TY - GEN

T1 - Stochastic dual-objective disassembly sequence planning with consideration of learning effect

AU - Guo, Xiwang

AU - Zhou, Mengchu

AU - Fu, Yaping

AU - Qi, Liang

AU - You, Dan

PY - 2019/5

Y1 - 2019/5

N2 - In an actual remanufacturing process, a human operator is able to continuously learn the disassembly knowledge of an end-of-life product when he/she disassembles it, which makes him/her disassemble it more proficiently. In order to describe this feature, this work proposes a stochastic dual-objective disassembly sequencing planning problem considering human learning effects. In this problem, actual disassembly and setup time of operations are a function of their normal time and starting time. A new mathematical model is established to maximize total disassembly profit and minimize disassembly time. In order to solve this problem efficiently, a multi-population multi-objective evolutionary algorithm is developed. In this algorithm, some special strategies, e.g., solution representation, crossover operator and mutation operator, are newly designed based on this problem's characteristics. Its effectiveness is well illustrated through several numerical cases and by comparing it with two prior approaches, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition. Experimental results demonstrate that the proposed algorithm performs well in solving this problem.

AB - In an actual remanufacturing process, a human operator is able to continuously learn the disassembly knowledge of an end-of-life product when he/she disassembles it, which makes him/her disassemble it more proficiently. In order to describe this feature, this work proposes a stochastic dual-objective disassembly sequencing planning problem considering human learning effects. In this problem, actual disassembly and setup time of operations are a function of their normal time and starting time. A new mathematical model is established to maximize total disassembly profit and minimize disassembly time. In order to solve this problem efficiently, a multi-population multi-objective evolutionary algorithm is developed. In this algorithm, some special strategies, e.g., solution representation, crossover operator and mutation operator, are newly designed based on this problem's characteristics. Its effectiveness is well illustrated through several numerical cases and by comparing it with two prior approaches, i.e., nondominated sorting genetic algorithm II and multi-objective evolutionary algorithm based on decomposition. Experimental results demonstrate that the proposed algorithm performs well in solving this problem.

KW - Disassembly sequencing planning problem

KW - Learning effect

KW - Multi-objective evolutionary algorithm

KW - Multi-population

KW - Remanufacture

KW - Stochastic dual-objective

UR - http://www.scopus.com/inward/record.url?scp=85068748366&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85068748366&partnerID=8YFLogxK

U2 - 10.1109/ICNSC.2019.8743161

DO - 10.1109/ICNSC.2019.8743161

M3 - Conference contribution

T3 - Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019

SP - 29

EP - 34

BT - Proceedings of the 2019 IEEE 16th International Conference on Networking, Sensing and Control, ICNSC 2019

A2 - Zhu, Haibin

A2 - Wang, Jiacun

A2 - Zhou, MengChu

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE International Conference on Networking, Sensing and Control, ICNSC 2019

Y2 - 9 May 2019 through 11 May 2019

ER -