Efficient neural network models for flow shop scheduling

Edward Chang, Chao Hsien Chu

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings - Annual Meeting of the Decision Sciences Institute
Editors Anon
PublisherDecis Sci Inst
Pages352-354
Number of pages3
Volume1
StatePublished - 1998
EventProceedings of the 1997 Annual Meeting of the Decision Sciences Institute. Part 1 (of 3) - San Diego, CA, USA
Duration: Nov 22 1997Nov 25 1997

Other

OtherProceedings of the 1997 Annual Meeting of the Decision Sciences Institute. Part 1 (of 3)
CitySan Diego, CA, USA
Period11/22/9711/25/97

All Science Journal Classification (ASJC) codes

  • Management Information Systems
  • Hardware and Architecture

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