基于排队理论的制造单元构建与加工路径选择

利用排队理论将每台机器等效为M/M/1排队模型,整个系统简化为开环Jackson排队网络,建立多目标非线性整数规划模型,通过目标函数优化单元制造系统中的加工成本与等待成本,同时缩小设备服务强度范围与单元服务强度范围,实现单元构建与工件加工路径选择。模型求解采用分枝定界法,最后在Lingo软件环境下验证一个随机算例,结果表明该模型的可行性与有效性。     

基于通用生成函数的混流制造系统脆弱性评价方法

为了解决基于状态熵的脆弱性评价方法在面对多状态系统组合爆炸问题时的困难,提出了一种基于通用生成函数的混流制造系统脆弱性量化评价方法。首先,分析求解各制造单元的状态及其概率,得到制造单元的通用生成函数,定义并计算各单元的脆性熵,建立制造单元的脆弱性分析模型;其次,根据混流制造系统结构特征和通用生成函数的串并联构造算子,得到系统通用生成函数,定义其脆性熵,基于脆性熵建立混流制造系统的脆弱性评价模型;最后,以某典型的混流制造系统为例,利用所提出的脆弱性评价方法,评估其脆弱性,并与基于状态熵的评价方法进行对比,结果表明该方法可显著提高脆弱性评价的效率。     

考虑性能参数变化的混流制造系统脆弱性评估方法

针对现有混流制造系统脆弱性评估方法的不足,在考虑系统性能参数实时变化的情况下,提出了一种基于LZ变换的脆弱性评估方法。利用离散时间马尔科夫原理求得制造单元各状态的瞬时概率,基于系统的结构特征和LZ变换原理、通用生成函数的串并联构造算子等得到制造单元和系统的LZ变换函数;根据混流制造系统脆弱性定义和系统的LZ变换函数,以其可用度、平均输出期望值和瞬时缺陷率等为脆弱性评价指标,建立混流制造系统的脆弱性评价模型;以一个包含7个制造单元的混流制造系统为例,通过求解系统的LZ变换函数,建立基于可用度、输出性能期望值、瞬时性能缺陷等评估指标的脆弱性评价模型,得到系统的脆弱性实时变化曲线,验证了该方法的可行性,扩展了脆弱性评价的应用范围。     

一类考虑返修和产品多态性的多级串联制造系统建模与性能分析

针对一类考虑返修和产品多态性的多级串联制造系统性能分析问题,在考虑系统设备故障基础上,重点研究产品的返修和报废以及产品的多态性导致工位正常运行时加工状态差异对系统性能的影响,提出一种基于扩展Petri网(Extended-stochastic Petri nets,ESPN)的系统模块化建模方法,解决传统利用Markov模型和排队网络模型建模难以清晰描述系统内部产品流转换导致复杂状态变化的问题。将ESPN模型同构于嵌入Markov链得到工位状态空间,推导产品多态性导致的工位不同加工状态相对激发概率分布。以双工位单缓冲区的两级串联制造系统单元模型性能分析为基础,考虑缓冲区两边系统对单元模型的影响,基于重叠分解法对系统模型进行迭代求解,分析系统各项性能指标。某企业汽车动力总成装配线的实例验证了该建模与分析方法的有效性。     

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。     

基于二元增强学习架构的可重构制造系统调度

构建基于重构和调度二元增强学习架构的调度系统,借助重构增强学习系统的行为来实现资源配置、重构制造单元的功能,借助调度增强学习系统的行为来为各制造单元安排加工任务,实现优化各产品加工路径和加工顺序的功能.重构和调度增强学习系统通过状态转移、行为选择和报酬获取进行联系.提出结合函数泛化器的自适应步长增强学习算法的学习机制和学习步长调整机制,通过实验分析了函数泛化器的性能,验证了该算法解决一类重人型的可重构制造车间调度问题的有效性.     

面向JCP的混流生产线重构过程中的工位复制问题研究

介绍了日本式单元生产方式,并与混流生产方式进行了比较,日本式单元生产是将一条长的生产线分拆成多条短的生产线,使其既能适应多样的市场需求,又具有较高的生产效率。讨论了两者如何转化。重点研究了混流生产方式重构成日本式单元生产方式(JCP)过程中的工位复制问题。总结了解决该问题的一般步骤。首先,对产品进行分类,每一个生产单元生产一类产品。然后,通过物流费用与工位复制费用的比较,判断出需要复制的工位,最后,计算不复制工位情况下的总费用和复制工位情况下的总费用。     

轿车发动机混流制造生产调度系统应用研究

针对目前混流制造模式下车间层的信息实时采集与处理能力不足、生产调度的工具实用性不足的问题,采用协同优化的框架,基于发动机总成装配的拉动,建立了以最小化总加工时间为目标的轿车发动机混流加工线生产调度模型;基于eM—Plant软件,建立了可以验证调度算法优劣的轿车发动机混流加工线的数字化模型;在车间实时制造数据采集方面,设计了一套基于无线网络传输模式的工位信息系统,支持计划层和执行层的双向联通。     

