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 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/2
Y1 - 2018/7/2
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 -