Heuristics for Mixed Model Assembly Line Balancing Problem with Sequencing

The growing global competition compels organizations to use many productivity improvement techniques. In this direction, assembly line balancing helps an organization to design its assembly line such that its balancing efficiency is maximized. If the organization assembles more than one model in the same line, then the objective is to maximize the average balancing efficiency of the models of the mixed model assembly line balancing problem. Maximization of average balancing efficiency of the models along with minimization of makespan of sequencing models forms a multi-objective function. This is a realistic objective function which combines the balancing efficiency and makespan. This assembly line balancing problem with multi-objective comes under combinatorial category. Hence, development of meta-heuristic is inevitable. In this paper, an attempt has been made to develop three genetic algorithms for the mixed model assembly line balancing problem such that the average balancing efficiency of the model is maximized and the makespan of sequencing the models is minimized. Finally, these three algorithms and another algorithm in literature modified to solve the mixed-model assembly line balancing problem are compared in terms of the stated multi-objective function using a randomly generated set of problems through a complete factorial experiment.

Sequencing of Mixed Model Assembly Lines Based on Improved Shuffled Frog Leaping Algorithm

Shuffled frog leaping algorithm( SFLA) was used to solve multi-objective sequencing problem of mixed model assembly line( MMAL). Local convergence can be avoided and optimal solution can be obtained to a certain extent. However,the multi-objective sequencing problem of MMAL is an non-deterministic polynomial hard( NP-hard) problem and the shortcomings are slow convergence rate and low precision. To solve the shortcomings for optimization objectives of minimizing total utility time and keeping average consumption rate of parts, a chaos differential evolution SFLA( CDESFLA) is proposed in this study. Because SFLA is easy to fall into local optimum,the evolution operator of differential evolution algorithms is introduced in SFLA as a local search strategy,and differential mutation operator is introduced in chaotic sequence to prevent premature convergence. The examples show that the proposed CDESFLA is better for convergence accuracy than SFLA,genetic algorithm( GA) and particle swarm optimization( PSO)

Mixed-Model U-Shaped Assembly Line Balancing Problems with Coincidence Memetic Algorithm

Mixed-model U-shaped assembly line balancing problems (MMUALBP) is known to be NP-hard resulting in it being nearly impossible to obtain an optimal solution for practical problems with deterministic algorithms. This paper pre-sents a new evolutionary method called combinatorial optimisation with coincidence algorithm (COIN) being applied to Type I problems of MMUALBP in a just-in-time production system. Three objectives are simultaneously considered;minimum number workstations, minimum work relatedness, and minimum workload smoothness. The variances of COIN are also proposed, i.e. CNSGA II, and COIN-MA. COIN and its variances are tested against a well-known algo-rithm namely non-dominated sorting genetic algorithm II (NSGA II) and MNSGA II (a memetic version of NSGA II). Experimental results showed that COIN outperformed NSGA II. In addition, although COIN-MA uses a marginal CPU time than CNSGA II, its other performances are dominated.

Genetic Algorithm for Concurrent Balancing of Mixed-Model Assembly Lines with Original Task Times of Models

The growing global competition compels manufacturing organizations to engage themselves in all productivity improvement activities. In this direction, the consideration of mixed-model assembly line balancing problem and implementing in industries plays a major role in improving organizational productivity. In this paper, the mixed model assembly line balancing problem with deterministic task times is considered. The authors made an attempt to develop a genetic algorithm for realistic design of the mixed-model assembly line balancing problem. The design is made using the originnal task times of the models, which is a realistic approach. Then, it is compared with the generally perceived design of the mixed-model assembly line balancing problem.

Solving Level Scheduling in Mixed Model Assembly Line by Simulated Annealing Method

This paper presents an application of the simulated annealing algorithm to solve level schedules in mixed model assembly line. Solving production sequences with both number of setups and material usage rates to the minimum rate will optimize the level schedule. Miltenburg algorithm (1989) is first used to get seed sequence to optimize further. For this the utility time of the line and setup time requirement on each station is considered. This seed sequence is optimized by simulated annealing. This investigation helps to understand the importance of utility in the assembly line. Up to 15 product sequences are taken and constructed by using randomizing method and find the objective function value for this. For a sequence optimization, a meta-heuristic seems much more promising to guide the search into feasible regions of the solution space. Simulated annealing is a stochastic local search meta-heuristic, which bases the acceptance of a modified neighboring solution on a probabilistic scheme inspired by thermal processes for obtaining low-energy states in heat baths. Experimental results show that the simulated annealing approach is favorable and competitive compared to Miltenburg’s constructive algorithm for the problems set considered. It is proposed to found 16,985 solutions, the time taken for computation is 23.47 to 130.35, and the simulated annealing improves 49.33% than Miltenberg.

