Abstract
Hopfiled-Tank (HT) neural nets have recently emerged as a potential approach to solve the NP-hard combinatorial optimization problems. Performance of the nets, however, are slow and their implementation is problem dependent. In this paper, we present an efficient implementation to solve the flow-shop scheduling (FSS) problems. An unique feature of our implementation is that it can be easily adapted to solve the FSS problem with different objective or multiple objectives; while maintaining good quality of solutions competing with the best known traditional heuristics.
Original language | English (US) |
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Title of host publication | Proceedings - Annual Meeting of the Decision Sciences Institute |
Editors | Anon |
Publisher | Decis Sci Inst |
Pages | 352-354 |
Number of pages | 3 |
Volume | 1 |
State | Published - 1998 |
Event | Proceedings of the 1997 Annual Meeting of the Decision Sciences Institute. Part 1 (of 3) - San Diego, CA, USA Duration: Nov 22 1997 → Nov 25 1997 |
Other
Other | Proceedings of the 1997 Annual Meeting of the Decision Sciences Institute. Part 1 (of 3) |
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City | San Diego, CA, USA |
Period | 11/22/97 → 11/25/97 |
All Science Journal Classification (ASJC) codes
- Management Information Systems
- Hardware and Architecture