The Study of Determining the Optimum Process Mean and Screening Limits

碩士 === 南台科技大學 === 工業管理研究所 === 92 === In this paper, we present a modified Lee et al.’s model for determining the optimum process parameters. There are three grades for the product after 100% inspection, that is, rework, conformance, and scrap. The under-filled product is scrapped, the conformance pr...

Full description

Bibliographic Details
Main Author: 吳龍志
Other Authors: 陳忠和
Format: Others
Language:zh-TW
Published: 2004
Online Access:http://ndltd.ncl.edu.tw/handle/14897618428078546555
id ndltd-TW-092STUT0041023
record_format oai_dc
spelling ndltd-TW-092STUT00410232016-11-22T04:12:23Z http://ndltd.ncl.edu.tw/handle/14897618428078546555 The Study of Determining the Optimum Process Mean and Screening Limits 最佳製程平均數及篩選界限設定之研究 吳龍志 碩士 南台科技大學 工業管理研究所 92 In this paper, we present a modified Lee et al.’s model for determining the optimum process parameters. There are three grades for the product after 100% inspection, that is, rework, conformance, and scrap. The under-filled product is scrapped, the conformance product is stored and sold for a fixed price, and the over-filled product is reworked. Two situations of the rework cost are considered. One is the constant and the other one is the linear reprocessing cost. Both the perfect rework and the imperfect rework processes for the product are proposed in the modified model. The accepted item has the storehouse cost which is related with the storehouse time. Assume that the storehouse time is truncated normal distributed. When the performance variable is impossible to measure directly, we need to adopt the surrogate one. Assume that the latter is highly correlated with the former. The objective of this study is to find the optimum process mean of performance variable and screening limit of surrogate variable for obtaining the maximum expected profit per the item. 陳忠和 2004 學位論文 ; thesis 88 zh-TW
collection NDLTD
language zh-TW
format Others
sources NDLTD
description 碩士 === 南台科技大學 === 工業管理研究所 === 92 === In this paper, we present a modified Lee et al.’s model for determining the optimum process parameters. There are three grades for the product after 100% inspection, that is, rework, conformance, and scrap. The under-filled product is scrapped, the conformance product is stored and sold for a fixed price, and the over-filled product is reworked. Two situations of the rework cost are considered. One is the constant and the other one is the linear reprocessing cost. Both the perfect rework and the imperfect rework processes for the product are proposed in the modified model. The accepted item has the storehouse cost which is related with the storehouse time. Assume that the storehouse time is truncated normal distributed. When the performance variable is impossible to measure directly, we need to adopt the surrogate one. Assume that the latter is highly correlated with the former. The objective of this study is to find the optimum process mean of performance variable and screening limit of surrogate variable for obtaining the maximum expected profit per the item.
author2 陳忠和
author_facet 陳忠和
吳龍志
author 吳龍志
spellingShingle 吳龍志
The Study of Determining the Optimum Process Mean and Screening Limits
author_sort 吳龍志
title The Study of Determining the Optimum Process Mean and Screening Limits
title_short The Study of Determining the Optimum Process Mean and Screening Limits
title_full The Study of Determining the Optimum Process Mean and Screening Limits
title_fullStr The Study of Determining the Optimum Process Mean and Screening Limits
title_full_unstemmed The Study of Determining the Optimum Process Mean and Screening Limits
title_sort study of determining the optimum process mean and screening limits
publishDate 2004
url http://ndltd.ncl.edu.tw/handle/14897618428078546555
work_keys_str_mv AT wúlóngzhì thestudyofdeterminingtheoptimumprocessmeanandscreeninglimits
AT wúlóngzhì zuìjiāzhìchéngpíngjūnshùjíshāixuǎnjièxiànshèdìngzhīyánjiū
AT wúlóngzhì studyofdeterminingtheoptimumprocessmeanandscreeninglimits
_version_ 1718396082818383872