汽车装配线生产计划与调度的集成优化方法

为提高汽车装配线的生产效率,优化资源配置,研究了汽车装配线生产计划和调度的集成优化问题,给出了该问题的混合整数规划模型.利用分枝定界算法和单纯型法求得问题的粗生产计划.通过将模拟退火算法和快速调度仿真相结合,探讨了一种新的启发式算法.然后基于已求得的粗生产计划,针对三种不同寻优组合论述了该算法的实现.将该算法应用于实际算例,仿真结果表明该算法对求解此类问题有着很好的效果.     

汽车同步装配线生产计划与调度集成优化汽车同步装配线生产计划与调度集成优化

为克服传统的"自顶向下"方式下生产计划与调度不协调的缺陷,针对汽车同步装配线,构造了生产计划与调度集成优化混合整数规划模型,并采用拉格朗日松弛法将其分解为批量计划及调度等子问题.将调度子问题转化为与时间相关的旅行商问题,并采用dynasearch算法求解.对于拉格朗日对偶问题,采用均衡方向策略法求解.仿真实验结果验证了模型及算法的有效性.     

基于改进烟花算法的随机装配线混流调度

针对随机装配线混流调度问题,分析作业时间随机波动对调度优化目标的影响,提出描述随机作业时间下工位闲置时间和超载时间有效数学期望的概念,并基于此构建以工位负荷均衡指数为优化目标的随机作业时间条件下的装配线混流调度优化模型.在标准烟花算法中引入精英策略、变邻域搜索等技术,设计一种改进烟花算法对模型进行求解,并结合测试算例和生产实例对随机装配线混流调度模型及求解算法进行验证,求解结果表明了模型和算法的有效性.     

面向航空发动机装配线的知识化制造系统重调度和自重构

为了解决航空发动机装配过程中存在的不确定返工问题,建立了面向航空发动机装配线的知识化制造系统(knowledgeable manufacturing system,KMS)重调度和班组自重构优化模型,提出装配线重调度及自重构集成优化算法.在算法调度层,证明了以加权完工成本为优化目标的工序排序性质,并对工序进行初始排序.定义了3种邻域结构,用变邻域搜索(variable neighborhood search,VNS)对工序在并行装配组上的指派问题和调度问题进行优化.在重构层,在不违背装配组装配技能约束的前提下利用装配线负载平衡原则对装配班组进行配置,并采用禁忌搜索(tabu search,TS)对班组配置进行优化.仿真实验结果表明了模型与算法的有效性.     

知识化制造环境下航空发动机装配线调度及自重构

航空发动机装配工序数量多、工序间装配约束复杂. 当产品需求变化时, 人工调整存在响应速度慢、装配效率低等问题. 以最小化产品完工成本、工序提前期惩罚成本及班组重构成本加权和为目标, 建立了航空发动机装配线调度和装配班组自重构优化模型. 提出一种新的基于工序局部最优排序的分解算法, 将调度问题分解为单个装配组上工序顺序优化问题. 设计了一种工序后向插入搜索策略. 最后提出装配线调度及自重构集成优化算法. 通过数值试验,验证了模型与算法的有效性.     

汽车装配车间生产计划调度系统的设计与开发

论述了汽车装配车间在企业顺利生产中的重要性,建立了车间生产计划与调度的同时优化模型.基于免疫算法.以概率1收敛的特点,选用免疫算法并将之进行适当组合得到交替式免疫算法来对该模型进行求解,以达到计划和调度同时优化的目的.在进行理论研究的基础上,根据南京某汽车厂的实际需求设计出了生产计划与调度系统方案,并使用组件化技术,开发出了基于.NET平台和B/S模式的汽车装配车间生产计划与调度系统,最终满足实际生产要求,达到了高效信息管理的目标.     

柔性作业车间生产计划与调度集成优化求解策略

针对柔性作业车间(Flexible job-shop, FJS)生产计划(Production planning, PP)与调度紧密衔接的特点, 建立了生产计划与调度集成优化模型. 模型综合考虑了安全库存、需求损失及工件加工路线柔性等方面因素. 提出了一种基于拉格朗日松弛(Lagrangian relaxation, LR)的分解算法, 将原问题分解为计划子问题与调度子问题. 针对松弛的生产计划子问题, 提出一种新的费用结构, 以保证生产计划决策与实际情况相符, 并设计了一种变量固定—松弛策略与滚动时域组合算法进行求解. 对于调度子问题中的加工路线柔性问题, 提出了一种新的机器选择策略. 通过数值实验验证了模型与算法的有效性.     

