Metaheuristic approaches to sequencing mixed-model fabrication/assembly systems with two objectives

This paper is concerned about the sequencing problems in pull production systems which are composed of one mixed-model assembly line and one flexible fabrication flow line with limited intermediate buffers. Two objectives are considered simultaneously: minimizing the total variation in parts consumption in the assembly line and minimizing the makespan in the fabrication line. The mathematical models are presented. Since the problem is Non-deterministic Polynomial-hard (NP-hard), a multiobjective genetic algorithm is proposed for solving the models, in which a method to generate the production sequence for the fabrication line from the production sequence for the assembly line is put forward, and Pareto ranking method and sharing function method are employed to evaluate the individuals' fitness. The feasibility and efficiency of the multiobjective genetic algorithm is shown by computational comparison with an adaptive genetic algorithm and a multiobjective simulated annealing.     

Solving a multi-objective mixed-model assembly line sequencing problem by a fuzzy goal programming approach

This paper presents a fuzzy goal programming approach to solve a multi-objective mixed-model assembly line sequencing problem in a just-in-time production system. A mixed-model assembly line is a type of production line capable of diversified small-lot production and is able to promptly respond to sudden demand changes for a variety of models. Determining the sequence of introducing models to such an assembly line is of particular importance for the efficient implementation of just-in-time (JIT) systems. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. Because of conflicting objectives, we propose a fuzzy goal programming-based approach to solve the model. This approach is constructed based on the desirability of decision maker (DM) and tolerances considered on goal values. To illustrate the behavior of the proposed model, some of instances are solved optimally and computational results reported.     

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.     

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.     

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...     

混流装配线平衡分析与算法设计

为了设计合理可靠的混流装配线并有效地提高装配线的平衡率,分析了混流装配线的特点并设计了一种以工序对调为核心思想的新算法,该算法的逻辑结构较为简易且兼容性高,能够与现有的大部分投产排序方法相结合,对装配线做进一步平衡优化处理.具体地描述了新算法的原理和执行过程,最后将该算法应用到某厂的汽车车灯线束装配线上,结果验证了该方法的有效性和可行性.     

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...     

A new approach to solve a mixed-model assembly line with a bypass subline sequencing problem

To meet the diversification of customer??s preferences, mixed-model assembly lines are installed in many manufacturing plants. In some of them, a large variation exists in assembly times among different product types. The large variation reduces production efficiency and may cause a line stoppage. These variations can be reduced by installing a bypass subline which processes a portion of assembly operations of products with relatively longer assembly times. In spite of its significance, sequencing problem on bypass subline rarely has been discussed in the literature. This paper addresses a sequencing problem with a bypass subline with the goals of leveling the part usage rates and reducing line stoppages. A novel hybrid algorithm incorporating genetic algorithm and event-based procedure is developed to solve the problem. Efficiency of the proposed algorithm is demonstrated through solving several test problems and comparing the resulted solutions with optimal solutions obtained from an exhaustive enumeration method.     

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。     

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.     

Sequencing the mixed-model assembly line to minimize the total utility and idle costs with variable launching interval

A mixed-model assembly line (MMAL) is a type of a production line where a variety of products is assembled on it. A mixed-model assembly line problem involves not only solving the traditional problems of the assembly line design (i.e., determining the cycle time, the number and sequence of stations, and the balancing problem) but also determining the sequence of products in assembly line. The product sequencing has a high effect on the mixed-model assembly line efficiency. In this paper, we consider sequencing problem with a variable launching interval between products on the assembly line. A mathematical model is presented, which is capable to solve the small-sized ones of these problems. The considered problem involves two optimization problems (the sequencing problem and launching interval problem). Since this problem is strongly NP-hard, a hybrid metaheuristic algorithm based on the simulated annealing approach and a heuristic approach is developed. The heuristic approach (launching interval between products algorithm) is presented to solve the launching interval problem for each sequence. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm. Variable launching interval consideration in MMAL problem causes the higher complexity of this problem. However, this assumption improves the considered goals for this problem. Not only a power algorithm for MMAL is presented in this paper but also the effect of this assumption is discussed. Numerical experiments are used to evaluate the performance and effectiveness of the proposed algorithm.     

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.     

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.     

A hybrid genetic algorithm approach to mixed-model assembly line balancing

Assembly line balancing has been a focus of interest to academics in operation management for the last four decades. Mass production has saved huge costs for manufacturers in various industries for some time. With the growing trend of greater product variability and shorter life cycles, traditional mass production is being replaced in assembly lines. The current market is intensely competitive and consumer-centric. Mixed-model assembly lines are increasing in many industrial environments. This study deals with mixed-model assembly line balancing for n models, and uses a classical genetic algorithm approach to minimize the number of workstations. We also incorporated a hybrid genetic algorithm approach that used the solution from the modified ranked positional method for the initial solution to reduce the search space within the global space, thereby reducing search time. Several examples illustrate the approach. The software used for programming is C++ language .     

面向柔性定制的并行不等效客车混装线生产计划主从联合优化

针对客车制造过程中多条异构混装线之间加工能力、作业时间不等效的特征,提出面向柔性定制的并行不等效客车混装线生产计划模型。分析订单分解和投产排序的耦合关联机理;以产品紧急度、匹配度以及产线负荷为目标,建立以订单分解为主、投产排序为从的主从联合优化模型。针对模型特征提出一种结合Pareto前沿解的双层交互式遗传算法。为了提高遗传算法的性能,引入自适应调整方法对交叉概率和变异概率进行改进,并采用小生境技术保证种群多样性。利用客车混装线中的案例对提出的模型进行了验证,并与多阶段遗传算法以及企业的实际方案进行了比较。所提出的使用双层交互式遗传算法的模型可以真实地代表企业的实际情况,并最大限度地提高混装线的效率。     

Balancing and Scheduling Mixed-Model U-Shaped Production Lines

The production line considered in this paper is a U-shaped, mixed-model, asynchronous line where assembly and fabrication tasks are performed to produce a variety of different discrete products or models in a just-in-time (JIT) environment. Two important problems occur routinely with these lines. The first is the assignment of tasks to stations on the line—the line balancing problem—and the second is the selection of the sequence in which models will be produced—the model sequencing problem. The model sequence cannot be set independently of the line balance, because different models require different tasks and the same tasks have different completion times for different models, and, in the JIT environment, the model sequence cannot be set independently of the schedules of other lines and production facilities. JIT uses a pull rather than a push system of production control, which means that the model sequence at the U-shaped mixed-model final assembly line sets the schedules at the other production facilities. JIT requires these latter schedules to be level and this requirement imposes an additional constraint on the model sequence. The effect of these two conditions is to require that the line-balancing and model-sequencing problems be solved simultaneously. In this article, we model the joint problem and present a solution algorithm for solving instances of practical size.     

基于混合遗传算法的混合装配线排序问题研究

为使混合装配线有效运作,研究了混合装配线的生产排序问题。以装配线上各种零部件消耗速率均匀化和最小生产循环周期最短为优化目标,描述了多目标排序问题,并建立了优化模型。针对基本遗传算法在求解排序问题时的早熟收敛问题,提出一种改进混合遗传算法。该算法借助模拟退火算法思想对适应度尺度进行调整,使遗传进化初期削弱种群中个体适应度差异,而在遗传进化后期强化种群中个体适应度差异,以提高对最优解的搜索能力。同时,根据个体适应度自动调整遗传操作参数,既保存了种群中的优良个体,又不失个体的多样性。最后通过案例分析验证了算法的有效性。     

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。