The paper deals with the system of parallel flow production lines. Each line can be dynamically divided into partitions designated to concurrent processing different products. The production flow through machines belo...
详细信息
The paper deals with the system of parallel flow production lines. Each line can be dynamically divided into partitions designated to concurrent processing different products. The production flow through machines belonging to the partition is synchronized. The set of products which can be manufactured in a given line depends on tools the line is equipped with. Each feasible set of products processed concurrently in the line has been named the preparedness variant. There are many algorithms to control switching the lines between their preparedness variants e.g. priority rules or the Follow-up Scheduling. The quality of current control for the flow production lines strongly depends on previous decision of the preparedness variants configuration. An optimization model for this problem and computational example have been presented in the paper.
A machine learning system named Galactica has been developed which uses a genetic algorithm to discover the rules for an expert system from databases. Galactica devised accurate diagnostic rules for female urinary inc...
详细信息
A machine learning system named Galactica has been developed which uses a genetic algorithm to discover the rules for an expert system from databases. Galactica devised accurate diagnostic rules for female urinary incontinence from difficult heterogeneous data. The percentages of correctly classified stress, mixed and sensory urge incontinence testing cases were 89, 86 and 87%, respectively. However, these rules were rather general, consisting of 4-6 out of 13 conditions available in the data. Diagnostic rules for stress and mixed incontinence extracted from straightforward homogeneous data were highly accurate, classifying 100% of testing cases correctly as well as being specific, having from 10 to 11 conditions. More specific, but less accurate, rules were found from heterogeneous data with a biased fitness function. All of the rules were correct, i.e. every condition in the rules had the expected value specified by the expert. Although, Galactica achieved a slightly better classification than the discriminant analysis, it is argued that the genetic approach is better than the statistical one, due to symbolic rules being comprehensible, whereas understanding. a complex mathematical model requires statistical expertise. (C) 1998 Elsevier Science Ireland Ltd. All rights reserved.
暂无评论