基于GA-SA的混合U型装配线平衡

在JIT生产系统中，混合U型装配线是一种能够满足市场多样化需求的柔性系统，章综合考虑作业元素的分配和产品的投产排序两个因素，建立了混合U型装配线的平衡模型，给出了人工智能算法的平衡方法，从全局优化的角度研究了混合U型装配线的平衡问题。     

具有工作站数量约束的多人工作站混合装配线平衡问题研究

针对装配线设计或改造过程中存在的因场地或成本原因导致的工作站数量不易变更的问题,研究了节拍已知情况下,具有工作站数量约束的多人工作站混合装配线平衡问题,建立以装配线总人数最小、工人负荷量标准差最小、各产品在各工作站装配时间与节拍之间的标准差最小为目标的数学模型,设计了一种结合差分进化的多目标混合遗传算法对该问题求解。通过案例计算以及与其他算法的对比分析表明,本文算法在收敛性和综合性能方面优于NSGAII和DEMO,在装配线人数和工人负荷标准差方面优于Roshani和Nezami提出的算法。     

多目标装配线平衡的优化算法

在生产制造系统中，装配线的平衡需要针对多个目标，传统的装配线平衡问题，优化单一目标，忽略了目标之间的联系。优化一个目标的同时，劣化了另外一个目标。章研究了多目标装配线平衡问题，探讨了这些目标之间的联系，设计了禁忌搜索算法求解多目标装配线的平衡问题。章从生产管理系统的角度优化多目标装配线平衡，与单一目标相比，具有显的改进。     

混合模式下U型装配线平衡问题的目标规划求解

装配线平衡对于提高装配效率和降低装配成本都有重要意义。为了满足顾客需求的多样化和减少成本，通常采用混合模式的装配线。本文针对混合模式下U型装配线平衡问题，提出一个目标规划法以求解该问题，量化示例表明所提方法是有效的。     

混合型装配线平衡问题求解方法研究

对混合型装配线平衡问题进行了描述和数学建模,提出一种启发式求解算法,求解目标是最小化工作站数目.为进一步优化求解结果,对启发式算法求解的结果进行仿真研究,分析各工作站的工作率、等待率和阻塞率,并以此为依据调整部分作业任务的分配,允许不同品种产品的相同作业任务安排在不同的工作站中,以对求解结果进行修正,进一步均衡各工作站的作业量.该求解方法既简化了求解过程,又兼顾到了系统的瞬时特性和作业任务的不可拆分性对求解结果的影响,实例分析验证了方法的有效性.     

随机混流U型拆卸线平衡排序问题多目标进化算法优化

针对混流U型拆卸线平衡排序问题,考虑拆卸时间不确定,建立了该问题最小拆卸线平均闲置率、尽早拆卸危害和高需求零部件、最小化平均方向改变次数的多目标优化模型,并提出一种基于分解和动态邻域搜索的混合多目标进化算法(Hybrid Multi-objective Evolutionary Algorithm Based on Decomposition, HMOEA/D)。该算法通过采用弹性任务分配策略、动态邻域结构和动态调整权重以保证解的可行性并搜索得到分布较好的非劣解集。最后,仿真求解实验设计技术(DOE)生成的测试算例,结果表明HMOEA/D较其它算法能得到更接近Pareto最优、分布更好的近似解集。     

基于物料超市的混流装配线准时化物料配送调度

针对混流装配线物料配送优化问题，结合新型的物料超市配送模式，提出了区别于传统周期性配送的准时化配送方法。首先，根据问题描述和相关假设构建了以线边库存最小化为目标的混合整数规划模型。然后，考虑到实际生产的连续性和复杂性，为求解大规模问题，设计出适用于问题的启发式算法，并与遗传算法进行比较，启发式算法能在更短时间内获得满意解。最后，在不同牵引拖车容量下，通过实例分析，与广泛应用的周期性配送进行比较，算例求解结果表明，准时化配送模型能够获得更低的库存水平，减少工作站的空间占用。     

