面向大规模定制的混流装配线平衡研究

为解决面向大规模定制的混流装配线的平衡问题,分析了这一类装配线的特点,并综合考虑工作站的数量、工作站的负荷及装配线效率三个因素,提出了面向大规模定制的混流装配线的平衡模型和优化装配线平衡的混合遗传算法.该算法将模拟退火算法和遗传算法相结合,采用了交叉概率和变异概率的自适应重构策略,有效避免了算法的早熟,增强了算法全局寻优能力.实例仿真计算表明,该算法比标准的遗传算法和模拟退火算法具有更高的求解质量和求解效率.     

求解多目标混流装配线平衡问题的VPS-PSO算法

为了更有效地减少工作站数目、提高装配线效率,提出了一种基于多目标混流装配线平衡问题的方法。针对混流装配线平衡问题,采用工作站损失指数、装配线损失效率和平滑指数的评价指标作为混流装配线平衡优化问题的适应度函数,在给定节拍和装配优先顺序的前提下建立多目标优化模型。提出一种基于变种群策略的改进粒子群(VPS-PSO)算法能有效地维持种群的多样性,提高粒子群算法的全局搜索寻优能力。案例表明,该算法相对于PSO算法具有更好的寻优能力和求解效率,可以更高效地得到合理的装配线平衡方案。     

基于改进粒子群优化算法的混流装配线演进平衡

随着产品需求的多样化、装配工艺及技术进步、设备更新等动态变化,装配线平衡方案需不断调整,甚至重新规划与演进平衡。为了探究上述因素对混流装配线演进平衡的影响,提出了实现装配线演进平衡的方法,建立了以最小化装配线的生产节拍、站间平滑指数、演进平衡调整成本为优化目标的混流装配线演进平衡数学模型,并通过改进粒子群优化算法进行优化。在该算法中,为增加粒子的多样性和搜索能力,克服传统粒子群优化算法快速收敛等问题,以粒子进化的成功率来更新算法中的惯性因子,将群体中非最优粒子中的有利信息迁移到群体中的最优粒子上,从而加快算法的搜索速度。结合某企业的生产实例验证了该方法的有效性和可行性。     

基于装配关系复杂性测度的多目标混流装配线平衡优化

在混流装配线中,各工作站装配关系复杂性会影响作业效率和工人负荷.基于信息熵对装配关系复杂性进行度量,以最小化生产节拍、装配线平滑指数和装配关系复杂性平滑指数为优化目标,建立多目标混流装配线平衡优化模型,运用第二代非支配排序遗传算法(NSGA 2)设计求解,并结合某企业混流装配线实例验证方法的有效性.研究结果表明该优化方...     

基于启发式算法的农机混流装配线平衡分析与优化

针对某农机企业装配车间生产效率低下的问题,以玉米机装配线为研究对象,对其构建和优化混流装配线进行了研究.首先计算了玉米机不同型号产品构建混流装配线的综合作业时间,绘制了作业优先顺序图,然后运用启发式算法对该混流装配线的平衡问题进行了分析与优化,最后运用Flexsim仿真软件对混流装配线进行了动态仿真,以此验证了构建的混...     

基于混合粒子群算法的混流装配线投产顺序研究及仿真

针对混流装配线投产顺序的优化问题,提出两个目标函数.针于这种多目标优化问题,提出了一种混合粒子群算法.该算法结合最优解评估选取方法和遗传算法编码和交叉的思想,对粒子的全局极值和个体极值的选取做了改进.给出发动机混流装配线的一个实例,用该算法求投产顺序,按照求得的投产顺序在AutoMod仿真平台上建立仿真模型.通过仿真运行,证明了该算法求解混流装配线投产顺序的有效性以及AutoMod仿真混流装配线投产顺序的可行性.     

电子液压制动控制单元的混流装配线平衡优化研究

针对国内某汽车电子液压制动控制单元的混流装配线存在工作站数目过多、生产节拍不合理、作业负荷不均衡的问题,对该混流装配线进行了数学建模、自适应遗传算法求解和分析研究.首先,介绍了混流装配线平衡的基本概念,以及汽车电子液压制动控制单元的混流装配线;然后,在传统混流装配线平衡的基础上,综合考虑了工作站的数目最少、生产节拍最小...     

采用遗传算法的混流装配线平衡多目标优化

针对混流装配线操作人数已知、生产节拍待定情况下的平衡问题,研究了混流装配线第二类平衡问题的优化方法,以均衡各工作站的加权平均负荷、最小化生产节拍为优化目标,建立了求解加权平均负荷与生产节拍加权和的数学模型,并运用遗传算法对其进行求解.各优化目标数量级的统一,简化了算法求解过程,在保证解的有效性的同时提高了算法求解效率.算例分析验证了算法的有效性.     

