TY - GEN
T1 - An ant colony optimization-based hyper-heuristic with genetic programming approach for a hybrid flow shop scheduling problem
AU - Chen, Lin
AU - Zheng, Hong
AU - Zheng, Dan
AU - Li, Dongni
N1 - Publisher Copyright:
© 2015 IEEE.
PY - 2015/9/10
Y1 - 2015/9/10
N2 - The problem of a k-stage hybrid flow shop (HFS) with one stage composed of non-identical batch processing machines and the others consisting of non-identical single processing machines is analyzed in the context of the equipment manufacturing industry. Due to the complexity of the addressed problem, a hyper-heuristic which combines heuristic generation and heuristic search is proposed to solve the problem. For each sub-problem, i.e., part assignment, part sequencing and batch formation, heuristic rules are first generated by genetic programming (GP) offline and then selected by ant colony optimization (ACO) correspondingly. Finally, the scheduling solutions are obtained through the above generated combinatorial heuristic rules. Aiming at minimizing the total weighted tardiness of parts, a comparison experiment with the other hyper-heuristic for the same HFS problem is conducted. The result has shown that the proposed algorithm has advantages over the other method with respect to the total weighted tardiness.
AB - The problem of a k-stage hybrid flow shop (HFS) with one stage composed of non-identical batch processing machines and the others consisting of non-identical single processing machines is analyzed in the context of the equipment manufacturing industry. Due to the complexity of the addressed problem, a hyper-heuristic which combines heuristic generation and heuristic search is proposed to solve the problem. For each sub-problem, i.e., part assignment, part sequencing and batch formation, heuristic rules are first generated by genetic programming (GP) offline and then selected by ant colony optimization (ACO) correspondingly. Finally, the scheduling solutions are obtained through the above generated combinatorial heuristic rules. Aiming at minimizing the total weighted tardiness of parts, a comparison experiment with the other hyper-heuristic for the same HFS problem is conducted. The result has shown that the proposed algorithm has advantages over the other method with respect to the total weighted tardiness.
KW - ant colony optimization
KW - discrete event systems
KW - genetic programming
KW - scheduling
UR - http://www.scopus.com/inward/record.url?scp=84963502494&partnerID=8YFLogxK
U2 - 10.1109/CEC.2015.7256975
DO - 10.1109/CEC.2015.7256975
M3 - Conference contribution
AN - SCOPUS:84963502494
T3 - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
SP - 814
EP - 821
BT - 2015 IEEE Congress on Evolutionary Computation, CEC 2015 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - IEEE Congress on Evolutionary Computation, CEC 2015
Y2 - 25 May 2015 through 28 May 2015
ER -