PMID- 31545754
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20200924
IS  - 2168-2275 (Electronic)
IS  - 2168-2267 (Linking)
VI  - 50
IP  - 10
DP  - 2020 Oct
TI  - Cooperative Coevolutionary Bare-Bones Particle Swarm Optimization With Function 
      Independent Decomposition for Large-Scale Supply Chain Network Design With 
      Uncertainties.
PG  - 4454-4468
LID - 10.1109/TCYB.2019.2937565 [doi]
AB  - Supply chain network design (SCND) is a complicated constrained optimization 
      problem that plays a significant role in the business management. This article 
      extends the SCND model to a large-scale SCND with uncertainties (LUSCND), which 
      is more practical but also more challenging. However, it is difficult for 
      traditional approaches to obtain the feasible solutions in the large-scale search 
      space within the limited time. This article proposes a cooperative coevolutionary 
      bare-bones particle swarm optimization (CCBBPSO) with function independent 
      decomposition (FID), called CCBBPSO-FID, for a multiperiod three-echelon LUSCND 
      problem. For the large-scale issue, binary encoding of the original model is 
      converted to integer encoding for dimensionality reduction, and a novel FID is 
      designed to efficiently decompose the problem. For obtaining the feasible 
      solutions, two repair methods are designed to repair the infeasible solutions 
      that appear frequently in the LUSCND problem. A step translation method is 
      proposed to deal with the variables out of bounds, and a labeled reposition 
      operator with adaptive probabilities is designed to repair the infeasible 
      solutions that violate the constraints. Experiments are conducted on 405 
      instances with three different scales. The results show that CCBBPSO-FID has an 
      evident superiority over contestant algorithms.
FAU - Zhang, Xin
AU  - Zhang X
FAU - Du, Ke-Jing
AU  - Du KJ
FAU - Zhan, Zhi-Hui
AU  - Zhan ZH
FAU - Kwong, Sam
AU  - Kwong S
FAU - Gu, Tian-Long
AU  - Gu TL
FAU - Zhang, Jun
AU  - Zhang J
LA  - eng
PT  - Journal Article
DEP - 20190920
PL  - United States
TA  - IEEE Trans Cybern
JT  - IEEE transactions on cybernetics
JID - 101609393
SB  - IM
EDAT- 2019/09/24 06:00
MHDA- 2019/09/24 06:01
CRDT- 2019/09/24 06:00
PHST- 2019/09/24 06:00 [pubmed]
PHST- 2019/09/24 06:01 [medline]
PHST- 2019/09/24 06:00 [entrez]
AID - 10.1109/TCYB.2019.2937565 [doi]
PST - ppublish
SO  - IEEE Trans Cybern. 2020 Oct;50(10):4454-4468. doi: 10.1109/TCYB.2019.2937565. 
      Epub 2019 Sep 20.