知识化制造系统中生产瓶颈的分析方法

在产品的制造过程中,如果某些生产环节能力过于薄弱,将会形成瓶颈效应,阻碍其他部分生产能力的充分利用。为此,建立了知识化制造系统的网状模型,该模型适用于柔性生产线的生产瓶颈分析。在此基础上,提出了完备生产网络的概念和寻找完备生产网络中生产瓶颈的方法。利用自学习方法将瓶颈和其对应的生产条件存入瓶颈知识库,以便确定在不同生产条件下哪些环节需要改进。最后通过算例分析,证实了该瓶颈分析方法的有效性。     

A multi-stage fluctuation smoothing method for multiple bottlenecks in wafer fabrication

The performance of a wafer fabrication system is constrained by its bottlenecks, which is often affected by the system fluctuations and unpredicted events, e.g., machine breakdown. Considering the re-entrant process flow in wafer fabrication, we propose a scheduling method that divides the process flow into several stages and protects the bottleneck step at each re-entrant stage from the system fluctuations. Furthermore, due to the multiple bottlenecks in wafer fabrication system, we identify the bottlenecks by sensitivity analysis on machines’ capacity. Then, during the real-time scheduling, the bottlenecks with stronger constraints are assigned with higher priority than the bottlenecks with weaker constraints. Finally, a set of simulation experiments are performed to validate the effectiveness of the proposed method.     

A new model of dynamic cell formation by a neural approach

This paper proposes a nonlinear integer model of cell formation under dynamic conditions. The cell formation (CF) problem is a portion of a cellular manufacturing strategy (CMS), in which the parts and machines are clustered with the aim of minimizing the material handling cost. In most previous research the cell formation problem has always been under static conditions in which cells are formed for a single-period planning horizon where product mix and demand are constant. In contrast, in dynamic conditions, a multi-period planning horizon is considered, where the product mix and demand in each period is different. This occurs in seasonally or monthly production. As a result, the best cell design for one period may not be efficient for subsequent periods. To verify the presented model, different problems have been solved and results are reported. Where the cell formation problem belongs to NP class, the use of a novel approach is necessary. In this research, we apply a neural approach based on mean filed theory for solving the proposed model. In this approach, the network weights are updated by an interaction procedure. The proposed model is solved by LINGO software and an optimum solution is obtained. Comparison of optimum and neural approach solutions shows the efficiency of the presented neural network approach.     

Bottleneck identification procedures for the job shop scheduling problem with applications to genetic algorithms

Two bottleneck identification algorithms (one for bottleneck machines and the other for bottleneck jobs) are presented for the job shop scheduling problem in which the total weighted tardiness must be minimized. The scheduling policies on bottleneck machines can have significant impact on the final scheduling performance, and therefore, they need to be optimized with more computational effort. Meanwhile, bottleneck jobs that can cause considerable deterioration to the solution quality also need to be considered with higher priority. In order to describe the characteristic information concerning such bottleneck machines and bottleneck jobs, a statistical approach is devised to obtain the bottleneck characteristic values for each machine, and, in addition, a fuzzy inference system is employed to transform human knowledge into the bottleneck characteristic values for each job. These bottleneck characteristic values reflect the features of both the objective function and the current optimization stage. Finally, the effectiveness of the two procedures is verified by specifically designed genetic algorithms.     

Product Grouping and Resource Allocation in Multiline Manufacturing Systems

This paper studies the product grouping problem and the resource allocation problem in the design of manufacturing systems where multiple production lines are used to manufacture a range of products. Each production line in the product grouping problem is dedicated to manufacturing a group of products. A mathematical model is developed to determine the number of product groups and the composition of each product group in the manufacturing system. For the resource allocation problem, a mathematical model is developed to determine the optimal resource allocation scheme for each production line and the optimal inventory level for each product. A genetic-based algorithm is proposed to solve the product grouping problem and the resource allocation problem simultaneously, and its results are compared to those of the conventional heuristic approaches. The proposed genetic approach is a simple but effective means of solving these problems.     

Analysis of priority mix planning for the fabrication of semiconductors under uncertainty

Because of intensive capital investment in semiconductor manufacturing, the priority mix decision plays a critical role for effective administration. The potential value of products, efficiency in the deployment of equipment, and characteristics of cash flow are inherently difficult to be precisely defined and determined by a decision maker. The main objective in this study is therefore to construct an analytical approach for dealing with the aforementioned managerial problems under subjective judgment environments. Thus, we utilized a fuzzy analytic hierarchy process method to deal with uncertainty. Of primary concern are the criteria of product, equipment efficiency, and finance, and detailed criteria are catered to the requirement for further analysis. Finally, a priority mix of strategic alternatives represented by a priority index can be evaluated. A performance ranking of priority mixes can then be generated. The results provide guidance to select strategies for accepting orders with the consideration of manufacturing efficiency and the aspects of product, equipment efficiency and finance. The model is easily understandable and can be followed by administrators to determine the most suitable priority mix for a fabrication process.     