Minimizing Products Rates Variation in Just-in-Time Mixed-Model Manufacturing

Mixed-Model assembly lines are often used in manufacturing based on just-in-time techniques. The effective utilization of these lines requires a schedule for assembling the different models be determined. The objective is to minimize the total deviation of actual production rates from the desired production rates. Mathematical method with the optimization algorithm is proposed here to solve this problem. To prove the efficiency of the proposed algorithm, a number of test problems are solved. The results show that the proposed algorithm is an efficient and effective algorithm which gives better results with the large problem sizes. This paper presents a practical procedure to minimize total product variation rates, and easy to use by practitioner.

Strategic Robust Mixed Model Assembly Line Balancing Based on Scenario Planning

Assembly line balancing involves assigning a series of task elements to uniform sequential stations with certain restrictions. Decision makers often discover that a task assignment which is optimal with respect to a deterministic or stochastic/fuzzy model yields quite poor performance in reality. In real environments, assembly line balancing robustness is a more appropriate decision selection guide. A robust model based on the α worst case scenario is developed to compensate for the drawbacks of traditional robust criteria. A robust genetic algorithm is used to solve the problem. Comprehensive computational experiments to study the effect of the solution procedure show that the model generates more flexible robust solutions. Careful tuning the value of α allows the decision maker to balance robustness and conservativeness of as- sembly line task element assignments.

Part Supply Method for Mixed-Model Assembly Lines with Decentralized Supermarkets

In-house part supply affects the efficiency of mixed-model assembly lines considerably. Hence, we propose a reliable Just-In-Time part supply strategy with the use of decentralized supermarkets. For a given production sequence and line layout, the proposed strategy schedules tow train routing and delivery problems jointly to minimize the number of employed town trains and the traveling time, while ensuring that stations never run out of parts. To solve this problem, a mathematical formulation is proposed for each sub-problem aiming at minimizing supply cost. Then, a dynamic programming algorithm for routing and a greedy algorithm for delivery are developed, both of which are of polynomial runtime. Finally, a computational study is implemented to validate the effectiveness of the strategy, and to investigate the effects of the delivery capacity of tow trains and storage capacity of stations on supply cost.

AN OPTIMAL KANBAN SYSTEM IN A MULTI-STAGE,MIXED-MODEL ASSEMBLY LINE

This paper studies the parameter design and the performance optimization of a Kanban system without stockouts in a multi-stage, mixed-model assembly line. The model of a Kanban system based on production processes is established by examining the relationship among the set-up time, the amount of work in process （WIP）, the capacity indicated by a Kanban, and the takt-time ratio. A novel method for optimizing performance on the premise of no stockouts is proposed. Empirical results show that the amount of WIP is reduced remarkably after optimization.

机器人人工拣选环境下混流装配线齐套物料配送优化

为了提高汽车制造企业配送的智能化水平、解决混流装配线齐套物料人工拣选效率低、成本高的问题,引入基于齐套物料配送策略的机器人人工拣选模式,通过优化自动拣选机器人、工人的配置数量及配送周期,使包含机器人使用成本、劳动力成本和在制品库存成本在内的总成本最小。为求解该配送优化问题,提出了改进量子蚁群算法,利用量子比特的叠加性增加种群多样性,避免算法陷入局部最优,同时设计了改进量子旋转门更新机制和基于差分进化操作的非最优个体优化策略,提高了算法收敛速度和寻优质量。最后,通过算例分析验证了模型的正确性与算法的有效性,并分析了拣选批量对总成本的影响。

考虑物料配送的混流装配线协同优化

针对混流装配线的平衡排序和物料配送的协同问题,提出了一种装配线同步的协同配送策略,在此基础上以平衡工位负载、最小化最大完工时间和减少物料配送成本为目标,建立了考虑物料配送的协同优化模型。利用改进的非支配排序遗传算法(INSGA)对模型进行求解,基于模型的三层编码设计保证了解集的合法性和完整性。提出的多层编码交叉算子和变异算子,在提高种群多样性的同时也加快了算法的收敛速度。为避免算法陷入局部最优,重新设计了拥挤度距离,并改进了精英选择策略。此外,基于竞争机制,设计了一种舒适度竞争算子,便于在Pareto最优解集中确定具体方案。最后,通过标准算例和具体案例的求解,证明了改进算法的优越性和模型的有效性,案例的优化结果表示,装配线的完工时间缩短了近20%,物料配送成本节省了30%以上,取得了较好的优化效果。

混流装配线负荷平衡与投产排序的优化研究

介绍了混流装配线的最新进展情况 ,阐述了混流装配线负荷平衡和投产排序问题之间的关系 .说明了混流装配线负荷平衡的分类、目标函数及求解方法 ;建立了排序问题的模型 ,分析了三种常用的排序目标函数和各种求解算法 .本文指出了混流装配线应进一步研究的问题及未来的发展方向 .