考虑共因失效的多态系统可靠性优化

建立了考虑共因失效的多态系统可靠性优化模型,并对优化模型进行了求解。针对市场上可供选择单元的性能、可靠度和费用参数,建立了以系统费用最少为目标,以系统可靠度为约束条件的优化模型。利用通用生成函数法分析了考虑共因失效的多态系统可靠度计算问题,利用遗传算法分析了优化模型的求解问题,并且对比了不考虑共因失效时和考虑共因失效时所得到的优化结果。结果表明,在分析发生共因失效的多态系统可靠性冗余配置优化问题时,应更加注重所选择单元的可靠度而不仅仅是单元的冗余数量。     

基于并行选择遗传算法的3Z(Ⅱ)型行星轮系优化设计

研究了3Z(Ⅱ)型行星轮系的优化设计方法。以行星轮系的体积最小、传动效率最高为目标函数建立了多目标优化模型,利用惩罚函数将原约束问题转换为无约束优化问题,提出了基于并行选择遗传算法的多目标优化求解算法。实例表明,该方法适用于3Z(Ⅱ)型行星轮系的多目标优化,且优化效果显著。该法对于其它行星轮系的优化设计也具有借鉴意义。     

Simultaneous manufacturing shape optimization for minimum cost of milling using interior point method

This paper presents an integrated manufacturing shape optimization method and explores the tradeoff between mass and machining cost with the application of the end-milling that used to machine a topological die. The aim is to make a simultaneous optimization method between product manufacturing and its topological shape to close the gap between theoretical work and practical needs. This innovative method depends on some manufacturing functional issues and shape constraints. The objective function is minimizing the total cost of process including the material and machining cost. The initial shape of the part is constructed by using B-splines and hole generation-based topological concept; the design variables are x- and y-coordinates of the control points that represent the B-splines. The cutting parameters of the milling process such as the cutting speed, the feed per flute, and the tool diameter are also optimized. The simultaneous manufacturing shape optimization problem will be achieved by interfacing between MATLAB2011a and ANSYS15 programs using interior point method (IPM) as a mathematical optimization solver. The comparison between the initial total cost and its optimal results for studied cases is presented, and the influence of the required batch size on the final shape is discussed. It is found that the reduction percentage in the cost function can receive 69% for small batch size (batch 50 pieces) and 54.18% for large batch size (batch 5000 pieces). The result from this simultaneous optimization is that the batch size of the required product influences the topological shape of the product.     

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.     

公差稳健优化设计的研究

为了解决产品加工成本与质量稳健的协调性问题,提出了一种新的公差稳健优化设计数学模型.依据公差稳健设计的思想,考虑产品质量的模糊性,以封闭环误差分布概率密度函数的方差和优质品概率之比为设计目标,建立了公差优化设计产品质量稳健性损失成本目标函数,并研究了优质品率和封闭环误差分布方差的确定方法.以加工成本和产品质量稳健性损失成本为目标,以模糊装配可靠度、可取公差极限范围为约束条件,建立了公差多目标优化数学模型.举例说明了文中所述的公差稳健优化设计方法的应用,采用遗传算法实现了公差的多目标优化设计.实例表明,该方法能够协调零件的加工成本和产品质量的稳健性损失成本,使优化指标的综合性能最佳.     

离散制造单元生产扰动识别模型及其自适应节能控制方法

离散制造单元的生产扰动是中断设备生产、造成能耗浪费的源头，如何在复杂动态的扰动环境下实现设备节能控制策略的自适应选择和控制参数的自适应优化从而达到最优的节能控制效果是一个亟待解决的难题，为此提出了基于生产扰动识别模型的离散制造单元自适应节能控制方法。首先，基于生产扰动的模式性和不确定性，构建了一种马尔科夫逻辑网络驱动的生产扰动识别模型，实现生产扰动的精准识别。然后，以能耗浪费期望为优化目标，生产延迟时间期望为优化约束，制定了基于时间阈值的设备节能控制策略，提出了面向生产扰动自适应的节能控制方法。最后，以某压铸单元为对象进行了案例验证，结果表明生产扰动类型会对节能控制效果造成显著影响，所提出方法在尽量保证生产的前提下通过提升节能控制的自适应能力实现了更好的节能效果。     

Inspection Allocation Planning for a Multiple Quality Characteristic Advanced Manufacturing System

