摘要:

In an assembly process, test operations are often introduced to ensure product quality. After having been rejected by a test operation due to a bad component, a product may be sent to a rework station for component replacement. Some products, however, may have two or more identical components for better reliability or higher capacity. For example, a rack holds a number of identical magnetic storage devices or a computer has multiple processors. Under this situation, different replacement policies may be considered. Bad components can be replaced by either untested ones or known good ones. In the latter case, the assembly line must keep an inventory of good components. Contingent on the condition of replacement components and the method of inventory replenishment, three different policies are identified. Under the first policy, all bad components will be replaced by untested ones, and therefore, the product must be retested. The second policy will replace all bad components with good ones if available. In this case, no additional test is required. However, if the number of bad components is greater than the inventory level, all components ( both good and bad) will be replaced by untested ones. Then the product is sent for retest and good components are placed in inventory. Policy III always replaces the bad ones with good ones. It is assumed that all (good) replacement components come from an independent source. If there are not enough good components for replacement, the product must wait. This paper investigates all three policies, using stochastic models. The performance of a policy is dependent on yield, test time, product configuration, and production demand. A good choice should consider the tradeoff between production lead time and inventory cycle. Numerical results, derived from a real-life case, are presented.

ISSN:0020-7543    

DOI:10.1080/00207549208948113

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

摘要:

Very often in a manufacturing plant, an important job order must be processed with higher priority than other regular jobs. It is vital for management to understand the impact on production performance of these high priority job orders. In this paper, this dynamic behaviour of assembly line systems is studied using computer simulation and derived metamodels in the form of mathematical expressions. An extensive computer simulation study is first conducted off-line. Next, analytical representations of the system lmetamodels) are fitted to the dynamic behaviour as simulated. These relatively simple equations can then be used on-line to estimate the impact of high priority job order processing when it occurs. The results indicate that the system response to this dynamic event can be described by first-order exponential delay functions captured in metamodels from simulation results. Using only the metamodels, the finish time of high priority jobs can be predicted and the number of delayed regular assembly products can be estimated in real time in the shop floor. This information is very valuable and useful for production scheduling.

ISSN:0020-7543    

DOI:10.1080/00207549208948114

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

摘要:

This paper addresses the application and comparison of spreadsheet analysis (LOTUS 123), rapid modeling (MANUPLAN II), and simulation (STARCELL) for the design and evaluation of manufacturing systems. The objective is to evaluate the quantity and quality of the required inputs, generated outputs, and the amount of time required for each of these analytical tools compared to the amount of knowledge gained. A comparison is conducted for an application to an automatic insertion card production facility where the best machine/cell configuration was emphasized to simplify the system and increase its efficiency. Results of this evaluation provide insights into the efforts and value obtained from Using increasingly more detailed modelling procedures

ISSN:0020-7543    

DOI:10.1080/00207549208948115

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

摘要:

This paper presents a new approach to concurrent engineering, namely the use of artificial intelligence constraint networks to advise the designer on improvements that can be made to the design from the perspective of the product's life-cycle. The difficulties associated with performing concurrent engineering are reviewed, and the various approaches to concurrent engineering are discussed. The requirements for a system to support concurrent engineering are indicated. An overview of constraint networks is given and this leads into a description of SPARK, an artificial intelligence constraint network systems for concurrent engineering. The operation of SPARK is illustrated by considering an example application of printed wiring board manufacture. The advantages of SPARK include being flexible enough to allow the designer to approach a problem from a variety of viewpoints, allowing the designer to design despite having incomplete information, and being able to handle the wide variety of life-cycle information requirements.

ISSN:0020-7543    

DOI:10.1080/00207549208948116

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor

ISSN:0020-7543    

DOI:10.1080/00207549208948118

出版商:Taylor & Francis Group    

年代:1992

数据来源: Taylor