以成本优化为平台的混流装配线排序

随着客户需求的日益多样化,混流装配线在工业生产中得到越来越多的应用.为了降低混流装配线的闲置和超载现象带来的成本浪费,在分析混流装配线的闲置和超载成本的基础上,提出一种最小化闲置和超载成本的排序模型,并应用粒子群算法来进行优化.优化综合考虑了粒子群算法中惯性权值的关键作用,给出了一种模糊自适应的惯性权值调整方法.最后将优化模型及算法应用到具体的混流装配线,验证了该方法的有效性.这种排序方法可以降低企业的生产成本,从而提高企业的效益,在企业内具有实际的应用价值.     

基于遗传算法的直线型和U型混流装配线平衡问题研究

针对混流装配线上不同产品作业时间差异导致的工作站瞬时负荷不均衡问题,提出了一种改进的直线型和U型混流装配线多目标平衡方法,并以装配线平衡率、平滑指数作为平衡效果的评价指标。综合考虑工序分配约束、工作站约束、优先关系约束和节拍约束等约束条件,同时兼顾工作站数最小和各工作站内不同品种产品负荷均衡2个目标函数,分别建立直线型和U型混流装配线多目标平衡优化模型。采用遗传算法作为优化算法,并在变异环节加入强制规则,使得变异过程能够朝着规定方向进行,以提高可行解的比例。最后通过对比算例分析结果,表明所提方法能够快速有效求解直线型和U型混流装配线平衡问题,可以为企业在直线型和U型混流装配线规划阶段制定平衡方案提供参考。     

基于数字孪生的混流装配线资源配置优化

针对混流装配线资源配置优化效率低、时效性差、动态响应能力不足等问题,提出基于数字孪生的混流装配线资源配置优化方法。构建了物理装配车间、虚拟装配车间和生产计划系统协同工作的资源配置优化数字孪生模型,阐述了基于分段式解耦和多目标优化相结合的资源配置优化实现过程。将该方法应用于某企业混流装配线,结果表明优化后的装配线平衡率由64.15%提升至81.84%,厂内日产量增加28.35%。所提方法是实现混流装配线资源配置优化的一种有效的新途径。     

Sequencing Mixed-model Production Systems by Modified Multi-objective Genetic Algorithms

As two independent problems,scheduling for parts fabrication line and sequencing for mixed-model assembly line have been addressed respectively by many researchers.However,these two problems should be considered simultaneously to improve the efficiency of the whole fabrication/assembly systems.By far,little research effort is devoted to sequencing problems for mixed-model fabrication/assembly systems.This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line with limited intermediate buffers and two flexible parts fabrication flow lines with identical parallel machines and limited intermediate buffers.Two objectives are considered simultaneously:minimizing the total variation in parts consumption in the assembly line and minimizing the total makespan cost in the fabrication/assembly system.The integrated optimization framework,mathematical models and the method to construct the complete schedules for the fabrication lines according to the production sequences for the first stage in fabrication lines are presented.Since the above problems are non-deterministic polynomial-hard(NP-hard),a modified multi-objective genetic algorithm is proposed for solving the models,in which a method to generate the production sequences for the fabrication lines from the production sequences for the assembly line and a method to generate the initial population are put forward,new selection,crossover and mutation operators are designed,and Pareto ranking method and sharing function method are employed to evaluate the individuals' fitness.The feasibility and efficiency of the multi-objective genetic algorithm is shown by computational comparison with a multi-objective simulated annealing algorithm.The sequencing problems for mixed-model production systems can be solved effectively by the proposed modified multi-objective genetic algorithm.     

Sequencing mixed-model assembly lines by considering feeding lines

A mixed-model assembly line is a type of production line where different models of a product are assembled on. Mixed-model assembly lines can respond to unanticipated changes in product demands quickly without keeping so many inventories. Designing mixed-model assembly line involves solving the traditional problems of the assembly line design (consists of balancing problem, determining cycle time, and the number and sequence of stations) in addition of determining the sequence of products in assembly line. The main goal of this paper is presenting a method in order to determine the sequence of products in mixed-model assembly line by considering Just-in-Time systems. Moreover, supplying some required components from feeding lines is considered. A mathematical model is presented which is capable of specifying the sequence of products in the mixed-model assembly line by considering main criteria and keeping feeding lines balanced. Mathematical model can be used for solving small-size problems. Because the combinatorial nature of sequencing problems typically provides an intractable search space for problems of “real world” size, the search heuristics of simulated annealing and ant colony algorithms are presented and used to find solutions for several problem sets. Experimentations show that the simulated annealing approach outperforms the ant colony approach in objective function performance.     