蚁群算法在混流装配线调度问题中的应用

混流装配线的调度问题是JIT生产方式中的一个重要问题， 蚁群算法具有通用性、 鲁棒性、并行搜索的优点，可以解决多种组合优化问题．本文利用新颖的蚁群算法解决了混 流装配线的调度问题，得到了较优的可行解(优于目标追随法、模拟退火算法和遗传算法)．     

带返工的定点装配车间生产计划与调度集成优化

针对定点装配车间不合格品返工导致生产计划及调度不可行的情况,提出一种基于返工延后处理的定点装配车间生产计划与调度集成优化方法.首先制定返工延后处理的粗生产计划;下达计划生产后,将每周期产生的不合格品放入缓冲区,在下周期初时重新调整生产计划并求解新计划下的最优调度,判断其是否满足装配班组负载率要求,不断交替迭代生产计划与调度直至达到计划与调度的均衡和优化.最后通过算例验证了所提出方法的可行性和有效性.     

短流程中小型钢铁企业生产调度优化

针对钢铁生产调度计划问题,研究并提出了短流程中小型钢铁企业生产调度计划的一种有效算法.短流程钢铁企业主要有炼钢(电炉)、连铸和热轧三个生产工序,中间在制品在线存储能力非常有限,三个工序的生产同步性为协调生产计划时的关键考虑因素.且每个生产过程必须批量运行.生产调度计划一般由分组和排序两部分组成,各工序有各自的判断准则,有时这些准则相互冲突.针对这些问题提出了一种有效的启发式算法,并由真实数据进行验算,结果表明算法合理有效.能够满足实际生产需要.     

汽车装配车间生产计划调度系统的设计与开发

论述了汽车装配车间在企业顺利生产中的重要性,建立了车间生产计划与调度的同时优化模型。基于免疫算法以概率1收敛的特点,选用免疫算法并将之进行适当组合得到交替式免疫算法来对该模型进行求解,以达到计划和调度同时优化的目的。在进行理论研究的基础上,根据南京某汽车厂的实际需求设计出了生产计划与调度系统方案,并使用组件化技术,开发出了基于．NET平台和B／S模式的汽车装配车间生产计划与调度系统,最终满足实际生产要求,达到了高效信息管理的目标。     

汽车装配线的可视化仿真研究

由于装配系统仿真对象复杂以及传统仿真方法难以建立装配系统的仿真模型,因此该文提出运用面向对象的理论建立汽车装配线的可视化虚拟仿真系统.文中详细论述了实现该仿真系统的关键技术和流程,接着根据离散事件系统的特点介绍了装配仿真系统的仿真策略,最后以某汽车总装车间为例建立了装配线的可视化虚拟仿真系统.通过可视化仿真实现了车间的布局设计和生产过程的动态仿真,并依据仿真结果确定生产线的技术参数以决策生产.     

数据驱动的汽车总装线建模与仿真技术研究

现代制造业的生产模式向柔性制造和客户化定制方向发展,为了提高制造系统的快速响应能力,实现快速仿真分析,提出基于数据驱动的建模与仿真方法,其具有建模周期短、模型可读性、可重用性较高的特点.利用IDEFIX建模方法建立模型的底层数据结构,基于Arena构建仿真模型生成器,利用已建好的数据模型提供的属性信息,自动生成目标系统...     

A hybrid multi-objective shuffled frog-leaping algorithm for a mixed-model assembly line sequencing problem

In this paper, a mixed-model assembly line (MMAL) sequencing problem is studied. This type of production system is used to manufacture multiple products along a single assembly line while maintaining the least possible inventories. With the growth in customers’ demand diversification, mixed-model assembly lines have gained increasing importance in the field of management. Among the available criteria used to judge a sequence in MMAL, the following three are taken into account: the minimization of total utility work, total production rate variation, and total setup cost. Due to the complexity of the problem, it is very difficult to obtain optimum solution for this kind of problems by means of traditional approaches. Therefore, a hybrid multi-objective algorithm based on shuffled frog-leaping algorithm (SFLA) and bacteria optimization (BO) are deployed. The performance of the proposed hybrid algorithm is then compared with three well-known genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed hybrid algorithm outperforms the existing genetic algorithms, significantly in large-sized problems.     