Producing products with multiple quality characteristics is always one of the concerns for an advanced manufacturing system. To assure product quality, finite automatic inspection systems should be used. Inspection planning to allocate inspection stations should then be performed to manage the limited inspection resource. Except for finite inspection station classes, in this work, the limited number of inspection stations, of each inspection station class, is considered for solving the inspection allocation problem in a multiple quality characteristic advanced manufacturing system. Since the product variety in batch production or job shop production increases to satisfy the changing requirements of the various customers, the tolerances specified will vary from time to time. This inspection allocation problem is solved using a unit cost model in which the manufacturing capability, inspection capability, and tolerance specified are concurrently considered for a multiple quality characteristic product. The situation of unbalanced tolerance design is also considered. The inspection allocation problem can then be solved according to customer requirements. Since determining the optimal inspection allocation plan seems to be impractical, as the problem size becomes large, two decision criteria (i.e. sequence order of workstation and tolerance interval) are employed separately to develop two different heuristic solution methods in this work. The performance of each method is measured in comparison with the enumeration method that generates the optimal solution. The result shows that a feasible inspection allocation plan can be determined efficiently. ID="A1"Correspondance and offprint requests to: Dr Yau-Ren Shiau, Department of Industrial Engineering, Feng-Chia University, 100 Wenhwa Road, Seatwen, PO Box 25–097, Taichung 407, Taiwan     

计及漂移瓶颈的时变物料配送路径优化

针对不确定作业车间环境下物料配送路径优化问题,采用能反映制造单元相对生产负荷及其变化趋势的瓶颈指数和瓶颈漂移指数表征实时变化的制造单元物料配送优先级,对路径选择过程中违反此优先级的行为设置惩罚成本,提出以最小化包括车辆运输成本和违反优先级的惩罚成本在内的总配送成本为优化目标,建立了时变的物料配送路径优化模型。在此基础上,为保证运输车辆所载物料全额配送,避免非必要负载以及由此造成的非必要配送子路径,对配送路径优化模型进行改进,允许运输车辆非满载和物料拆分配送,以提高物料配送效率降低配送成本;并结合模型特点将贪婪策略融入遗传算法对优化模型求解。最后,通过某作业车间内物料配送实例验证了所提出的计及漂移瓶颈的改进时变物料配送路径优化方法在不确定作业环境中具有有效性和实用性。     

Modelling the effects of machine breakdowns in the generalized cell formation problem

Machines are key elements in manufacturing systems and their breakdowns can dramatically affect system performance measures. This paper proposes a new multi-objective pure integer linear programming approach for the cell formation problem with alternative process routings and machine reliability consideration. The model minimizes total cost and maximizes system reliability simultaneously. Traditional reliability evaluation approaches attempt to model the reliability of the manufacturing system as a function of its elements. These approaches have some negative aspects; therefore, instead of modeling the system reliability as an explicit objective function, we use an approach to model the effects of the machine unreliability in terms of cost and time-based effects. Using the ?-constraint method as an optimization tool for multi-objective programming, a numerical example is solved to demonstrate the capability of the proposed model in evaluating various effects of the reliability consideration.     

Multi-task scheduling of distributed 3D printing services in cloud manufacturing

The problem of service matching and scheduling in cloud manufacturing (CMfg) is complex for different types of manufacturing services. 3D printing, as a rapidly developing manufacturing technology, has become an important service form in the CMfg platform due to its characteristics of personalized manufacturing. How to solve the task scheduling problem for distributed 3D printing services in CMfg needs further research. In this paper, a service transaction model of 3D printing services in CMfg is built. Based on the service transaction model, we propose 3D printing service matching strategies and matching rules of different service attributes, including model size, printing material, printing preciseness, task cost, task time, and logistics. To reduce the delivery time of tasks from service suppliers to service demanders, a 3D printing service scheduling (3DPSS) method is proposed to generate optimal service scheduling solutions. In 3DPSS, optimization objective, constraints, and optimization algorithm are presented in detail. Experimental results show that the average task delivery time of 3DPSS is shorter than that of typical scheduling methods, such as particle swarm optimization, pattern search, and sequential quadratic programming, when the amounts of tasks change.     

Concurrent process/inspection planning for a customized manufacturing system based on genetic algorithm

Producing products with multiple quality characteristics is always one of the concerns for an advanced manufacturing system. To assure product quality, finite manufacturing resources (i.e., process workstations and inspection stations) could be available and employed. The manufacturing resource allocation problem then occurs, therefore, process planning and inspection planning should be performed. Both of these are traditionally regarded as individual tasks and conducted separately. Actually, these two tasks are related. Greater performance of an advanced manufacturing system can be achieved if process planning and inspection planning can be performed concurrently to manage the limited manufacturing resources. Since the product variety in batch production or job-shop production will be increased for satisfying the changing requirements of various customers, the specified tolerance of each quality characteristic will vary from time to time. Except for finite manufacturing resource constraints, the manufacturing capability, inspection capability, and tolerance specified by customer requirement are also considered for a customized manufacturing system in this research. Then, the unit cost model is constructed to represent the overall performance of an advanced manufacturing system by considering both internal and external costs. Process planning and inspection planning can then be concurrently solved by practically reflecting the customer requirements. Since determining the optimal manufacturing resource allocation plan seems to be impractical as the problem size becomes quite large, in this research, genetic algorithm is successfully applied with the realistic unit cost embedded. The performance of genetic algorithm is measured in comparison with the enumeration method that generates the optimal solution. The result shows that a near-optimal manufacturing resource allocation plan can be determined efficiently for meeting the changing requirement of customers as the problem size becomes quite large.