Research on disassembly depth optimization for waste smartphones based on improved TLBO algorithm

doi:10.16731/j.cnki.1671-3133.2024.03.016

Abstract

Abstract: Aiming at the problem that the existing disassembly depth decision method cannot objectively and accurately obtain the optimal disassembly depth of waste smartphones,a disassembly depth optimization method for waste smartphones based on improved Teaching-Learning-Based Optimization (TLBO) algorithm was proposed. The disassembly profit model,disassembly time model and disassembly energy consumption model were established and the disassembly depth optimization target was determined,an optimal disassembly depth solution set was obtained by the improved TLBO algorithm. The disassembly process of Xiaomi 5 was selected as an example to verify the effectiveness of the proposed method. The result show that the optimal solution search ability and the convergence speed of the proposed method are enhanced.

Key words: disassembly depth, disassembly model of waste smartphones, Circle chaotic map, TLBO algorithm, multi-objective optimization

CLC Number:

TH122

CHEN Zepeng, LI Lin, CHU Xiaojing, YIN Fengfu. Research on disassembly depth optimization for waste smartphones based on improved TLBO algorithm[J]. Modern Manufacturing Engineering, 2024, 522(3): 119-126.

References

[1] REN Y,JIN H,ZHAO F,et al.A multi-objective disassembly planning for value recovery and energy conservation from end-of-life products[J].IEEE Transactions on Automation Science and Engineering,2020,18(2):791-803.
[2] WANG K,LI X,GAO L,et al.Energy consumption and profit-oriented disassembly line balancing for waste electrical and electronic equipment[J].Journal of Cleaner Production,2020,265:121829.
[3] 田广东,张洪浩,王丹琦.基于模糊AHP-灰色关联TOPSIS的拆解方案评估研究[J].机械工程学报,2017,53(5):34-40.
[4] LAMBERT A J D.Optimizing disassembly processes subjected to sequence-dependent cost[J].Computers & Operations Research,2007,34(2):536-551.
[5] YEH W C.Optimization of the Disassembly Sequencing Problem on the Basis of Self-Adaptive Simplified Swarm Optimization[J].IEEE Transactions on Systems,Man,and Cybernetics-part A:Systems and Humans,2012,42(1):250-261.
[6] XIA K,GAO L,LI W,et al.Disassembly sequence planning using a simplified teaching-learning-based optimization algorithm[J].Advanced Engineering Informatics,2014,28:518-527.
[7] TSENG H E,CHANG C C,LEE S C,et al.A block-based genetic algorithm for disassembly sequence planning[J].Expert Systems with Applications,2018,96:492-505.
[8] XU W,TANG Q,LIU J,et al.Disassembly sequence planning using discrete bees algorithm for human-robot collaboration in remanufacturing[J].Robotics and Computer-integrated Manufacturing,2020,62:101860.
[9] XIE J,LI X,GAO L.Disassembly sequence planning based on a modified grey wolf optimizer[J].The International Journal of Advanced Manufacturing Technology,2021,116:3731-3750.
[10] 郭洪飞,陈志彬,任亚平,等.基于零件回收综合评价的废旧产品拆解序列与拆解深度集成决策研究[J].机械工程学报,2022,58(4):258-268.
[11] SMITH S,HSU L Y,SMITH G C.Partial disassembly sequence planning based on cost-benefit analysis[J].Journal of Cleaner Production,2016,139:729-739.
[12] ACHILLAS C,AIDONIS D,VLACHOKOSTAS C,et al.Depth of manual dismantling analysis:a cost-benefit approach[J].Waste Management,2013,33(4):948-956.
[13] 马淑梅,陆星润,徐立云.基于改进人工蜂群算法的废旧产品拆卸规划[J].现代制造技术与装备,2023,59(1):32-36,40.
[14] YANG Y,YUAN G,ZHUANG Q,et al.Multi-objective low-carbon disassembly line balancing for agricultural machinery using MDFOA and fuzzy AHP[J].Journal of Cleaner Production,2019,233:1465-1474.
[15] AICHA M,BELHADJ I,HAMMADI M,et al.A mathemati-cal formulation for processing time computing in disassembly lines and its optimization[J].Computers & Industrial Engineering,2022,165:107933.
[16] 王晓东,刘广阔,尹凤福,等.废旧手机拆解序列生成及优化研究[J].青岛大学学报(工程技术版),2022,37(3):23-31.
[17] 尹凤福,刘广阔,王晓东,等.基于多种群遗传算法的废旧手机拆卸序列规划[J].合肥工业大学学报(自然科学版),2023,46(4):438-446.
[18] 孙凤山,范孟豹,曹丙花,等.基于混沌映射与差分进化自适应教与学优化算法的太赫兹图像增强模型[J].仪器仪表学报,2021,42(4):92-101.