面向绿色智能制造的高维多目标动态作业车间调度优化

在当前环境问题日益严峻情况下,绿色智能制造受到广泛关注。在动态柔性作业车间基础上考虑不同机器状态下的能耗情况、机器使用节能方法,构建以极小化总能耗、最大完工时间、机器总负荷和产品质量稳定性为目标的高维多目标绿色动态柔性作业车间调度模型,并设计改进的灰狼优化IMOGWO算法求解该问题。首先,采用反向学习初始化种群策略,以扩大种群多样性;然后,依据多目标问题和标准GWO算法的特点提出多级官员领导机制,并引入POX交叉和逆序变异算子;最后,改进精英保留策略用于多目标优化算法。为证明算法的有效性,设计两组仿真实验分别对三种算法进行比较。实验结果表明,运用本文改进的IMOGWO算法求解多目标问题有更好的收敛性和分布性。     

面向大规模定制的建筑工业化住宅混合装配线平衡优化研究

为解决大规模定制工业化住宅混合装配线平衡问题,以预制构件均匀消耗为目标建立了混合装配线生产优化模型,并基于目标追随法(GCA)和建筑工业化住宅的建造特点,提出相应的求解算法.通过所构建的优化模型及算法,可对随机输入的工业化装配顺序进行优化,进而获得预制构件消耗速率均匀的优化装配顺序.最后,通过案例分析验证了优化模型的有效性,优化结果表明优化后的生产序列的预制构件消耗更为趋近于平均消耗水平,能够获得更为均匀的预制构件消耗速率,进而提升工业化住宅项目的准时性及效率.     

煤炭码头船货匹配下泊位动态分配多目标优化模型及算法

本文针对输出型煤炭码头船货匹配下泊位动态分配问题,构建了堆场-取装线-泊位-船舶联合分配优化数学模型,并设计了采用仿真推演策略解码的遗传算法求解。首先,综合考虑船舶、泊位、堆场、取装线、煤种、航道开放时间和装船作业规则等要素,以船舶在港时间最短和作业效率最大为目标建立了相应的多约束多目标优化模型。然后,综合多目标优化、遗传算法以及仿真推演技术,设计了相应的遗传算法求解,包括:组合式编码、采用仿真推演策略的解码方法,追加了具有合法性检查的染色体生成算法,设计了采用多种策略的遗传操作等。最后实例表明,本算法的执行效率高而且优化效果好。     

Mathematical analysis and solutions for multi-objective line-cell conversion problem

The line-cell (or line-seru) conversion is an innovation of assembly system applied widely in the electronics industry. Its essence is tearing out an assembly line and adopting a mini-assembly unit, called seru (or Japanese style assembly cell). In this paper, we develop a multi-objective optimization model to investigate two line-cell conversion performances: the total throughput time (TTPT) and the total labor hours (TLH). We analyze the bi-objective model to find out its mathematical characteristics such as solution space, combinatorial complexity and non-convex properties, and others. Owing to the difficulties of the model, a non-dominated sorting genetic algorithm that can solve large size problems in a reasonable time is developed. To verify the reliability of the algorithm, solutions are compared with those obtained from the enumeration method. We find that the proposed genetic algorithm is useful and can get reliable solutions in most cases.     

Binary fuzzy goal programming approach to single model straight and U-shaped assembly line balancing

Assembly line balancing generally requires a set of acceptable solutions to the several conflicting objectives. In this study, a binary fuzzy goal programming approach is applied to assembly line balancing. Models for balancing straight and U-shaped assembly lines with fuzzy goals (the number of workstations and cycle time goals) are proposed. The binary fuzzy goal programming models are solved using the methodology introduced by Chang [Chang, C.T., 2007. Binary fuzzy goal programming. European Journal of Operational Research 180 (1), 29–37]. An illustrative example is presented to demonstrate the validity of the proposed models and to compare the performance of straight and U-shaped line configurations.     

An imperialist competitive algorithm for multi-objective U-type assembly line design

Many assembly lines are now being designed as U-type assembly lines rather than straight lines because of the pressure of the just-in-time (JIT) manufacturing concept. Since any type of an assembly line balancing problem is known to be NP-hard, there has been a growing tendency toward using evolutionary algorithms to solve such a hard problem. This paper proposes a new population-based evolutionary algorithm, namely imperialist competitive algorithm (ICA) inspired by the process of socio-political evolution, to address the multi-objective U-type assembly line balancing problem (UALBP). Two considered objectives are to minimize the line efficiency and minimize the variation of workload. Furthermore, the Taguchi design is applied to tune the effective parameters of the proposed ICA. To demonstrate the efficiency of the proposed algorithm, the associated results are compared against an efficient genetic algorithm (GA) in the literature over a large group of benchmarks taken from the literature. The computational results show that the proposed ICA outperforms GA.     