A hybrid genetic algorithm for multi-objective product plan selection problem with ASP and ALB

Facing current environment full of a variety of small quantity customized requests, enterprises must provide diversified products for speedy and effective responses to customers’ requests. Among multiple plans of product, both assembly sequence planning (ASP) and assembly line balance (ALB) must be taken into consideration for the selection of optimal product plan because assembly sequence and assembly line balance have significant impact on production efficiency. Considering different setup times among different assembly tasks, this issue is an NP-hard problem which cannot be easily solved by general method. In this study the multi-objective optimization mathematical model for the selection of product plan integrating ASP and ALB has been established. Introduced cases will be solved by the established model connecting to database statistics. The results show that the proposed Guided-modified weighted Pareto-based multi-objective genetic algorithm (G-WPMOGA) can effectively solve this difficult problem. The results of comparison among three different kinds of hybrid algorithms show that in terms of the issues of ASP and ALB for multiple plans, G-WPMOGA shows better problem-solving capability for four-objective optimization.     

A review of the current applications of genetic algorithms in assembly line balancing

Most of the problems involving the design and plan of manufacturing systems are combinatorial and NP-hard. A well-known manufacturing optimization problem is the assembly line balancing problem (ALBP). Due to the complexity of the problem, in recent years, a growing number of researchers have employed genetic algorithms. In this article, a survey has been conducted from the recent published literature on assembly line balancing including genetic algorithms. In particular, we have summarized the main specifications of the problems studied, the genetic algorithms suggested and the objective functions used in evaluating the performance of the genetic algorithms. Moreover, future research directions have been identified and are suggested.     

A comparative study of Multi-Objective Ant Colony Optimization algorithms for the Time and Space Assembly Line Balancing Problem

Assembly lines for mass manufacturing incrementally build production items by performing tasks on them while flowing between workstations. The configuration of an assembly line consists of assigning tasks to different workstations in order to optimize its operation subject to certain constraints such as the precedence relationships between the tasks. The operation of an assembly line can be optimized by minimizing two conflicting objectives, namely the number of workstations and the physical area these require. This configuration problem is an instance of the TSALBP, which is commonly found in the automotive industry. It is a hard combinatorial optimization problem to which finding the optimum solution might be infeasible or even impossible, but finding a good solution is still of great value to managers configuring the line. We adapt eight different Multi-Objective Ant Colony Optimization (MOACO) algorithms and compare their performance on ten well-known problem instances to solve such a complex problem. Experiments under different modalities show that the commonly used heuristic functions deteriorate the performance of the algorithms in time-limited scenarios due to the added computational cost. Moreover, even neglecting such a cost, the algorithms achieve a better performance without such heuristic functions. The algorithms are ranked according to three multi-objective indicators and the differences between the top-4 are further reviewed using statistical significance tests. Additionally, these four best performing MOACO algorithms are favourably compared with the Infeasibility Driven Evolutionary Algorithm (IDEA) designed specifically for industrial optimization problems.     

A multi-objective scatter search for a mixed-model assembly line sequencing problem

A mixed-model assembly line (MMAL) is a type of production line where a variety of product models similar to product characteristics are assembled. There is a set of criteria on which to judge sequences of product models in terms of the effective utilization of this line. In this paper, we consider three objectives, simultaneously: minimizing total utility work, total production rate variation, and total setup cost. A multi-objective sequencing problem and its mathematical formulation are described. Since this type of problem is NP-hard, a new multi-objective scatter search (MOSS) is designed for searching locally Pareto-optimal frontier for the problem. To validate the performance of the proposed algorithm, in terms of solution quality and diversity level, various test problems are made and the reliability of the proposed algorithm, based on some comparison metrics, is compared with three prominent multi-objective genetic algorithms, i.e. PS-NC GA, NSGA-II, and SPEA-II. The computational results show that the proposed MOSS outperforms the existing genetic algorithms, especially for the large-sized problems.     

Assembly line balancing and group working: A heuristic procedure for workers’ groups operating on the same product and workstation

In this paper, we examine an assembly line balancing problem that differs from the conventional one in the sense that there are multi-manned workstations, where workers’ groups simultaneously perform different assembly works on the same product and workstation. This situation requires that the product is of sufficient size, as for example in the automotive industry, so that the workers do not block each other during the assembly work. The proposed approach here results in shorter physical line length and production space utilization improvement, because the same number of workers can be allocated to fewer workstations. Moreover, the total effectiveness of the assembly line, in terms of idle time and production output rate, remains the same. A heuristic assembly line balancing procedure is thus developed and illustrated. Finally, experimental results of a real-life automobile assembly plant case and well-known problems from the literature indicate the effectiveness and applicability of the proposed approach in practice.