An adaptive learning approach for no-wait flowshop scheduling problems to minimize make-span

DOI

10.2991/ijcis.2011.4.4.11How to use a DOI?

Keywords

No-wait flowshop; Adaptive learning approach; Genetic algorithm; Makespan

Abstract

No-wait flowshop scheduling problem (NW-FSSP) with the objective to minimize the makespan is an important sequencing problem in the production plans and applications of no-wait flowshops can be found in several industries. In a NW-FSSP, jobs are not allowed to wait between two successive machines. The NW-FSSPs are addressed to minimize makespan and the NW-FSSP is known as a NP- Hard problem. In this study, Agarwal et al.’s1 adaptive learning approach (ALA) is improvement for NW-FSSPs. Improvements in adaptive learning approach is similar to neural-network training. The improvement adaptive learning approach (IALA) is applied to all of the 192 problems. The proposed IALA method for NW-FSSP is compared with Aldowaisan and Allahverdi’s2 results by using Genetic heuristic. The results of computational experiments on randomly generated NW-FSSPs are show that the proposed adaptive learning approach performs quite well.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - Orhan Engin
AU  - Cengiz Gunaydin
PY  - 2011
DA  - 2011/06/01
TI  - An adaptive learning approach for no-wait flowshop scheduling problems to minimize make-span
JO  - International Journal of Computational Intelligence Systems
SP  - 521
EP  - 529
VL  - 4
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.4.11
DO  - 10.2991/ijcis.2011.4.4.11
ID  - Engin2011
ER  -