考虑产品与单元匹配的单元装配系统构建模型

针对日本式单元生产过程中流水装配线向单元装配系统转换的决策优化问题,本文以最小化总完工时间和工人总工时为目标,同时考虑产品与单元的匹配关系,构建了单元装配系统多目标优化模型,基于NSGA-II算法设计了适用于该问题的三段染色体的编码方式。通过算例分析了产品与单元匹配关系变化对总完工时间和工人总工时的影响,结果表明,在工人数不变的情况下,产品与单元匹配关系变化会导致总完工时间产生较大差异,随着工人数增多,这种差异逐渐变小。     

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。     

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.     

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.     

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。     

考虑订单合并和货物转运的多式联运路径优化研究

为了获得运输的规模经济效应,本文研究了一种考虑订单合并和货物转运的零担多式联运路径优化问题。首先,以总运输成本为目标函数,以网络中的运输工具容量、可以提供的运输工具最大数量、运输工具服务的关闭时间以及订单时间窗为约束,构建混合整数规划模型,在模型中允许多个订单进行合并运输并考虑运输过程中的转运成本。其次,由于多式联运路径优化问题是典型的NP-hard问题,为了快速求解该模型,开发了一种可以快速为该问题提供近似最优解和下界的列生成启发式算法。最后,生成并测试了大量算例,结果表明所开发的列生成启发式算法可以在较短的时间内提供高质量的近似最优解。文章所构建的模型和开发的列生成启发式算法可以为零担自营多式联运物流企业提供高效的决策支持。     

分散搜索算法求解复杂产品协同优化分配问题

研究了网络化制造环境下复杂产品的协同优化分配问题.考虑网络联盟企业的生产能力约束和复杂产品的交货期约束,加入满足装配要求的公差约束,以最大化公差和最小化制造费用、运输费用以及质量损失费用为目标,建立了该问题的两阶段数学规划模型.结合问题解的特点,把两阶段模型整合成一个非线性混合整数规划模型,提出了针对问题特点的分散搜索算法.该算法采用启发式方法随机产生初始解,开发了适合问题特点的子集产生、合并和改进以及参考集更新等多种改进机制实现对该问题的有效求解.对小规模实例进行分析,验证了加入公差约束的必要性,同时仿真分析了15组不同规模的实例,与遗传算法的结果进行比较,验证了该模型与算法的合理性和有效性.     

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。     

基于启发式Q学习的汽车涂装车间作业排序优化

针对汽车涂装车间中的作业优化排序问题,提出一种基于启发式Q学习的优化算法。首先,建立包括满足总装车间生产顺序和最小化喷枪颜色切换次数的多目标整数规划模型。将涂装作业优化排序问题抽象为马尔可夫过程,建立基于启发式Q算法的求解方法。通过具体案例,对比分析了启发式Q学习、Q学习、遗传算法三种方案的优劣。结果表明:在大规模问题域中,启发式Q学习算法具有寻优效率更高、效果更好的优势。本研究为机器学习算法在汽车涂装作业优化排序问题的应用提出了新思路。     

装配系统的构模技术

产业革命以后，人们对合格产品的大量需求，促使工程技术人员去研究大批生产的新方法，从而提出了许多成本低、批量大的生产新技术、新方法。其中卓有成效的重要制造工艺过程之一是福特首创的装配线思想。事实上，装配线迄今仍是成批生产的关键手段，而且装配成本往往超过了总制造成本的50％。所以，制造业的是否成功，装配系统的效能是非常关键的因素。近几年来，许多研究人员花费了巨大的精力来研究流水系统的效能。然而，由于问题的复杂性，装配系统在管理方面仍不能跟上步伐。在装配系统的早期研究中，尽管用了许多解析模型，然而，它们在为管理人员提供决策依据时却不够精确。因此，在研究和优化装配系统的效能时，仍广泛地使模拟技术。并且，随着像摄动分析方法等这些先进的模拟优化技术的发展，模拟优化更为有效，而且还节省了大量的计算费用。在这篇章中，我们将首先介绍装配系统的特征，然后讨论装配系统构模的困难之外。最后，说明和总结一些主要的研究问题。     

A Lagrangian relaxation approach to the mixed-product assembly line sequencing problem: A case study of a door-lock company in Taiwan

In mixed-product assembly line sequencing, the production resources required for the assembly lines should be scheduled to minimize the overall cost and meet customer demand. In this paper, we study an assembly line sequencing problem for the door-lock industry in Taiwan and develop an integer programming formulation with realistic constraints. The complex solution space makes the resulting program difficult to solve using commercial optimization packages. Therefore, a heuristic based on the Lagrangian relaxation principle is developed to solve this problem efficiently. We evaluate the efficiency of the developed Lagrangian relaxation heuristic by comparing its solutions with those obtained using a commercial optimization package: the computational results show that the developed heuristic solves the real-world problem faster than the optimization package by almost 15 times in CPU time at a comparable solution quality.     

