非同步混装线负荷平衡与投产排序集成优化方法

为了提高非同步混流装配线的生产效率,提出了一种负荷平衡和投产排序的集成优化方法.首先通过综合优先图,将多品种装配线平衡问题转变成单品种平衡问题;通过设计组合优先启发式方法,以最小化生产节拍为目标,生成平衡方案;在平衡方案基础上,按照生产需求,采用考虑有限缓冲区容量的NEH(Nawaz-Enscore-Ham)算法,获得...     

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.     

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 .     

一类新的混流装配线排产优化模型

针对混流装配线的负荷均衡化问题,建立了最优化辅助工人数量的一类新的排产优化模型,分析了混流装配线的空间间隔约束和能力约束之间的关系,让明了空间间隔约束是满足工作站能力约束的充分条件.在此基础上给出了有辅助工人参与情况下的空间间隔约束不等式.对一些非线性约束进行了线性化处理,从而可以方便地求解模型.最后,通过仿真实例验让了该方法的可行性和有效性.     

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.     

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.     

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...     

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.     

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.     

Considering the conveyer stoppages in sequencing mixed-model assembly lines by a new fuzzy programming approach

Mixed-model production is the practice of assembling different and distinct models in a line without changeovers with responding to sudden demand changes for a variety of models. In this paper, we specify sequence of models to minimize conveyer stoppages. We assume that our lines are fixed and we cannot change the balance of the lines. When the condition of lines like setup cost and demand of each model change, it is important to specify the sequence for minimizing the conveyer stoppages without balancing the line again because the main lines are fixed. We consider three objective functions simultaneously: minimizing the variation in the actual and required production capacity of the line and minimizing the objectives which increase the chance of conveyer stoppage, including: (a) minimizing the total setup time, (b) minimizing the total production variation cost, and (c) minimizing the total utility work cost. Because of conflicting objectives, we propose the fuzzy goal programming-based approach to solve the model. Finally, we present an estimator for nearness of conveyer stoppages and study about affecting of sub-lines and changing the conveyer velocity in a station for reducing stoppages.     

Multi-criteria sequencing problem in mixed-model synchronous assembly lines

Mixed-model synchronous assembly line (MMSAL) is a type of assembly line that can be found in the production of large household appliances (e.g., washing machines, dryers, etc.). In this assembly line, products that are simultaneously processed on the line are transferred to the next station at the same time. In this paper, the sequencing problem of MMSAL with three objectives: (1) total idle cost, (2) total production rate variation cost, and (3) total setup cost is simultaneously studied. These three objectives are first weighted by their relative importance weights and a mathematical model is proposed for the problem. Since this problem is well known as non-deterministic polynomial-time hard class, a meta-heuristic algorithm based on simulated annealing is proposed to solve it. Performance of the proposed algorithm is compared against the Lingo 9 software. The computational results validate the efficiency and effectiveness of the proposed algorithm.     

基于禁忌粒子群算法的混流装配线排序研究

为了更好地解决开放式作业域的混流装配线排序问题,建立了以最小化超载时间与平顺化零部件消耗为优化目标的混流装配线排序问题数学模型,并提出了一种禁忌粒子群算法求解该排序问题。针对标准粒子群算法在算法后期搜索精度不足以及容易陷入局部最优不能跳出的缺陷,引入了禁忌搜索算法建立了对最优微粒的重搜索机制来提高算法跳出局部最优的能力,同时给出了禁忌算法中候选解、禁忌表长度、禁忌对象、藐视准则的设置方法,并采用了随机权重的惯性权重更新方式来平衡算法的全局和局部搜索能力,最后建立了禁忌粒子群的算法流程。通过比较禁忌粒子群算法与遗传算法的实例计算结果,验证了禁忌粒子群算法在求解开放式作业域的混流装配线排序问题中的有效性和优越性。     

基于改进遗传算法的混装线多目标优化

在考虑产品需求速率的前提下,提出了调整加工成本的新方法,建立了混流装配线平衡问题的多目标优化模型。设计了基于自然数序列和拓扑排序的改进遗传算法对模型进行求解,改进交叉、变异操作来保护优秀基因,提出了种群扩张机制。对经典问题的计算试验结果表明,改进遗传算法在降低生产节拍的同时能优化产品加工成本,在求解效率和求解质量方面有显著的成效。     

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.     

An overview of JIT

The term JIT (Just in Time), when used as a manufacturing control system, has become very confusing over the past few years. Everyone wants to claim they are users of JIT systems and because of this a variety of systems creations, all claiming themselves to be JIT systems, have sprouted up, none of which look anything like what Japan uses. This article attempts to clear up some of this confusion by showing four different variations of JIT and comparing them to the traditional MRP/MRP II environment.     

模拟退火算法在混流装配投产中的应用

对混流装配投产顺序的不同调度目标进行分析,建立混流装配投产的目标函数,着重讨论了模拟退火算法的原理及在混流装配投产中的应用流程,最后通过实例对算法进行验证.验证结果表明,模拟退火算法比常规的目标追迹法更有效,可以在生产中推广应用.     

一类多品种小批量混流制造系统作业组织模式与工件排序

为解决考虑搬运时间的混流制造系统的资源优化与调度问题,对作业组织模式与工件排序进行了深入研究。建立了以设备停机次数、工件搬运次数、搬运设备数量、搬运次数均衡程度作为第一、第二、第三和第四优化目标,以生产周期作为约束的多目标决策模型,利用该模型选择作业组织模式并确定最优排序方案。分别研究了用于求解该多目标决策模型的各分目标和约束:为不同类型批量工件建立加工时间模型;设计开始和结束搬运时间优化模型;给出平行移动模式的设备停机次数算法。开发了考虑搬运时间的混流制造系统软件程序,并设计了所需搬运设备情况的全局优化算法。通过分析和求解企业订单数据验证了所提方法的有效性。     

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.     

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...