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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| Format: | Others |
| Language: | zh-TW |
| Published: |
2002
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| Online Access: | http://ndltd.ncl.edu.tw/handle/98241652622656142415 |
| Summary: | 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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