An assembly sequence planning approach with a discrete particle swarm optimization algorithm

In this paper, a discrete particle swarm optimization (DPSO) algorithm is proposed to solve the assembly sequence planning (ASP) problem. To make the DPSO algorithm effective for solving ASP, some key technologies including a special coding method of the position and velocity of particles and corresponding operators for updating the position and velocity of particles are proposed and defined. The evolution performance of the DPSO algorithm with different setting of control parameters is investigated, and the performance of the proposed DPSO algorithm to solve ASP is verified through a case study.     

粒子群算法在装配顺序规划中的应用

装配顺序规划是计算机辅助工艺设计的一个重要环节,影响着轿车车身的装配质量和效率.针对当前装配顺序规划易产生组合爆炸等问题,提出了基于粒子群算法的装配顺序规划算法.装配偏差是影响装配质量的重要因素,因此应用装配体的装配偏差评价装配顺序.在装配顺序规划过程中,首先将装配顺序编码为粒子,根据所建立的判断规则进行识别粒子的可行性,并通过装配偏差评估可行粒子的适应度值,然后根据粒子群算法过程规划装配顺序,最后采用前翼子板案例阐述装配顺序的生成和优化过程.     

基于粒子群—遗传混合算法的装配操作优化

为通过装配工艺优化提高车身装配尺寸质量,针对车身众多几何可行装配顺序,应用多属性有向图描述零件间的优先关系和装配控制特征数量,来去除非工程可行装配顺序。以装配尺寸质量为目标函数,提出粒子群—遗传混合算法优化零件间装配操作,通过线性装配偏差分析模型进行装配偏差累积运算,获得了最优装配顺序。通过车身侧围装配体阐述了装配控制特征的优化过程,结果表明,不同的装配顺序将影响装配控制特征的选择,从而影响最终的产品装配偏差。     

A green assembly sequence planning model with a closed-loop assembly and disassembly sequence planning using a particle swarm optimization method

In a green product life cycle, it is necessary to determine how to disassemble a product before the product is planned to be assembled. In this research, a green assembly sequence planning model is developed. A closed-loop assembly sequence planning model is presented by integrating assembly and disassembly sequence planning models. For an assembled product, an assembly sequence planning model is required for assembling the product at the start, while a disassembly sequence planning model is performed for disassembling the product at the end. In typical assembly and disassembly sequence planning approaches, the two sequences are independently planned and evaluated. In this paper, a closed-loop model is presented to concurrently generate and evaluate the assembly and disassembly sequences. First, a graph-based model is presented for representing the feasible assembly sequences and disassembly sequences. Next, a particle swarm optimization (PSO) method with a new encoding scheme is presented. In the PSO method, the assembly and disassembly sequences can be simultaneously represented and evaluated with an objective of minimizing the total of assembly costs and disassembly costs. The test results show that the presented method is feasible and efficient for solving the integrated assembly and disassembly sequence planning problem. An example product is illustrated and discussed.     

基于粒子群算法的产品拆卸序列规划方法

为求取复杂产品的最优拆卸序列,建立了一种产品拆卸赋权混合图模型.利用该模型可以有效地表达组件问的拆卸优先关系,将零件的拆卸序列规划转化为图模型寻优的问题.基于该模型,推导出可拆卸性条件,并通过几何推理的方法产生可拆卸序列.针对复杂产品拆卸序列规划的特点,为了将赋权混合图模型映射到粒子群模型,给出了粒子速度和位置公式以及粒子进化规则,构建粒子适应度,应用粒子群算法实现了复杂产品的最优拆卸序列规划.最后,通过一个实例验证了该方法的有效性.     

Research on a kind of assembly sequence planning based on immune algorithm and particle swarm optimization algorithm

In order to solve the assembly sequence planning of a certain type of product (e.g., product is composed of different types of parts and each type has several identical parts), first of all, the rule of nomenclature for this type of product is designed. Secondly, geometric feasibility and coherence are designed as constraint conditions, and these two constrains and processability are combined with each other as the objective function. Finally, the proposed novel method under the name of immune particle swarm optimization algorithm is tested. The results show that the proposed immune particle swarm algorithm is an efficient approach to solve the assembly sequence planning problem.     

