TY - GEN
T1 - Stochastic Disassembly Sequence Optimization for Profit and Energy Consumption
AU - Fu, Yaping
AU - Zhou, Mengchu
AU - Guo, Xiwang
AU - Qi, Liang
N1 - Funding Information:
ACKNOWLEDGMENT (Heading 5) The authors would like to thank the National Natural Science Foundation of China under Grant No. 61703220; Shandong Provincial Natural Science Foundation, China under Grant No ZR2016FP02, Postdoctoral Science Foundation Project of China under Grant No 2017M610407 and Qingdao Postdoctoral Research Project under Grant No 2016027. Liaoning Province Dr. Research Foundation of China under Grant No. 20175032.
Publisher Copyright:
© 2018 IEEE.
PY - 2019/1/16
Y1 - 2019/1/16
N2 - Industrial products' reuse, recovery and recycling are very important because of their environmental and economic benefits. Effective disassembly sequencing can improve recovery revenue and reduce environment impact. In this work, a stochastic dual-objective disassembly sequencing problem is established, which includes maximizing disassembly profit and minimizing energy consumption. Two popular and classical multi-objective evolutionary algorithms, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition, are used to deal with this important problem. By conducting simulation experiments on several numerical cases and analyzing experimental results with two well-known performance metrics, i.e., inverted generational distance and hypervolume, this work concludes that both can be used to obtain highly desired solutions.
AB - Industrial products' reuse, recovery and recycling are very important because of their environmental and economic benefits. Effective disassembly sequencing can improve recovery revenue and reduce environment impact. In this work, a stochastic dual-objective disassembly sequencing problem is established, which includes maximizing disassembly profit and minimizing energy consumption. Two popular and classical multi-objective evolutionary algorithms, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition, are used to deal with this important problem. By conducting simulation experiments on several numerical cases and analyzing experimental results with two well-known performance metrics, i.e., inverted generational distance and hypervolume, this work concludes that both can be used to obtain highly desired solutions.
KW - Disassembly sequence optimization
KW - energy consumption optimization
KW - multi-objective evolutionary algorithm based on decomposition
KW - nondominated sorting genetic algorithm II
UR - http://www.scopus.com/inward/record.url?scp=85062209798&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85062209798&partnerID=8YFLogxK
U2 - 10.1109/SMC.2018.00246
DO - 10.1109/SMC.2018.00246
M3 - Conference contribution
AN - SCOPUS:85062209798
T3 - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
SP - 1410
EP - 1415
BT - Proceedings - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2018
Y2 - 7 October 2018 through 10 October 2018
ER -