Applied Hopfield-Tank Neural Network to Single Machine
碩士 === 元智大學 === 工業工程與管理學系 === 90 === The major tendency orientation of the production system has been changed to meet the customer demand in the recent years. It results in frequent changeovers in the production process. Consequently, many enterprises have switched systems from large- qua...
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ndltd-TW-090YZU000310272016-06-24T04:15:30Z http://ndltd.ncl.edu.tw/handle/98241652622656142415 Applied Hopfield-Tank Neural Network to Single Machine 應用霍普菲爾-坦克神經網路於單機排程上之研究 Wen-Hao Lu 呂文豪 碩士 元智大學 工業工程與管理學系 90 The major tendency orientation of the production system has been changed to meet the customer demand in the recent years. It results in frequent changeovers in the production process. Consequently, many enterprises have switched systems from large- quantity production into the small-size large-variety mode. The research of scheduling concerning the setup time is getting more and more. According to this situation, a Hopfield-Tank Neural Network (HTNN) was applied to a single machine scheduling problem with minimizing the tardiness and the setup time is addressed in this thesis. When the problem size is small, the result of the HTNN is not far away from the optimal solutions acquired by using LINGO. The maximal error approximates to 2.6% and the computation of HTNN takes much less time. When the problem size is large, the HTNN was compared with the Apparent Tardiness Cost and Setup (ATCS) rule. The experimental result indicated that HTNN is superior to the ATCS in the quality of solution. Pei-Chann Chang 張百棧 2002 學位論文 ; thesis 68 zh-TW |
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碩士 === 元智大學 === 工業工程與管理學系 === 90 === The major tendency orientation of the production system has been changed to meet the customer demand in the recent years. It results in frequent changeovers in the production process. Consequently, many enterprises have switched systems from large- quantity production into the small-size large-variety mode. The research of scheduling concerning the setup time is getting more and more. According to this situation, a Hopfield-Tank Neural Network (HTNN) was applied to a single machine scheduling problem with minimizing the tardiness and the setup time is addressed in this thesis. When the problem size is small, the result of the HTNN is not far away from the optimal solutions acquired by using LINGO. The maximal error approximates to 2.6% and the computation of HTNN takes much less time. When the problem size is large, the HTNN was compared with the Apparent Tardiness Cost and Setup (ATCS) rule. The experimental result indicated that HTNN is superior to the ATCS in the quality of solution.
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author2 |
Pei-Chann Chang |
author_facet |
Pei-Chann Chang Wen-Hao Lu 呂文豪 |
author |
Wen-Hao Lu 呂文豪 |
spellingShingle |
Wen-Hao Lu 呂文豪 Applied Hopfield-Tank Neural Network to Single Machine |
author_sort |
Wen-Hao Lu |
title |
Applied Hopfield-Tank Neural Network to Single Machine |
title_short |
Applied Hopfield-Tank Neural Network to Single Machine |
title_full |
Applied Hopfield-Tank Neural Network to Single Machine |
title_fullStr |
Applied Hopfield-Tank Neural Network to Single Machine |
title_full_unstemmed |
Applied Hopfield-Tank Neural Network to Single Machine |
title_sort |
applied hopfield-tank neural network to single machine |
publishDate |
2002 |
url |
http://ndltd.ncl.edu.tw/handle/98241652622656142415 |
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