A novel two-stage genetic algorithm for a mixed-model U-line balancing problem with duplicated tasks

A widespread supposition on mixed-model assembly line-balancing problems assigns a task, which is shared between two or more models to a single station. Bukchin and Rabinowitch (European Journal of Operational Research, 174:492–508, 2006) relaxed the restriction for mixed-model straight-line assembly line problems and allowed tasks common to multiple models to be assigned to different stations, called task duplication. In this paper, considering the same relaxation but for mixed-model U-shaped assembly lines, a novel genetic algorithm (GA) approach for solving large-scale problems is developed. Although superiorities of U-shaped assembly lines over straight lines have been discussed in several articles, this paper makes the advantage more tangible by providing a quantitative example. This paper also presents a novel two-stage genetic algorithm which is fittingly devised for solving the new proposed model. In order to evaluate the effectiveness of the GA, one small-scale and one medium-scale problem are solved using both the proposed GA and Lingo 8.0 software, and the obtained outcomes are compared. The computational results indicate that the GA is capable of providing high-quality solutions for small- and medium-scale problems in negligible central processing unit (CPU) times. It is worth mentioning that, for large-scale problems, such as Kim and Arcus test problems, no analogous results for those obtained by our proposed GA exist. To conclude, it can be said that the proposed GA performs well and is able to solve large-scale problems within acceptable CPU times.     

多品种混合型装配流水线的平衡设计

分析了混合型装配流水线的平衡设计与排序设计的关系，提出了系统求解混合型装配流水线平衡的并行设计方法为平衡，排序，反馈、采用协同进化算法进行优化，以期得到全局意义上的最优解，仿真分析的结果显示了方法的有效性。     

ATO的物料需求预测与混流装配线排序研究

研究了面向订单装配有效实现的关键技术——物料需求预测分析、混流装配线的排序。用假设检验实现物料需求分析,以保持各种零部件的消耗率均匀为目标函数,用遗传算法进行混流装配线排序计算,结果表明了该方法的有效性。     

Simulation of mixed-model PCB assembly lines with group setup and bypass conveyors

Recent technological innovations, namely the development of new solder pastes with increased tack life and the introduction of bypass conveyors, facilitate the use of mixed-model lines in printed circuit board (PCB) assembly. This paper presents a simulation study comparing PCB assembly lines operating in conventional batch and in mixed-model assembly mode. For the latter case, a novel group setup strategy is developed. In the first stage, component types and placement operations for each type of PCB are assigned to the machines in the assembly line with the objective of balancing the workload. In the second stage, PCB types with similar component requirements are clustered into setup families. In addition, the feeder-slot assignment for each setup family and the sequence of placement operations for each PCB type are determined. In the final stage, a cyclical mixed-model production schedule is established. The performance of the proposed solution approach is evaluated by means of a simulation study. Two alternative material flow systems are investigated: a serial conveyor which directly links one machine to the other and a flexible one that allows to bypass downstream machines in the line.     

Real options approach for a mixed-model assembly line sequencing problem

Mixed-model assembly lines are widely used in manufacturing. This can be attributed to increased product variety and potential just-in-time (JIT) benefits obtained by applying mixed-model assembly lines. Because of market demand volatility, the flexibility of such a line is increasingly becoming more important and, consequently, determining an accurate sequence is becoming more complex. In this paper, first, we use the real options approach to evaluate one specific type of flexibility, i.e., product-mix flexibility. This methodology is applied to determine the products’ quantity that must be satisfied by the mixed-model assembly line. Then, in order to determine a desired sequence, we consider three objectives simultaneously: (1) total utility work cost, (2) total production rate variation cost, and (3) total set-up cost. A nonlinear zero–one model is developed for the problem whose objective function is a weighted sum of the above-mentioned objectives. Moreover, two efficient metaheuristics, i.e., a genetic algorithm (GA) and a memetic algorithm (MA), are proposed. These solution methods are compared with the optimal solution method using Lingo 6 software over a set of randomly generated test problems. The computational results reveal that the proposed memetic algorithm performs better than the proposed genetic algorithm.     

基于遗传算法和仿真分析的混合装配线平衡问题研究

为兼顾混合装配线平均负荷平衡和瞬时负荷平衡,提出了一种综合运用遗传算法和仿真分析的混合装配线平衡问题的求解方法.首先,基于综合作业顺序图和多品种产品在每个作业任务上的平均作业时间,采用遗传算法求解混合装配线平衡问题,其优化目标是均衡各工作站平均作业时间;然后,对遗传算法求解的一组较优解,从瞬时负荷平衡方面进行仿真分析,其优化目标是最大化各工作站利用率;最后,综合两个优化目标确定混合装配线平衡问题的最优解.通过算例分析,验证了求解方法的有效性.