PMID- 27652166
OWN - NLM
STAT- PubMed-not-MEDLINE
DCOM- 20160921
LR  - 20201001
IS  - 2193-1801 (Print)
IS  - 2193-1801 (Electronic)
IS  - 2193-1801 (Linking)
VI  - 5
IP  - 1
DP  - 2016
TI  - Hybrid Pareto artificial bee colony algorithm for multi-objective single machine 
      group scheduling problem with sequence-dependent setup times and learning 
      effects.
PG  - 1593
LID - 10.1186/s40064-016-3265-3 [doi]
LID - 1593
AB  - Group scheduling is significant for efficient and cost effective production 
      system. However, there exist setup times between the groups, which require to 
      decrease it by sequencing groups in an efficient way. Current research is focused 
      on a sequence dependent group scheduling problem with an aim to minimize the 
      makespan in addition to minimize the total weighted tardiness simultaneously. In 
      most of the production scheduling problems, the processing time of jobs is 
      assumed as fixed. However, the actual processing time of jobs may be reduced due 
      to "learning effect". The integration of sequence dependent group scheduling 
      problem with learning effects has been rarely considered in literature. 
      Therefore, current research considers a single machine group scheduling problem 
      with sequence dependent setup times and learning effects simultaneously. A novel 
      hybrid Pareto artificial bee colony algorithm (HPABC) with some steps of genetic 
      algorithm is proposed for current problem to get Pareto solutions. Furthermore, 
      five different sizes of test problems (small, small medium, medium, large medium, 
      large) are tested using proposed HPABC. Taguchi method is used to tune the 
      effective parameters of the proposed HPABC for each problem category. The 
      performance of HPABC is compared with three famous multi objective optimization 
      algorithms, improved strength Pareto evolutionary algorithm (SPEA2), 
      non-dominated sorting genetic algorithm II (NSGAII) and particle swarm 
      optimization algorithm (PSO). Results indicate that HPABC outperforms SPEA2, 
      NSGAII and PSO and gives better Pareto optimal solutions in terms of diversity 
      and quality for almost all the instances of the different sizes of problems.
FAU - Yue, Lei
AU  - Yue L
AD  - State Key Lab of Digital Manufacturing Equipment and Technology, HUST-SANY Joint 
      Lab of Advanced Manufacturing, Huazhong University of Science and Technology, 
      Wuhan, 430074 People's Republic of China.
FAU - Guan, Zailin
AU  - Guan Z
AD  - State Key Lab of Digital Manufacturing Equipment and Technology, HUST-SANY Joint 
      Lab of Advanced Manufacturing, Huazhong University of Science and Technology, 
      Wuhan, 430074 People's Republic of China.
FAU - Saif, Ullah
AU  - Saif U
AD  - State Key Lab of Digital Manufacturing Equipment and Technology, HUST-SANY Joint 
      Lab of Advanced Manufacturing, Huazhong University of Science and Technology, 
      Wuhan, 430074 People's Republic of China ; Department of Industrial Engineering, 
      University of Engineering and Technology, Taxila, Pakistan.
FAU - Zhang, Fei
AU  - Zhang F
AD  - State Key Lab of Digital Manufacturing Equipment and Technology, HUST-SANY Joint 
      Lab of Advanced Manufacturing, Huazhong University of Science and Technology, 
      Wuhan, 430074 People's Republic of China.
FAU - Wang, Hao
AU  - Wang H
AD  - State Key Lab of Digital Manufacturing Equipment and Technology, HUST-SANY Joint 
      Lab of Advanced Manufacturing, Huazhong University of Science and Technology, 
      Wuhan, 430074 People's Republic of China.
LA  - eng
PT  - Journal Article
DEP - 20160917
PL  - Switzerland
TA  - Springerplus
JT  - SpringerPlus
JID - 101597967
PMC - PMC5026988
OTO - NOTNLM
OT  - Group scheduling
OT  - Hybrid Pareto artificial bee colony algorithm
OT  - Learning effect
OT  - Multi-objectives
OT  - Sequence dependent setup
OT  - Taguchi method
EDAT- 2016/09/22 06:00
MHDA- 2016/09/22 06:01
PMCR- 2016/09/17
CRDT- 2016/09/22 06:00
PHST- 2016/07/01 00:00 [received]
PHST- 2016/09/07 00:00 [accepted]
PHST- 2016/09/22 06:00 [entrez]
PHST- 2016/09/22 06:00 [pubmed]
PHST- 2016/09/22 06:01 [medline]
PHST- 2016/09/17 00:00 [pmc-release]
AID - 3265 [pii]
AID - 10.1186/s40064-016-3265-3 [doi]
PST - epublish
SO  - Springerplus. 2016 Sep 17;5(1):1593. doi: 10.1186/s40064-016-3265-3. eCollection 
      2016.