遗传算法对多目标优化模型进行求解,获得了Pareto最优解集。仿真计算结果表明,所提出的模型和算法能够获得满意的解。     

制造过程多目标优化的集成计算智能方法

针对制造过程因动态多变而难以定量控制的问题，提出了用集成计算智能方法进行多目标优化。利用人工神经网络进行系统建模，并为遗传算法找到适应度函数及求得目标函数值的方法，进而利用遗传算法进行多目标优化。通过实例验证了方法的有效性与实用性，实现了制造过程的定量分析，为复杂制造系统的建模和优化提出了一种新的方法。     

基于改进遗传算法的多目标装配线平衡优化研究

针对离散制造企业装配线再平衡问题,文章提出基于改进遗传算法的多目标装配线平衡优化方法.以最小化生产节拍、最大化产线平衡率和最小化平滑指数为优化目标建立装配线再平衡优化模型,并采用改进的遗传算法对平衡模型进行求解,算法基于任务排序的种群初始化方法,采用两点交叉方法,提高了算法寻优能力.文章最后以青贮机装配线实际案例验证了...     

基于遗传算法的绿色集成工艺优化模型求解

为了提高生产计划的可执行性,实现制造资源的优化配置和可持续发展,建立了一种面向绿色制造的集成工艺优化模型,并针对该模型的特点,提出了一种基于遗传算法的面向绿色制造的集成工艺优化模型求解方法。该方法改进了传统遗传算法中复杂的实数编码方式,将其转化为通过"编码—解码"方式实现的二进制编码方式,避免了繁杂的实数编码。最后采用高级语言实现了该算法,并进行了案例分析,结果表明,该算法具有一定的先进性。     

基于田口方法的汽车悬架稳健性优化

针对悬架设计制造过程中存在不确定性因素而带来性能不稳定的问题,对某电动车麦弗逊悬架进行空间运动学分析,建立了求解悬架性能参数的数学模型,并通过试验验证了模型的合理性。利用该数学模型,采用田口稳健性设计方法,进行正交试验设计,对悬架跳动力学特性进行了多目标稳健性优化,将优化结果进行蒙特卡罗验证,结果表明,优化后的悬架性能及其稳健性均有了很大提高。     

基于遗传算法的绿色集成工艺优化模型求解

为了提高生产计划的可执行性,实现制造资源的优化配置和可持续发展,建立了一种面向绿色制造的集成工艺优化模型,并针对该模型的特点,提出了一种基于遗传算法的面向绿色的制造的集成工艺优化模型求解方法.该方法改进了传统遗传算法中复杂的实数编码方式,将其转化为通过"编码--解码"方式实现的二进制编码方式,避免了繁杂的实数编码.最后采用高级语言实现了该算法,并进行了案例分析,结果表明,该算法具有一定的先进性.     

面向复杂零件协同制造的资源优化配置技术研究

面向复杂零件的异地协同制造，提出依据工艺流程进行制造任务分解，研究了以工艺流程为核心的逻辑制造单元（LMU）和逻辑加工路线（LMP）设计，有效利用LMP和LMU描述针对复杂零件的协同制造任务。对复杂零件异地协同制造的制造资源优化配置问题进行了数学分析和描述，阐述了问题的目标与约束条件，将资源优化配置问题归结为多目标优化问题，利用遗传算法进行求解，并进行了应用实例分析，证明了采用制造资源优化配置方法可以有效解决复杂零件网络化异地协同制造的资源优化配置问题。     

Optimization of cutting conditions during continuous finished profile machining using non-traditional techniques

Optimum machining parameters are of great concern in manufacturing environments, where economy of machining operation plays a key role in competitiveness in the market. Many researchers have dealt with the optimization of machining parameters for turning operations with constant diameters only. All Computer Numerical Control (CNC) machines produce the finished components from the bar stock. Finished profiles consist of straight turning, facing, taper and circular machining.This research work concentrates on optimizing the machining parameters for turning cylindrical stocks into continuous finished profiles. The machining parameters in multi-pass turning are depth of cut, cutting speed and feed. The machining performance is measured by the production cost.In this paper the optimal machining parameters for continuous profile machining are determined with respect to the minimum production cost subject to a set of practical constraints. The constraints considered in this problem are cutting force, power constraint, tool tip temperature, etc. Due to high complexity of this machining optimization problem, six non-traditional algorithms, the genetic algorithm (GA), simulated annealing algorithm (SA), Tabu search algorithm (TS), memetic algorithm (MA), ants colony algorithm (ACO) and the particle swarm optimization (PSO) have been employed to resolve this problem. The results obtained from GA, SA,TS, ACO, MA and PSO are compared for various profiles. Also, a comprehensive user-friendly software package has been developed to input the profile interactively and to obtain the optimal parameters using all six algorithms. New evolutionary PSO is explained with an illustration .     

Designing integrated cellular manufacturing systems with scheduling considering stochastic processing time

This paper addresses a new mathematical model for cellular manufacturing problem integrated with group scheduling in an uncertain space. This model optimizes cell formation and scheduling decisions, concurrently. It is assumed that processing time of parts on machines is stochastic and described by discrete scenarios enhances application of real assumptions in analytical process. This model aims to minimize total expected cost consisting maximum tardiness cost among all parts, cost of subcontracting for exceptional elements and the cost of resource underutilization. Scheduling problem in a cellular manufacturing environment is treated as group scheduling problem, which assumes that all parts in a part family are processed in the same cell and no inter-cellular transfer is needed. Finally, the nonlinear model will be transformed to a linear form in order to solve it for optimality. To solve such a stochastic model, an efficient hybrid method based on new combination of genetic algorithm (GA), simulated annealing (SA) algorithm, and an optimization rule will be proposed where SA and optimization rule are subordinate parts of GA under a self-learning rule criterion. Also, performance and robustness of the algorithm will be verified through some test problems against branch and bound and a heuristic procedure.     