Applying GIS and Combinatorial Optimization to Fiber Deployment Plans

A decision-support system for the Fiber Deployment Plan problem is developed for the telephone cable network design in the telecommunications industry. The system employs a Geographic Information System (GIS) and uses combinatorial optimization techniques as its components. A mathematical combinatorial optimization model is formulated for the problem and a heuristic solution procedure is developed for the model. A GIS within the ESRI Arc/INFO and ArcView environment is used to provide data needed to build the mathematical combinatorial optimization model and to furnish an interface between the users and computers in data input and in solution result display. Combinatorial optimization techniques are used in the heuristic solution procedure to find good solutions for the optimization model. The developed decision-support system has been used to real life problems and has resulted in tremendous improvements in the telephone cable network design process. The user is completely satisfied with the performance of the system.     

基于时间约束的生产过程优化问题研究

本文提出一个实际的生产过程优化问题:基于时间约束的生产过程优化问题。客户要求企业在规定时间内完成指定批量工件的生产任务,该问题便是从中引出的。该问题的目标是在满足生产时间的条件下最小化总生产成本。本文为该问题建立了整数规划模型。然后以某厂工作缸生产过程为例,采用数学规划软件Cplex 9.0求解模型。     

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.     

An Integer Programming Heuristic for Component Allocation in Printed Circuit Card Assembly Systems

Component allocation is an important element of process planning for printed circuit card assembly systems. The component allocation problem directly impacts the productivity and cost of a circuit card assembly system. Many companies have recognized the importance of component allocation and have started to develop a better decision process. Also, a few commercial software packages have been developed that provide environments to support process planning. However, optimization methods are not yet widely used. We demonstrate that component allocation is amenable to improvement using optimization methods. We present an integer programming heuristic for the component allocation problem and report on several case studies that have been conducted and that demonstrate its effectiveness. The heuristic is based on a mixed integer programming formulation of the component allocation problem that incorporates estimates of downstream process planning decisions.     

Minimum Required Local Storages of an Automated Assembly System

An optimal strategy for selection of the required local storages of the workstations and transporter stations of a finite capacity flexible assembly line system is presented while controlling the bottleneck problem. For this purpose, a mixed non-Markovian queueing network with infinite capacity nodes is presented to model its performance, a stochastic optimization model is provided and a heuristic algorithm is developed for solving it. Finally, an example is presented and the approximation results are compared with those from a simulation study.     

The stochastic U-line balancing problem: A heuristic procedure

Many heuristics have been proposed for the assembly line balancing problem due to its computational complexity and difficulty in identifying an optimal solution. Still, the basic line balancing model fails to consider a number of realistic elements. The implementation of a Just-In-Time manufacturing system generally entails the replacement of traditional straight assembly lines with U-shaped lines. An important issue in the U-line balancing problem is the consideration of task time variability due to human factors or various disruptions. In this paper, we consider the stochastic U-line balancing problem. A hybrid heuristic is presented consisting of an initial feasible solution module and a solution improvement module. To gain insight into its performance, we analyze the heuristic under different scenarios of task time variability. Computational results clearly demonstrate the efficiency and robustness of our algorithm.     

Packing items to feed assembly lines

We treat a practical application of packing problems in feeding assembly lines. This study was motivated by a real situation encountered in the shop floor of a major automobile industry plant in Brazil. The assembly line feed problem (LFP) consists in how pack the items in the available containers to meet the line work centers’ requirements with a minimum total cost over the planning horizon. LFP is a variable-sized bin packing problem that has two special features: (i) a cardinality constraint on each bin’s size; and, (ii) a cost structure such that each bin’s cost varies according to the items that are packed in it. We propose an integer programming model and a GRASP heuristic for LFP. Numerical results on real-life test instances are reported.     

Balancing and scheduling tasks in assembly lines with sequence-dependent setup times

The classical Simple Assembly Line Balancing Problem (SALBP) has been widely enriched over the past few years with many realistic approaches and much effort has been made to reduce the distance between the academic theory and the industrial reality. Despite this effort, the scheduling of the execution of tasks assigned to every workstation following the balancing of the assembly line has been scarcely reported in the scientific literature. This is supposed to be an operational concern that the worker should solve himself, but in several real environments, setups between tasks exist and optimal or near-optimal tasks schedules should be provided inside each workstation. The problem presented in this paper adds sequence-dependent setup time considerations to the classical SALBP in the following way: whenever a task is assigned next to another at the same workstation, a setup time must be added to compute the global workstation time. After formulating a mathematical model for this innovative problem and showing the high combinatorial nature of the problem, eight different heuristic rules and a GRASP algorithm are designed and tested for solving the problem in reasonable computational time.