标题：Stochastic Dual-objective Disassembly Sequence Planning with Consideration of Learning Effect
作者：Guo, XiWang; Zhou, MengChu; Fu, YaPing; Qi, Liang; You, Dan
作者机构：[Guo, XiWang] Liaoning Shihua Univ, Coll Comp & Commun Engn, Fushun 113001, Peoples R China.; [Zhou, MengChu] New Jersey Inst Technol, Dept Elect & 更多
会议名称：16th IEEE International Conference on Networking, Sensing and Control (ICNSC)
会议日期：MAY 09-11, 2019
来源：PROCEEDINGS OF THE 2019 IEEE 16TH INTERNATIONAL CONFERENCE ON NETWORKING, SENSING AND CONTROL (ICNSC 2019)
关键词：remanufacture; disassembly sequencing planning problem; learning effect;; multi-population; multi-objective evolutionary algorithm; stochastic; dual-objective
摘要：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.