多品种小批量生产环境下基于约束管理的混流制造运行控制技术研究

在多品种小批量生产环境中,现有的、以适应大批量重复型生产过程为主的混流制造技术难以得到推广,为实现此类生产环境下的精益化改造,出现了基于混流路径的混流制造新模式.在研究该模式总体架构的基础上,提出多品种小批量生产环境下基于约束管理的混流制造运行控制机制,并就混流路径规划、资源瓶颈识别与动态瓶颈管理等关键技术进行探讨,为进一步的研究应用提供基础.     

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。     

Algorithm for solving product mix problem in two-constraint resources environment

The theory of constraints is an approach to production planning and control that emphasizes on the constraints in the system to increase throughput. One application in the theory of constraints is product mix decision. The objective of this paper is to present an algorithm to determine the product mix in a two-constraint resources environment. The theory of constraints solution could not reach optimum solution and has the risk of being infeasible when multiple constraint resources exist. The proposed algorithm is suitable for improving solutions obtained from theory of constraints and could provide throughput in product mix problems. Some alternatives are compared in this paper: the standard theory of constraints, integer linear programming, tabu search, hybrid tabu simulated annealing, and proposed algorithm solution. The numerical result shows the advantages of the proposed algorithm.     

Tool planning in the scenario of multiple existing semiconductor fabs

The tool planning problem involves determining how many tools should be allocated to each tool group to achieve a set of objectives. Most previous studies assume that a demand mix has been given for a new factory to be planned. However, when a semiconductor company has several existing fabs, the demand mix for the new fab is not explicitly known, and needs to be allocated from the demand mix of the whole company. This paper presents an integrated approach to determine the optimal demand mix and associated tool plan for the new fab that can minimize the tool cost of the new fab while each fab (new or existing) is requested to meet a predefined target in its mean cycle time. Simulation experiments indicate that the proposed solution is better than that obtained by a heuristic method used in industry. The saving in tool cost for a typical tool planning problem can be over US$ 70 million.     

Dynamic cellular manufacturing systems design��a comprehensive model

This paper addresses the dynamic cell formation problem (DCF). In dynamic environment, the product demand and mix changes in each period of a multiperiod planning horizon. It causes need of reconfiguration of cells to respond to the product demand and mix change in each period. This paper proposes a mixed-integer nonlinear programming model to design the dynamic cellular manufacturing systems (DCMSs) under dynamic environment. The proposed model, to the best of the author??s knowledge, is the most comprehensive model to date with more integrated approach to the DCMSs. The proposed DCMS model integrates concurrently the important manufacturing attributes in existing models in a single model such as machine breakdown effect in terms of machine repair cost effect and production time loss cost effect to incorporate reliability modeling; production planning in terms of part inventory holding, part internal production cost, and part outsourcing; process batch size; transfer batch size for intracell travel; transfer batch size for intercell travel; lot splitting; alternative process plan, and routing and sequence of operation; multiple copies of identical copies; machine capacity, cutting tooling requirements, work load balancing, and machine in different cells constraint; machine in same cell constraint; and machine procurements and multiple period dynamic cell reconfiguration. Further, the objective of the proposed model is to minimize the sum of various costs such as intracell movement costs; intercell movement costs and machine procurement costs; setup cost; cutting tool consumption costs; machine operation costs; production planning-related costs such as internal part production cost, part holding costs, and subcontracting costs; system reconfiguration costs; and machine breakdown repair cost, production time loss cost due to machine breakdown, machine maintenance overheads, etc. ,in an integrated manner. Nonlinear terms of objective functions are transformed into linear terms to make mixed-integer linear programming model. The proposed model has been demonstrated with several problems, and results have been presented accordingly.     

Workload balance and part-transfer minimization in flexible manufacturing systems

Problems related to the flow management of a flexible manufacturing system (FMS) are here formulated in terms of combinatorial optimization. We consider a system consisting of several multitool automated machines, each one equipped with a possibly different tool set and linked to each other by a transportation system for part moving. The system operates with a given production mix.The focused flow-management problem is that of finding the part routings allowing for an optimal machine workload balancing. The problem is formulated in terms of a particular capacity assignment problem.With the proposed approach, a balanced solution can be achieved by routing parts on a limited number of different paths. Such a balancing routing can be found in polynomial time. We also give polynomial-time and-space algorithms for choosing, among all workload-balancing routings, the ones that minimize the global amount of part transfer among all machines.     

A nonlinear scheduling rule incorporating fuzzy-neural remaining cycle time estimator for scheduling a semiconductor manufacturing factory—a simulation study

Scheduling of a semiconductor wafer fabrication system (SWFS) is complicated due to its re-entrant product flow, high uncertainties in operations, and rapidly changing products and technologies; thus dispatching rules have been widely used for real-time scheduling because they can provide a very quick and pretty good solution. However, deciding how to select appropriate rules is very difficult and seldom tackled. This paper describes an approach into the evaluation and optimization of dispatching rules by integrating the simulation and response surface methodology (RSM). In order to implement the proposed approach, a dynamic bottleneck dispatching (DBD) policy is designed, in which bottlenecks are detected in a timely way and adaptive dispatching decisions are made according to the real-time conditions. In addition, two case studies are carried out to demonstrate the approach. One case compares DBD to regular rules, such as CR + FIFO, EDD, SRPT, SPT, SPNB and Justice, a bottleneck dispatching method. Simulation results show that the DBD policy is superior to the other six methods. In another case study, the parameters of DBD are optimized by RSM and desirability function, and the result proves that the optimized DBD method can get even better performance.     