Bees Algorithm for constrained fuzzy multi-objective two-sided assembly line balancing problem

Bees Algorithm is one of the swarm intelligence based heuristics which tries to model natural behaviour of honey bees in food foraging and used to solve optimization problems. On the other hand, Two-sided Assembly Line Balancing Problem is a generalization of simple Assembly Line Balancing Problem where different assembly tasks are carried out on the same product in parallel at both left and right sides of the line. Two-sided assembly lines are generally employed for the assembly of large-sized products such as buses and trucks. Furthermore, many real life problems contain imprecise objectives and Fuzzy Multi-objective Programming gives an opportunity to handle such situations. In this study, Two-sided Assembly Line Balancing Problem is considered more realistically by employing positional, zoning and synchronous task constraints and by utilizing fuzzy approaches so as to maximize work slackness index and line efficiency, and minimize total balance delay. For solving this problem Bees Algorithm is used as a search mechanism for obtaining good solutions and extensive computational results are presented.     

A genetic algorithm for robotic assembly line balancing

Flexibility and automation in assembly lines can be achieved by the use of robots. The robotic assembly line balancing (RALB) problem is defined for robotic assembly line, where different robots may be assigned to the assembly tasks, and each robot needs different assembly times to perform a given task, because of its capabilities and specialization. The solution to the RALB problem includes an attempt for optimal assignment of robots to line stations and a balanced distribution of work between different stations. It aims at maximizing the production rate of the line. A genetic algorithm (GA) is used to find a solution to this problem. Two different procedures for adapting the GA to the RALB problem, by assigning robots with different capabilities to workstations are introduced: a recursive assignment procedure and a consecutive assignment procedure. The results of the GA are improved by a local optimization (hill climbing) work-piece exchange procedure. Tests conducted on a set of randomly generated problems, show that the Consecutive Assignment procedure achieves, in general, better solution quality (measured by average cycle time). Further tests are conducted to determine the best combination of parameters for the GA procedure. Comparison of the GA algorithm results with a truncated Branch and Bound algorithm for the RALB problem, demonstrates that the GA gives consistently better results.     

Environmental supply chain network design using multi-objective fuzzy mathematical programming

The concern about environmental impact of business activities has spurred an interest in designing environmentally conscious supply chains. This paper proposes a multi-objective fuzzy mathematical programming model for designing an environmental supply chain under inherent uncertainty of input data in such problem. The proposed model is able to consider the minimization of multiple environmental impacts beside the traditional cost minimization objective to make a fair balance between them. A life cycle assessment-based (LCA-based) method is applied to assess and quantify the environmental impact of different options for supply chain network configuration. Also, to solve the proposed multi-objective fuzzy optimization model, an interactive fuzzy solution approach is developed. A real industrial case is used to demonstrate the significance and applicability of the developed fuzzy optimization model as well as the usefulness of the proposed solution approach.     

A survey on problems and methods in generalized assembly line balancing

Assembly lines are traditional and still attractive means of mass and large-scale series production. Since the early times of Henry Ford several developments took place which changed assembly lines from strictly paced and straight single-model lines to more flexible systems including, among others, lines with parallel work stations or tasks, customer-oriented mixed-model and multi-model lines, U-shaped lines as well as unpaced lines with intermediate buffers.In any case, an important decision problem, called assembly line balancing problem, arises and has to be solved when (re-) configuring an assembly line. It consists of distributing the total workload for manufacturing any unit of the product to be assembled among the work stations along the line.Assembly line balancing research has traditionally focused on the simple assembly line balancing problem (SALBP) which has some restricting assumptions. Recently, a lot of research work has been done in order to describe and solve more realistic generalized problems (GALBP). In this paper, we survey the developments in GALBP research.     

Simple heuristics for the assembly line worker assignment and balancing problem

We propose simple heuristics for the assembly line worker assignment and balancing problem. This problem typically occurs in assembly lines in sheltered work centers for the disabled. Different from the well-known simple assembly line balancing problem, the task execution times vary according to the assigned worker. We develop a constructive heuristic framework based on task and worker priority rules defining the order in which the tasks and workers should be assigned to the workstations. We present a number of such rules and compare their performance across three possible uses: as a stand-alone method, as an initial solution generator for meta-heuristics, and as a decoder for a hybrid genetic algorithm. Our results show that the heuristics are fast, they obtain good results as a stand-alone method and are efficient when used as a initial solution generator or as a solution decoder within more elaborate approaches.