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.     

混流汽车总装配线上的零部件优化配送方案研究

配送车每次配送的利用率大小是提高配送效率的主要因素,因此设计一种启发式算法使配送车一次装载的标准塑料周转箱尽可能多,提高配送车空间利用率.最后的仿真实验验证了上述配送方法的实用性,提出的零部件配送管理方法,只需使用较少的配送工人,同时降低了线旁库存,能为企业节省人力,降低生产成本.     

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.     

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.     

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 multi-objective genetic algorithm for solving cell formation problem using a fuzzy goal programming approach

A comprehensive multi-objective mathematical programming model is proposed in this paper to design a cellular manufacturing system. The model considers machine redundancy, production volume, processing time, cost of machines, sequence of operations, and alternative processing plans. A fuzzy goal programming approach is used to convert the proposed multi-objective model into a single-objective one. Due to NP-hard nature of the model, a genetic algorithm is developed for solving the proposed model. Performance of the proposed genetic algorithm is evaluated by adopting four problems from literature. The results indicate effectiveness and efficiency of the proposed algorithm in comparison with those obtained by Lingo and NSGA-II algorithm.     

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.     

Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.     

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...     

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 .     

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.     

基于遗传退火算法的装配线设计多目标优化方法

针对混装配线设计这一有约束的多目标优化问题,建立了数学模型。将基于Pareto的解的分级方法与Lp-范数形式的非线性机制相组合,构建了基于遗传退火算法多目标优化方法。重点阐述了个体编码、染色体检修、多目标处理机制等关键技术。设计了算法流程图,并开发了优化程序。该方法克服了加权和方法的不足,用模拟退火改善了遗传算法全局寻优性能。计算实例表明,随着迭代次数的增加,每代的非受控点逐渐收敛于Pareto最优边界,是一种混装线设计多目标优化的新方法。     

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...     

A fuzzy multi-objective programming for scheduling of weighted jobs on a single machine

This paper considers a classic model of weighted single machine scheduling problem and aims to improve it to a real-world application through fuzzy set theory. For this purpose, processing times and due dates of jobs are defined as fuzzy numbers. In the proposed model, two objectives are considered to be minimized: average tardiness and number of tardy jobs. The objectives are converted into fuzzy statement through fuzzy arithmetic. Because the problem is NP-hard, it is proposed to solve the model through three well-known meta-heuristic algorithms as Simulated Annealing, Tabu Search, and Genetic Algorithm albeit with some modifications. Comparative analysis of algorithms is attainable through different experimented results on generated benchmark problems with different sizes and difficulties. Efficiency of the developed algorithms is analyzed on the different versions of the model which come from the assumption of different parameters and/or components. Algorithms' behaviors to managerial insights are also considered in some sensitivity analysis experiments. The obtained results show the applicability of the proposed model in real-world scheduling problems.     

Optimization of workcell layouts in a mixed-model assembly line environment

Mixed-model assembly lines enable automotive manufacturers to build different vehicles using the same equipment and facility. They allow a more rapid response to changes in customer demand. However, the increased production flexibility creates new challenges regarding assembly line planning and balancing. Part of this problem is how to allocate stock within the workcells so that non-value added operations, such as walking and waiting, are minimized. This paper addresses the issue of workstation layout optimization. Different formulations can be used to minimize non-value added time depending on the level of sophistication desired. We present three different optimization formulations and give an example of an optimized workstation layout.