Job releasing and throughput planning for wafer fabrication under demand fluctuating make-to-stock environment

In the semiconductor industry, dynamic changes in demand force companies changing the product mix makes the production planning challenging. This paper aims at an environment where product mix changes periodically and presents a production scheduling system to plan the wafer lot release and throughput. The proposed system is designed on the make-to-stock basis with the objective of meeting demand forecast while maintaining production smoothness. Two modules are included in the system. Preliminary analysis module analyzes throughput and cycle time distributions for different product mixes so as to determine relative parameters to be used as the inputs to the job releasing plan. In the production scheduling module, with the considerations of the attainment of demand forecast, production smoothness, and commitment of the due dates of the released job orders, the job release schedule and completion time table are prepared. A simulation model of a semiconductor fab is used as the base case to demonstrate the effectiveness and efficiency of the proposed system.     

机械产品功能细化下的冲突确定

产品创新设计的核心是解决设计中的矛盾或冲突,如何确定设计中冲突或矛盾便成为创新设计中的关键问题之一.根据机械产品在创新过程中的特点,通过功能分析,利用功能细化后的特点,提出一种新的冲突确定的方法,该方法能够确定冲突的存在及冲突在产品结构中的位置,将该方法称为功能细化下的冲突确定.对冲突确定的整个过程进行了详细的描述.并结合以具体实例说明其应用过程.     

A tool planning approach considering cycle time constraints and demand uncertainty

The tool planning problem is to determine how many tools should be allocated to each tool group to meet some objectives. Recent studies aim to solve the problem for the cases of uncertain demand. Yet, most of them do not involve cycle time constraints. Cycle time, a key performance index in particular in semiconductor foundry, should not be ignored. The uncertain demand is modeled as a collection of scenarios. Each scenario, with an occurrence probability, represents the aggregate demand volume under a given product mix ratio. A genetic algorithm embedded with a queuing analysis is developed to solve the problem. Experiments indicate that the proposed solution outperforms that obtained by considering only a particular scenario.     

A tool planning approach considering cycle time constraints and demand uncertainty

The tool planning problem is to determine how many tools should be allocated to each tool group to meet some objectives. Recent studies aim to solve the problem for the cases of uncertain demand. Yet, most of them do not involve cycle time constraints. Cycle time, a key performance index in particular in semiconductor foundry, should not be ignored. The uncertain demand is modeled as a collection of scenarios. Each scenario, with an occurrence probability, represents the aggregate demand volume under a given product mix ratio. A genetic algorithm embedded with a queuing analysis is developed to solve the problem. Experiments indicate that the proposed solution outperforms that obtained by considering only a particular scenario.     

Make-to-order/make-to-stock partitioning decision using the analytic network process

This paper presents a decision-making structure to determine the appropriate product delivery strategy for different products in a manufacturing system. These strategies include make-to-stock (MTS), make-to-order (MTO), and hybrid MTS/MTO production systems. In fact, the proposed approach gets the decision maker the opportunity to benefit from both strategies through applying the hybrid one. There are varieties of driving factors involved in choosing the right product delivery strategy, and all these factors have positive and negative interactions with each others; in this regard, we apply an appropriate multi-criteria decision-making method. In this method, relevant criteria affecting MTS/MTO partition are split into four categories: market-related criteria, product-related criteria, process-related criteria, and supplier-related criteria. Due to the interdependency between these criteria, we use analytic network process that generalizes analytic hierarchical process by considering the interdependencies among factors. Finally, in order to show the applicability of the proposed structure in practice, the structure is implemented to choose the best production policy among three aforementioned strategies in the real industrial case company.     

基于区间数聚类分析的多属性瓶颈区域识别方法

制造业生产环境复杂,瓶颈现象的出现会制约其发展.针对利用单一数据识别单个瓶颈机器的方法难以具体描述系统瓶颈的问题,提出了一种基于区间数聚类分析的多属性瓶颈区域识别方法.首先采用k-means++聚类分析方法,根据决策矩阵比较各指标的相似程度,找出不同距离下的机器区域.然后利用决策矩阵求出的可能度矩阵排序向量找出各区域的...     

基于两种瓶颈度的制造车间多瓶颈动态预测方法

针对制造车间多瓶颈预测问题,提出一种基于两种瓶颈度的多瓶颈动态预测方法,以在合理认定瓶颈责任的前提下实现系统多瓶颈的连续准确预测。首先,建立综合瓶颈度和独立瓶颈度的概念模型,并以时间、质量和成本为参数,构建其数学模型;然后,构建包含多瓶颈双层预测机制、瓶颈多态性分析和瓶颈指数迭代预测模型的多瓶颈动态预测方法;最后,将多瓶颈动态预测方法、单瓶颈度法和PBM法运用至某汽车产品总装线,以验证该方法有效性。