孔群加工路径规划问题的进化求解

孔群加工路径规划对于提高多孔类零件的加工效率和质量具有重要意义。建立了两个孔群加工路径规划问题的数学模型，分别归纳为单目标和多目标组合优化问题，并引入进化蚁群系统算法和人工免疫算法求解单目标组合优化问题。这两种算法均能有效防止解空间的“组合爆炸”问题，计算复杂度的阶次低于Hopfield神经网络算法，且性能优于Hopfield算法。采用多目标解的快速排序技术分别对进化蚁群系统算法和人工免疫算法加以改进，开发出多目标进化蚁群系统算法和多目标人工免疫算法。分析表明，改进算法不增加原算法的计算复杂度，能直接用于求解多目标组合优化问题而无需事先给出目标权值向量，并能一次运行求得问题的多个Pareto最优解。     

多目标拆卸线平衡问题的Pareto遗传模拟退火算法

针对传统方法求解多目标拆卸线平衡问题时求解结果单一、无法平衡各目标等不足,提出一种基于Pareto解集的多目标遗传模拟退火算法。该算法融合了遗传操作的快速全局搜索能力和模拟退火操作较强的局部搜索能力,对遗传操作的结果进行模拟退火操作,避免了算法陷入局部最优。结合多目标优化问题的特点,改进了模拟退火操作的Metropolis准则。根据拆卸序列之间的Pareto支配关系得到非劣解,并采用拥挤距离评价非劣解,实现了拆卸序列的精英保留,进而将非劣解添加到种群中,加快了算法的收敛速度。基于25项拆卸任务算例,通过与现有的6种单目标算法进行对比,验证了所提算法的有效性,并将所提算法应用于某拆卸线实例中,求得10种平衡方案,结果表明所提算法较Pareto蚁群算法更具优势。     

多目标不完全拆卸线平衡问题的建模与优化

针对实际生产中在满足约束条件下仅考虑拆卸需求零件和危害零件的特点,以工作站数目、空闲时间均衡指标和拆卸成本为优化目标,构建了不完全拆卸线平衡问题多目标模型。基于解的离散性和优化目标的多重性,提出一种Pareto解集思想的变邻域-粒子群融合算法。该算法通过建立拆卸任务和粒子群迭代搜索的对应关系,将变邻域搜索作为局部搜索策略,同时引入Pareto解集思想、拥挤距离机制处理多目标问题,以保证求解结果的多样性;通过Hyper-volume指标解决了多目标优化难以评价算法收敛性能及Pareto解集优劣等问题。采用所提算法求解不同规模完全拆卸线平衡问题测试算例,其中不同搜索深度的对比试验表明了变动搜索深度能很好地兼顾求解质量和求解效率,不同算法的对比试验表明了所提算法的优越性。最后,将所提模型与求解方法应用至某打印机不完全拆卸线的设计中。     

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。     

基于Pareto蚁群算法的拆卸线平衡多目标优化

为提高产晶拆卸效率,针对拆卸线平衡问题建立了数学模型.该模型以最小拆卸线闲置率、负荷均衡和最小拆卸成本为优化目标.结合拆卸线平衡问题的具体特点,提出了一种改进的基于Pareto解集的多目标蚁群优化算法.算法采用小生境技术,引导蚂蚁搜索到分布良好的Pareto最优解集,并以被支配度和分散度为个体评价规则.实验测试结果表明了该算法的可行性.最后,结合企业生产实际,给出了所提模型与算法的具体应用.     

虚拟装配中拆卸序列规划算法的研究与实现

为了解决装配体拆卸序列的生成与优化问题,利用零件的拆卸约束关系,建立了矩阵表达的包括几何约束、拆卸工具约束以及稳定性约束的多目标数学模型。利用干涉矩阵确定可行的拆卸方向,利用支撑矩阵分析拆卸过程的稳定性,利用拆卸工具列表分析拆卸过程中所用的拆卸工具。在运用蚁群算法寻求最优的拆卸序列过程中,加入方向变化因子、工具变化因子和稳定性因子,对拆卸转移概率公式进行了改进,通过更新局部和全局信息素,指导算法快速寻找最优解,最后以动车组转向架轮对作为实例验证了算法的可行性。     

基于SPEA2+的产品族模块单元多目标规划方法

在分析零件功能和物理特性的基础上,应用模糊数学理论给出产品族模块单元规划准则的量化计算方法,建立产品族模块单元多目标规划优化模型.针对传统的目标加权法在模型求解中的不足,采用改进的强度Pareto进化算法(SPEA2+)对模块单元多目标规划问题进行优化求解,从而得到一系列基于Pareto解集的产品模块规划方案,并利用模糊集合理论的Pareco综合选优方法得到了产品模块多目标规划的最优方案.结合项目实施以缝纫机为应用实例,验证提出方法的有效性和适应性.     

