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.     

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.     

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。     

基于改进遗传算法的双边装配线平衡

针对目前研究较少的双边装配线平衡问题,研究了双边装配线的特点及其对平衡的特殊要求,建立了双边装配线平衡问题的数学模型.根据双边装配中任务具有操作方位约束,以及工位上分配任务的操作顺序与平衡结果直接相关等特点,提出了相应的符合问题特性的遗传算法.该算法采用基于序列、任务及其分配方位组合的编码方法,运用可行的交叉与变异算子,使搜索过程仅在可行解空间内进行,提高了效率.算例结果验证了算法的有效性.     

给定序列的混合品种装配生产线平衡算法

针对任意给定序列的混合品种装配生产线平衡问题建立了数学模型,模型中假定不同品种之间可以具有不同的偏序结构和关联任务。为了获得最优的产出效率,模型的目标函数是品种负荷波动产生的剩余工作总量最小。由于该装配生产线平衡问题是NP-hard问题,开发了相应的遗传算法。最后用一个实例来说明算法的有效性。     

Multi-objective artificial bee colony algorithm for simultaneous sequencing and balancing of mixed model assembly line

In recent years, mixed model assembly lines are gaining popularity to produce a variety of models on the single-model assembly lines. Mixed model assembly lines have two types of problems which include sequencing of different models on the line and balancing of assembly line. These two problems collectively affect the performance of assembly lines, and therefore, current research is aimed to balance the workload of different models on each station, to reduce the deviation of workload of a station from the average workload of all the stations and to minimize the total flow time of models on different stations simultaneously. A multi-objective artificial bee colony (multi-ABC) algorithm for simultaneous sequencing and balancing problem with Pareto concepts and local search mechanism is presented. Two kinds of mixed model assembly line problems are analysed. For the first and second problems, each model task time data and precedence relation data are taken from standard assembly line problems, from operation research library (ORL) and from a truck manufacturing company in China, respectively. Both problems are solved using the proposed multi-ABC algorithm on two different demand scenarios of models, and the results are compared against the results obtained from a famous algorithm in the literature, i.e. non-dominated sorting genetic algorithm (NSGA) II. Computational results of the selected problems indicate that the proposed multi-ABC algorithm outperforms NSGA II and gives better Pareto solutions for the selected problems on different demand scenarios of models.     

[工艺规划与装配线平衡]基于果蝇算法的第二类装配线平衡问题

根据第二类装配线平衡问题的特点,兼顾生产节拍最小和工作负荷均衡,建立了多目标研究模型,运用果蝇算法对标杆案例进行求解,并通过MATLAB进行了仿真研究。将果蝇算法优化结果与标杆案例中自适应遗传算法求解的结果进行对比可知,生产节拍缩短,装配线平衡率进一步提升,且各工作站的负荷更加均衡,从而验证了果蝇算法求解第二类装配线平衡问题的有效性,果蝇算法在获取全局最优解的能力上比自适应遗传算法更强。     

A mathematical model and ant colony algorithm for multi-manned assembly line balancing problem

In real-world assembly lines, that the size of the product is large (e.g., automotive industry), usually there are multi-manned workstations where a group of workers simultaneously perform different operations on the same individual product. This paper presents a mixed integer programming model to solve the balancing problem of the multi-manned assembly lines optimally. This model minimizes the total number of workers on the line as the first objective and the number of opened multi-manned workstations as the second one. Since this problem is well known as NP (nondeterministic polynomial-time)-hard, a heuristic approach based on the ant colony optimization approach is developed to solve the medium- and large-size scales of this problem. In the proposed algorithm, each ant tries to allocate given tasks to multi-manned workstations in order to build a balancing solution for the assembly line balancing problems by considering the precedence relations, multi-manned assembly line configuration, task times, and cycle time constraints. Through computational experiments, the performance of the proposed ACO is compared with some existing heuristic on various problem instances. The experimental results validate the effectiveness and efficiency of the proposed algorithm.     

A DEA approach for comparing solution efficiency in U-line balancing problem using goal programming

Line balancing problem plays an important role in the decision making process to increase efficiency and productivity. Recently, U-shaped layout in many production lines has replaced the traditional straight line layout using just-in-time concept. Here, we propose a model, using multi-objective decision making approach to the U-shaped line balancing problem, to offer enhanced decision maker flexibility, by allowing for conflicting goals. The assembly line operation efficiency is the most significant aim in our study, and this efficiency relates to management of resources and the solution of line balancing problem. First, the U-shaped line balancing problem is solved considering the model's goals. Then, the index function of assembly line balancing is determined and the efficiencies of the optimal solution outputs are evaluated using data envelopment analysis (DEA). Finally, the discrimination weakness and distribution of illogical weight in simple DEA models are resolved using a mixed method.     

汽车线束装配线平衡问题优化与仿真

简要介绍了汽车线束装配流程及特点。以生产节拍和完工率为优化目标,构建汽车线束装配线第二类平衡问题的数学模型,并以遗传算法求解该数学模型。以某型号线束装配线为例,利用Plant-simulation仿真软件构建模型仿真优化,缩短了生产节拍,提高了完工率,验证了该数学模型的有效性,为汽车线束装配线工序任务分配提供了一个解决方案。     

基于混合优化算法的装配线平衡问题

为求解给定装配线生产节拍、最大化装配效率的装配线平衡问题,根据装配线的特点和平衡优化需求,分析了装配作业顺序、站位数量等因素对装配线站位内作业分配的影响,综合考虑装配线平衡率和平滑系数,建立了装配线平衡问题数学模型,并设计了 一种结合遗传算法(Genetic Algorithm,GA)、蚁群算法(Ant Colony ...     

