Modeling impact of choice complexity on production rate in mixed-model assembly system

被引：2

作者：

Wang, Kunpeng ^{[1
]}

Rao, Yunqing ^{[1
]}

Wang, Mengchang ^{[1
]}

机构：

[1] Huazhong Univ Sci & Technol, State Key Lab Digital Mfg Equipment & Technol, Wuhan 430074, Hubei, Peoples R China

来源：

INTERNATIONAL JOURNAL OF ADVANCED MANUFACTURING TECHNOLOGY | 2012年 / 59卷 / 9-12期

基金：

中国国家自然科学基金;

关键词：

Choice complexity; Error rate; Meta-model; Artificial neural network; MANUFACTURING SYSTEMS; NEURAL-NETWORKS; INFORMATION; OPERATIONS; VARIETY; DESIGN;

D O I：

10.1007/s00170-011-3530-0

中图分类号：

TP [自动化技术、计算机技术];

学科分类号：

0812 ;

摘要：

The increasing variety of products complicates the mixed-model assembly process and affected the mixed-model assembly system in terms of product quality and productivity. Choice complexity comes from the process of making choices for various assembly operations due to the product variety and impacts production rate which is the performance measure of the system. The choice complexity is measured with information entropy, and the relational expression between choice complexity and error rate is analyzed by means of those research finds on average reaction time and speed-accuracy trade-off. The main achievement of our study is establishing an artificial neural network meta-model for the impact of choice complexity on production rate. The meta-model performs better than a multiple linear regression meta-model in terms of experiment results and appears to be the optimal model of the impact of choice complexity on production rate in the mixed-model assembly system.

引用

页码：1181 / 1189

页数：9

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