面向多人同时作业的拆卸序列规划

为生成面向多人同时作业的并行拆卸序列、提高求解的效率与质量,在分析现有方法不足之处的基础上,提出基于改进蚁群算法的面向多人同时作业的拆卸序列规划方法(Disassembly Sequence Planning For Multipeople Simultaneous Operation,DSPMSO)。针对该方法的特点,基于零件分层图对于零件配合关系以及可拆卸性的表达,提出改进的蚂蚁搜索方式,并采用动态候选表避免无效序列的产生;定义了零件拆卸所需人数与拆卸等待时间,提出以考虑等待时间为主的多人拆卸成本模型;针对基本蚁群算法求解复杂装配体拆卸序列时算法求解效率与解的质量难以兼顾的问题,提出算法的分阶段迭代策略:根据路径信息量确定算法迭代阶段,使蚂蚁具有不同的选路策略,提出与之对应的蚂蚁信息素自适应更新机制,使算法在求解的效率与质量之间取得较好的平衡。通过实例对关键参数的取值进行讨论,并验证了算法各项优化措施的有效性。     

随机作业时间的U型拆卸线平衡多目标优化

为更好地反映实际拆卸作业时间的不确定性,建立了考虑随机作业时间的多目标U型拆卸线平衡问题的数学模型,并针对传统方法求解多目标问题时求解结果单一、无法均衡各目标等不足,提出一种基于Pareto解集的多目标混合人工鱼群算法。算法采用自适应视野串行觅食方式,以减少并行觅食时出现重复搜索现象,并根据多目标拆卸序列之间的支配关系得到Pareto非劣解集,实现了鱼群寻优结果的多样性。对鱼群觅食得到的拆卸序列进行模拟退火操作,增强了算法跳出局部最优的能力。采用拥挤距离机制筛选非劣解,实现了拆卸序列的精英保留,进而将非劣解添加到下次迭代的种群中,加快了算法的收敛速度。将所提算法应用于具有55项任务的某打印机拆卸实例,经与基本人工鱼群算法、模拟退火算法对比,验证了所提算法的有效性和优越性。     

多目标的拆卸线平衡问题优化研究

拆卸是废旧产品回收利用的重要途径，根据产品拆卸线的特点，构建拆卸线平衡问题的多目标关系。针对求解拆卸线平衡问题所遇到的求解结果单一、无法满足平衡诸多目标等问题，以均衡工作站负载且引入操作安全性、任务拆卸方向和任务需求为优化目标建立数学模型，在此基础上，提出一种基于遗传改进的多目标灰狼算法进行求解计算。基于废旧电冰箱拆卸案例对比验证算法的效果与性能，并将其应用于再制造企业某废旧电视机拆卸实例中。以Plant Simulation 15.0仿真软件为平台，运用仿真技术对所得理论方案分析与优化，通过在重要工位上建立缓冲区来解决实际拆卸过程中所存在的工位堵塞问题。结果表明该算法的收敛性较好，所获得非支配解集更逼近Pareto最优前沿，得出多个合理拆卸方案，为决策者选择拆卸方案提供了依据，且改善后的工位产能也提升了3.23%。     

A new multi-objective ant colony algorithm for solving the disassembly line balancing problem

The disassembly line is the best choice for automated disassembly of disposal products. Therefore, disassembly line should be designed and balanced so that it can work as efficiently as possible. In this paper, a mathematical model for the multi-objective disassembly line balancing problem is formalized firstly. Then, a novel multi-objective ant colony optimization (MOACO) algorithm is proposed for solving this multi-objective optimization problem. Taking into account the problem constraints, a solution construction mechanism based on the method of tasks assignment is utilized in the algorithm. Additionally, niche technology is used to embed in the updating operation to search the Pareto optimal solutions. Moreover, in order to find the Pareto optimal set, the MOACO algorithm uses the concept of Pareto dominance to dynamically filter the obtained non-dominated solution set. To validate the performance of algorithm, the proposed algorithm is measured over published results obtained from single-objective optimization approaches and compared with multi-objective ACO algorithm based on uniform design. The experimental results show that the proposed MOACO is well suited to multi-objective optimization in disassembly line balancing.     