The optimization of attribute selection in decision tree-based production control systems

This study develops a learning-based production control system (PCS) to support a manufacturing system to make on-line decisions that are robust in the face of various production requirements. Selecting essential system attributes (or features) based on various production requirements to construct PCS knowledge bases is a critical issue because of the existence of a large amount of shop floor information in a manufacturing system. However, a classical decision tree (DT) learning approach to construct dynamic dispatching knowledge bases does not consider the optimal subset of system attributes in the problem domain. To resolve this problem, this study develops a hybrid genetic algorithm/decision tree (GA/DT) approach for DT-based PCS. The hybrid GA/DT approach is used to simultaneously evolve an optimal subset of system attributes and determine learning parameters of the DT from a large set of candidate manufacturing system attributes according to various performance measures. For a given feature subset and learning parameters of a DT decoded by a GA, a DT was applied to evaluate the fitness in the GA process and to generate the PCS knowledge base. The results demonstrate that the proposed GA/DT-based PCS has, according to various performance criteria, a better long term system performance than those obtained with classical DT-based PCS and the heuristic individual dispatching rules, according to various performance criteria.     

基于遗传算法的铣削加工参数模糊优化系统研究

优化铣削参数对于降低铣削加工成本、提高生产率有重要的作用。传统的铣削参数优化模型中，铣削参数和条件约束的匹配取值往往是通过实际加工的经验获得，这种结果具有不确定性和模糊性。文章分析了模糊参数优化的数学模型，根据模糊集合原理将模糊模型转化为一个传统的单目标模糊优化问题，借用IDEF1x方法建立了铣削参数模糊数据库模型，并运用遗传算法（GA）为优化引擎开发实现了模糊优化系统。给出的运行实例表明该优化系统对铣削参数优化具有更好的效果，同时，系统为CAPP优选铣削参数提供了支持平台。     

基于群智能算法的设备布局离散优化研究

针对单向环形设备布局设计问题,建立了新的数学模型.利用多维实数编码及映射方法,将连续粒子群优化算法应用于求解设备单向环形布局问题,提供了求解离散优化问题的新思路.利用遗传算法中的杂交策略扩展了粒子群优化算法,提高了粒子群优化算法性能.建立了问题的图结构描述,以引入蚁群系统算法搜索优化解.给出了两种方法的求解步骤.通过实例计算和结果比较,说明该算法能有效地解决此类离散优化问题,降低成本,提高效率,所得解质量较高,有很好的实用价值.     

蚁群算法及灰色理论在制造资源配置中的应用

为了优化网络化制造环境下的制造资源配置问题,提出了一种将灰色关联理论和蚁群算法相结合的资源优化选择求解方式.在该求解方式中,首先根据工艺规划将零件加丁任务分解为按照时间先后排列的工序集;然后在每个工序节点上利用灰色关联理论解决多目标决策问题中的优势,通过多层次灰色关联系数的确定,筛选出一定数量满足要求的候选制造资源,从而缩小问题域的范围;最后利用蚁群算法从运输成本角度出发,寻找零件加工最优的制造资源选择路径,以实现网络化制造中制造资源的优化配置.     

Hybrid evolutionary algorithm with marriage of genetic algorithm and extremal optimization for production scheduling

This paper presents a hybrid evolutionary algorithm with marriage of genetic algorithm (GA) and extremal optimization (EO) for solving a class of production scheduling problems in manufacturing. The scheduling problem, which is derived from hot rolling production in steel industry, is characterized by two major requirements: (i) selecting a subset of orders from manufacturing orders to be processed; (ii) determining the optimal production sequence under multiple constraints, such as sequence-dependant transition costs, non-execution penalties, earliness/tardiness (E/T) penalties, etc. A combinatorial optimization model is proposed to formulate it mathematically. For its NP-hard complexity, an effective hybrid evolutionary algorithm is developed to solve the scheduling problem through combining the population-based search capacity of GA and the fine-grained local search efficacy of EO. The experimental results with production scale data demonstrate that the proposed hybrid evolutionary algorithm can provide superior performances in scheduling quality and computation efficiency.     

求解分布式混合流水线调度问题的改进双层嵌套式遗传算法

针对分布式混合流水线生产的生产调度问题,模拟实际排产中的排产到线和排产到时的排产策略,提出了基于改进双层嵌套式遗传算法的两层优化模型。外层依据流水线分配平衡和准时交货等基本原则总体上解决生产订单在流水线之间的分配问题,内层以最小生产时间为主要目的求解流水线的生产订单生产次序问题。考虑到双层嵌套式遗传算法的时间复杂性,基于模糊逻辑理论设计了一种模糊控制器来动态调整遗传算子,并采用主动检测停止方法,提高算法效率。使用某空调工厂的实际生产数据验证了算法的可行性、计算结果的准确性及排产策略的有效性,为高级计划与排程(Advanced Planning and Scheduling,APS)中大规模复杂供应链调度问题提供了可借鉴的方法。