No-idle permutation flow shop scheduling based on a hybrid discrete particle swarm optimization algorithm

A novel hybrid discrete particle swarm optimization (HDPSO) algorithm is proposed in this paper to solve the no-idle permutation flow shop scheduling problems with the criterion to minimize the maximum completion time (makespan). Firstly, two simple approaches are presented to calculate the makespan of a job permutation. Secondly, a speed-up method is proposed to evaluate the whole insert neighborhood of a job permutation with (n?1)² neighbors in time O(mn ²), where n and m denote the number of jobs and machines, respectively. Thirdly, a discrete particle swarm optimization (DPSO) algorithm based on permutation representation and a local search algorithm based on the insert neighborhood are fused to enhance the searching ability and to balance the exploration and exploitation. Then, computational simulation results based on the well-known benchmarks and statistical performance comparisons are provided. It is concluded that the proposed HDPSO algorithm is not only superior to two recently published heuristics, the improved greedy (IG) heuristic and Kalczynski–Kamburowski (KK) heuristic, in terms of searching quality, but also superior to the single DPSO algorithm and the PSO algorithm with variable neighborhood search (PSOvns) in terms of searching quality, robustness and efficiency.     

基于改进粒子群算法的锂电池模块装配序列规划

根据电动汽车中用到的锂电池模块装配特点,提出了模块内部零件的编码规则,并综合几何可行性、装配方向改变次数、装配连续性、装配工具改变次数和装配优先性五个指标设计了目标函数,结合改进的粒子群算法迭代更新得出最优装配序列组.通过电池模块实例对该算法进行了验证,结果表明本文提出的改进粒子群算法可以有效地求解该锂电池模块的装配序列规划问题.     

A hybrid particle swarm optimization approach for the job-shop scheduling problem

Managing multiple projects is a complex task. It involves the integration of varieties of resources and schedules. The researchers have proposed many tools and techniques for single project scheduling. Mathematical programming and heuristics are limited in application. In recent years non-traditional techniques are attempted for scheduling. This paper proposes the use of a heuristic and a genetic algorithm for scheduling a multi-project environment with an objective to minimize the makespan of the projects. The proposed method is validated with numerical examples and is found competent.     

基于粒子群算法的装配公差优化分配

装配公差分配是产品公差设计的重要组成部分.目前装配公差优化分配主要使用遗传算法.为了提高收敛速度,避免早熟收敛,提出了基于粒子群算法的装配公差优化分配方法.采用了基于实数的编码表示方法,以装配公差分配的优化目标函数作为评价函数,利用罚常数将约束条件并入评价函数中.一个实例的优化结果表明所提方法的收敛性、稳定性和算法效率均优于基于遗传算法的方法.     

A hybrid discrete particle swarm optimization algorithm for solving fuzzy job shop scheduling problem

In this paper, a hybrid algorithm combining particle swarm optimization (PSO) and tabu search (TS) is proposed to solve the job shop scheduling problem with fuzzy processing time. The object is to minimize the maximum fuzzy completion time, i.e., the fuzzy makespan. In the proposed algorithm, PSO performs the global search, i.e., the exploration phase, while TS conducts the local search, i.e., the exploitation process. The global best particle is used to direct other particles to optimal search space. Therefore, in the proposed algorithm, TS-based local search approach is applied to the global best particle to conduct find-grained exploitation. In order to share information among particles, one-point crossover operator is embedded in the hybrid algorithm. The proposed algorithm is tested on sets of the well-known benchmark instances. Through the analysis of experimental results, the highly effective performance of the proposed algorithm is shown against the best performing algorithms from the literature.     

Ant colony optimization for assembly sequence planning based on parameters optimization

Frontiers of Mechanical Engineering - As an important part of product design and manufacturing, assembly sequence planning (ASP) has a considerable impact on product quality and manufacturing...     