Scheduling of an assembly line with a multi-objective genetic algorithm

Scheduling problems are difficult combinatorial problems because of the extremely large search space of possible solutions and the large number of local optima that arise. A multi-objective genetic algorithm is presented as an intelligent algorithm for scheduling of the mixed-model assembly line in this paper. The Pareto ranking method and distance-dispersed approach are employed to evaluate the fitness of the individuals. The computational results show that the proposed multi-objective genetic algorithm is quite effective.     

用基于作业序列的遗传算法求解装配线平衡问题

给出了一个用于求解装配线平衡问题的基于作业序列的遗传算法。该算法只在可行作业序列子空间进行搜索 ,具有极高的搜索效率 ,同时又保留了一般遗传算法的并行和随机搜索能力。实例验证 ,结果较优。     

基于改进蚁群算法的汽车混流装配调度模型求解

针对某汽车总装车间混流装配过程涉及大量人工以及人机协同操作而导致工位过载、整车装配质量无法得到保证的问题,建立了瓶颈选装工位负载平衡化、考虑换装与提前作业时间的加工滞后次数最小化的分层序列双目标优化模型,同时设计了一种改进蚁群算法.该算法在信息素全局更新以及概率转移规则过程中,使用一种特定启发式函数,并更改迭代过程中最...     

面向混批加工的装夹方案选择与线平衡集成优化方法

针对一类多种箱体类同族零件混批加工的装夹方案选择与线平衡问题,提出一种集成优化方法。考虑操作优先关系和生产线产能约束,以节拍、夹具种类、平衡率和整线平滑系数为优化目标,分析了不同装夹组合下的各零件生产指标。引入多零件批量比重系数,并采用改进遗传算法对模型进行求解。最后,以某企业两缸体混批生产线为例,验证了该方法的有效性和高效性。     

Balancing of stochastic U-type assembly lines: an imperialist competitive algorithm

Recently, there is a growing interest in the industry to replace traditional straight assembly lines with U-shaped lines for more flexibility and higher productivity. Due to mathematical and computational complexity, assembly line balancing problems are known to be NP hard in nature. Therefore, many meta-heuristics have been proposed to find optimal solution for these problems. This paper presents a new hybrid evolutionary algorithm to solve stochastic U-type assembly line balancing problems, with the aim of minimizing the number of work stations, idle time at each station, and non-completion probabilities of each station (probability of the station time exceeding cycle time). The proposed algorithm is a combination of computer method for sequencing operations for assembly lines (COMSOAL), task assignment rules heuristic, and newly introduced imperialist competitive algorithm (ICA). Unlike the current evolutionary algorithms that are computer simulation of natural processes, ICA is inspired from socio-political evolution processes. Since appropriate design of the parameters has a significant impact on the algorithm efficiency, various parameters of the ICA are tuned by means of the Taguchi method. For the evaluation of the proposed hybrid algorithm, the performance of the proposed method is examined over benchmarks from the literature and compared with the best algorithm proposed before. Computational results demonstrate the efficiency and robustness of our algorithm.     

Stochastic U-line balancing using genetic algorithms

The advantages of U-type lines are very well known in industry. They offer improved productivity and quality, and are considered as one of the better techniques in implementing just-in-time (JIT) systems. There is a growing interest in the literature to organize traditional assembly lines as U-lines for improved performance. U-type assembly line balancing is an extension of the traditional line balancing problem, in which tasks can be assigned from both sides of the precedence diagram. Although there are many studies in the literature for the design of traditional straight assembly lines, the work on U-type lines is limited. Moreover, in most of the previous studies, task times are assumed to be deterministic. In this paper, a new multiple-rule-based genetic algorithm (GA) is proposed for balancing U-type assembly lines with stochastic task times.     

管控一体柔性装配线平衡问题研究

针对影响管控一体柔性装配线平衡因素的复杂性与多样性问题,提出一种基于实时分析工序装配柔性因子的管控策略和改进型遗传算法的优化处理方法。首先综合权衡管控一体柔性装配线不平衡的各类因素,为装配工序建立装配次数-预期时间函数与平衡模型;其次在管控决策台对平衡状态实时分析的基础上,构建了基于改进型遗传算法的优化处理模型,并给出基于动态工位分割算法和动态交叉、变异概率的算法改进步骤。     

遗传算法优化的汽车混流装配线投产排序决策

采用遗传算法,以车辆完成装配的总等待时间最少为目标,对汽车混流装配线进行投产排序优化,通过AutoMod仿真软件和遗传算法优化,得出了优化方案,其目标值减少69.5％,大大提高了企业生产效率,从而证明了遗传优化算法的有效性.     

Product family assembly line balancing based on an improved genetic algorithm

Product family assembly line (PFAL) is a mixed-model assembly line on which a family of similar products can be assembled at the same time. Aiming at the balance problem of PFAL, a balancing model for PFAL is established, and simultaneously an improved dual-population genetic algorithm is proposed. Firstly, through the characteristic analysis of PFAL, the tasks on PFAL are divided into three categories, namely the common, optional, and personality tasks. In addition, the correlation between the tasks is mainly considered. In the improved genetic algorithm, minimizing the number of stations, minimizing the load indexes between stations and within each station, and maximizing task-related degree are used as optimization objectives. In the initialization process, a method based on a TOP sorting algorithm is adopted for generating chromosomes. Furthermore, a new decoding algorithm is proposed to make up for the lack of the traditional decoding method, and individuals in the two populations are exchanged. Therefore, the search speed of the algorithm is accelerated, which shows good performance through classic tested problems. Finally, the effectiveness and feasibility of the method were validated by optimizing assembly line balancing of loaders.