基于蚁群算法的产品拆卸序列规划研究

为了能以较高的效率求解出产品拆卸序列的方案，首先阐述了拆卸可行性信息图的概念，将产品的拆卸序列规划问题转述成对该加权有向图中具备最优值的路径搜索和寻优问题。提出了一种蚁群优化算法，并结合对产品元件的拆卸路径求解工具，以实现对产品拆卸可行性信息图的构建和对拆卸方案的搜索和寻优。蚂蚁的一条遍历路径代表了一个描述产品元件拆卸的方案；蚂蚁已经遍历过的路径上代表可行操作的节点数决定了其留下的信息素。启发式信息的求解分为两个部分，包括了确定启发式向量和求出启发式信息值，它们分别表征了方案的可行性及其优异程度。最后，通过一个实例，验证了这一方法的可行性及其计算效率。     

双边拆卸线平衡问题建模与优化

针对实际作业中部分产品采用双边拆卸,但已有拆卸线平衡问题研究中工作站均为单边布局的不足,建立了多目标双边拆卸线平衡问题模型。解码时,将任务优先分配至工作站较少的边、次优先分配至剩余时间较多的工作站,以缩短输送路径和工作站空闲时间。针对所建立模型,提出一种Pareto蝙蝠算法,引入Pareto思想以保证解的多样性;采用精英策略有效加速算法的收敛;通过拥挤距离筛选外部档案以提高算法运行效率。通过求解经典算例并对比分析,验证了所提出算法的有效性。将所建模型应用于拆卸线设计,能为决策者提供多种高质量的平衡方案。     

面向绿色制造的产品选择拆卸技术研究

针对机电产品的绿色制造,分析了产品生命周期中设计、服役使用和退役处理三个阶段的产品零部件选择拆卸问题;提出了基于蚁群算法的产品选择拆卸规划方法,给出了面向选择拆卸的产品图建模和选择拆卸序列的动态构建过程,并考虑拆卸方向改变次数和拆卸零件总数原则,以求解优化的选择拆卸序列;讨论了面向全生命周期的产品选择拆卸平台构成,并初步实现了平台的功能.最后,用实例说明了选择拆卸平台的工作过程.     

Disassembly sequence planning approach for product virtual maintenance based on improved max–min ant system

In order to realize automation and intelligence of product disassembly process in a virtual maintenance environment, an improved max–min ant system based methodology for product disassembly sequence planning was proposed. The feasibility graph for product disassembly process was defined and the mathematic model of product disassembly sequence planning problem was set up. Thus, the problem of product disassembly sequence planning was transformed into the problem of searching optimal path on a feasibility graph. Moreover, an improved max–min ant system based on the strategy of sorting elite ants was presented and the flowchart of the improved algorithm was designed. Finally, by simulation examples, the robustness and outperforming others of the improved algorithm were verified.     

A converse method-based approach for assembly sequence planning with assembly tool

In order to improve the quality and efficiency of assembly sequence planning for complex mechanical product, a converse method-based approach for assembly sequence planning is proposed. Firstly, the disassembly interference matrix of product and the disassembly constraint degree of part are defined. Product disassembly sequence is obtained through geometry reasoning algorithm and the searching work of disassembly sequence is simplified by the contact constraint set. Secondly, the maneuverability of tool is inspected through the using matrix and interference matrix of assembly tool. The infeasible disassembly sequence is excluded and product assembly sequence is obtained by reversing the order of disassembly sequence. Thirdly, the evaluation mechanism for assembly sequence is established to get the optimal assembly sequence and the optimal assembly sequence is selected by calculating the value of evaluation function. Finally, a vibration generator is illustrated to verify the validity and feasibility of the proposed approach.     

Optimization of disassembly sequence planning for preventive maintenance

Disassembly sequence planning is considered an important research topic in the manufacturing automation field. In recent years, in close collaboration with manufacturers, many investigations have been conducted to design robust and profitable dismantling systems. Therefore, the designers of new products have to consider the disassembly constraints in the design phase of products. Moreover, considering the disassembly constraints is important not only in the context of life end of a product but also in its life cycle in order to reduce problems related to its exploitation and maintenance. Consequently, the optimization of the disassembly process of a product is a crucial task for improving the product design. This article presented an automated disassembly sequence planning approach based on ant colony algorithm. The developed method permits the generation of an optimal and feasible disassembly sequence planning of a product from its computer-aided design (CAD) model. Several criteria were introduced in this approach such as part volume, tool change, disassembly directions, and the maintainability of wearing part. A comparison between genetic and ant colony algorithms was conducted to reveal the effectiveness of the proposed approach. A case study is presented and an implemented tool was developed. The obtained results demonstrate the satisfactory side of the considered criterions to identify the feasible disassembly sequence plan.     

Pareto-based discrete artificial bee colony algorithm for multi-objective flexible job shop scheduling problems

This paper presents a hybrid Pareto-based discrete artificial bee colony algorithm for solving the multi-objective flexible job shop scheduling problem. In the hybrid algorithm, each solution corresponds to a food source, which composes of two components, i.e., the routing component and the scheduling component. Each component is filled with discrete values. A crossover operator is developed for the employed bees to learn valuable information from each other. An external Pareto archive set is designed to record the non-dominated solutions found so far. A fast Pareto set update function is introduced in the algorithm. Several local search approaches are designed to balance the exploration and exploitation capability of the algorithm. Experimental results on the well-known benchmark instances and comparisons with other recently published algorithms show the efficiency and effectiveness of the proposed algorithm.