基于粒子群优化算法的小型足球机器人路径规划

为了解决足球机器人无法躲避动态障碍物和容易陷入局部极值的问题,在深入研究粒子群优化算法的基础上,提出了采用栅格法与粒子群优化算法相结合的路径规划算法。首先采用栅格法对小型足球机器人工作环境构造模型,再利用改进的粒子群优化算法进行最优路径搜索。该算法实现简单,收敛速度快,不易陷入局部极值,不仅能够满足足球机器人实时动态的路径规划要求,而且能满足不同环境下的路径规划要求。仿真实验表明,该方法可以很好地应用于足球机器人的路径规划中。     

基于离散粒子群优化算法的汽车发动机故障特征选择

最优特征选择属于组合优化范畴,针对汽车发动机机械故障特征选择问题,分析了冗余特征的存在对于故障分类器分类性能的影响,选择最优特征组合可以提高故障分类的正确率,提出基于离散粒子群算法的特征优化组合算法,利用BP神经网络评价特征优化的性能,并将其应用到汽车发动机曲轴轴承磨损故障诊断中.实验结果表明,与遗传算法相比,基于离散粒子群算法的特征优化算法优化效率较高,分类正确率较高,优化后的特征集可以显著地提高故障分类器的分类性能.     

基于遗传算法的飞机装配序列规划优化方法

在基于割集的装配顺序规划方法中,随着零件的增加,装配序列数呈指数增加,由此导致算法的组合爆炸问题。为此,提出了基于．遗传算法的装配序列规划优化方法,研究了零件装配信息的基因组表示方法,将参与装配的零件的基因组排列成序,实现装配顺序的染色体表示。采用模糊集理论建立了适应度函数,并对装配序列进行评价和优化。以某型飞机机翼产品为例,验证了技术的有效性。     

A particle swarm optimization approach to optimize component placement in printed circuit board assembly

The particle swarm optimization (PSO) approach has been successfully applied in continuous problems in practice. However, its application on the combinatorial search space is relatively new. The component assignment/sequencing problem in printed circuit board (PCB) has been verified as NP-hard (non-deterministic polynomial time). This paper presents an adaptive particle swarm optimization (APSO) approach to optimize the sequence of component placements on a PCB and the assignment of component types to feeders simultaneously for a pick-and-place machine with multiple heads. The objective of the problem is to minimize the total traveling distance (the traveling time) and the total change time of head nozzle. The APSO proposed in the paper incorporates three heuristics, namely, head assignment algorithm, reel grouping optimization and adaptive particle swarm optimization. Compared with the results obtained by other research, the performance of APSO is not worse than the performance of genetic algorithms (GA) in terms of the distance traveled by the placement head.     

A model-based approach to assembly sequence planning

This paper presents a systematic approach for automatic assembly sequence planning (ASP) by using an integrated framework of assembly relational model (ARM) and assembly process model (APM), which are established by object-oriented method. ARM, consisting of assembly, components and liaisons, is the static model of assembly to describe the geometric relationships between components in terms of contact, constraint and interference matrixes. APM, containing states and sub-processes, is the dynamic model of assembly to represent all potential alternatives in assembly process. An algorithmic procedure is also presented by which APM of an assembly can be constructed systematically by analyzing states from lower levels to higher levels. Through the procedure, all feasible sequences of a product can be generated, and the optimum one can be determined by further evaluating each sequence. To explain the approach more clearly, a practical assembly with five components is used as an illustration in the paper.     

基于粒子群和约束满足的钢轧一体化批量计划优化

在分析钢铁生产中的钢轧一体化批量计划编制问题基本特征的基础上给出了一体化编制策略,并建立了问题的约束满足优化模型。针对模型的NP难特性,提出了一种将改进离散粒子群算法、约束满足和邻域搜索相结合的混合算法。算法采用自然数矩阵编码,每个粒子代表其相应任务分配问题的解;在构造启发式解的基础上,利用邻域搜索方法计算粒子的适应值;为提高算法的收敛速度,利用约束满足技术生成初始化可行种群并修复迭代过程中产生的不可行解。基于企业实际生产数据的仿真实验结果验证了模型和算法的有效性。