基于多目标猫群算法的混流装配线排序问题

针对现实混流装配线上各工作站内设备闲置/超载的成本不同的问题,在传统的最小化闲置/辅助工作总成本目标的基础上,考虑不同工作站内设备闲置/超载成本的差异,建立了以改进的最小化工作站闲置/超载总成本、产品变化率和产品切换总时间为目标的多目标优化模型,并设计一种改进多目标猫群优化算法进行求解。提出一种基于线性混合比率的猫行为模式选择方法,以提高算法前期的全局搜索能力和后期的局部寻优能力;提出能生成分布广泛的候选个体、基于多样化搜寻算子的改进搜寻模式,拓展算法的搜索空间,提高算法的全局搜索能力。运用基准实例对所提算法与第二代非支配排序遗传算法、多目标粒子群算法、第二代强度Pareto进化算法进行比较,结果表明所提算法在解的收敛性、分布性和Pareto解的搜索能力上均具有优势。将该算法用于求解某实例企业的混流装配线排序问题,为车间调度人员的决策提供了多样化的选择,且优于车间已有方法的求解结果。

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。

基于改进遗传算法的混合装配生产线平衡问题研究

文章针对传统混合装配生产线缺乏对一个工位多个产品的研究,建立了一个工位多个产品的装配生产线平衡问题模型;以汽车主减速器装配生产线为例,对模型进行分析建模,设计了基于激素调节机制的改进遗传算法的适应度函数、选择、交叉及变异算子,对一工位多产品的混合装配生产线平衡问题模型进行求解;利用Matlab软件编写仿真算法程序对模型进行仿真,在一定程度上验证了模型的可行性和算法的有效性。

基于改进遗传算法的混流装配线的优化设计

兼顾混流装配线的负荷平衡和投产排序两个方面,建立了以最小化闲置与超载时间之和为优化目标的优化模型,优化目标包括了一次投产循环中,各工位上最后一个产品可能引起的超载时间,从而有效地保证了混流装配线装配过程的连续性。设计了一种引入小生境技术的改进遗传算法,对混流装配线进行了协同优化设计。实例研究表明:引入小生境技术的改进遗传算法在收敛速度和收敛可靠性方面均优于简单遗传算法和保留最佳个体的简单遗传算法;在给定等长工作站个数的情况下,混流装配线的闲置与超载时间之和随各工作站长度的增加而减小,最后趋于收敛。

基于改进分散搜索的混流装配线排序问题

针对混流装配线生产效率问题,设计了以最小化工作站的闲置与超载时间和最小化总调整变换时间为优化目标的数学模型。为得到高质量且平稳性好的解,提出了适应该模型求解的改进分散搜索算法。该算法首次应用一种基于遗传理论的多样性初始解生成法,不但保证了初始种群的多样性,而且为参考集提供了高质量的初始解。设计了参考集生成更新方法、子集产生方法、子集合并方法和优化解方法等多种机制,既保持了种群的多样性,又寻求到了质量较高的满意解。通过某企业混流装配线实例,验证了所设计算法的有效性。

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 ) .

基于混合粒子群算法求解多目标混流装配线排序

针对生产调度中的多目标混流装配线排序问题,建立以最小化超载时间、产品变化率与总切换时间为优化目标的数学模型,并提出一种改进的多目标粒子群算法求解。该算法采用基于工件的编码方式,并提出新的解码方法;应用Pareto排序和小生境数评价个体,在此基础上形成了一种新的适应度函数。在个体最优解的更新中,为避免最优解丢失,对非支配粒子与支配粒子采用差异化方法更新。此外,运用两种策略解决粒子群算法过早收敛的问题:在个体最优解的更新中引入模拟退火思想,并将全局最优解的选择扩大到整个种群。通过数值算例研究了算法的收敛性、分布性和执行效率,结果表明了所提算法的优越性。

考虑生产线平衡的多目标混流装配线排程问题研究

为使生产多品种小批量的混流装配线高效运作,研究了混流装配线的排序问题。首先建立以产品变化率、超载时间和总切换时间最小化为优化目标的数学模型。其次用该模型在离散系统仿真软件Plant Simulation平台上对生产过程进行仿真,并结合总加工时间最小化目标,采用遗传算法求解混流装配线的排序问题得到一组满意解;最后从装配线平衡角度,针对遗传算法得到的一组较优解进行分析,得到满足目标的最优解。通过实例,验证了该方法的可行性和有效性。