The product’s service life has been shortened rapidly along with the development of modern technologies and human esthetic evolutions, resulting in a great number of end-of-life (EOL) products. Disassembling renewable parts from EOL products for remanufacturing is of great significance to save resources and protect the environment. The existing studies on selective disassembly sequence planning (SDSP) largely focus on the sequential disassembly planning, which is inefficient especially for complex products because it is a linear process where only one component can be removed at a time. Thus, this work focuses on parallel SDSP and further proposes a multiple-target asynchronous parallel selective DSP (APSDSP) with the objectives of minimizing disassembly time and maximizing disassembly profit simultaneously. In APSDSP, operators can remove multiple components simultaneously as long as disassembly constraints are not violated, and without synchronization requirement between operators. The feasible disassembly sequence and disassembly direction sequence are generated by a space interference matrix method (SIMM) to meet the actual disassembly environment. Based on SIMM, an improved multi-objective evolutionary algorithm based on multiple neighborhood search strategy is developed to create the pareto frontier of the problem. Finally, two cases study are presented to validate the effectiveness of the proposed methodology. It gives enterprises new insights to reduce disassembly time and improve disassembly profit.
ZhouZLiuJPhamDT, et al. Disassembly sequence planning: Recent developments and future trends. Proc IMechE, Part B: J Engineering Manufacture2019; 233(5): 1450–1471.
2.
HuangJPhamDTWangY, et al. A case study in human–robot collaboration in the disassembly of press-fitted components. Proc IMechE, Part B: J Engineering Manufacture2020; 234(3): 654–664.
3.
TaoFBiLZuoY, et al. Partial/parallel disassembly sequence planning for complex products. J Manuf Sci Eng2018; 140(1): 011–016.
4.
LiWDXiaKGaoL, et al. Selective disassembly planning for waste electrical and electronic equipment with case studies on liquid crystaldisplays. Robot Comp Integr Manuf2013; 29(4): 248–260.
5.
ZhangNLiuZQiuC, et al. Disassembly sequence planning using a fast and effective precedence-based disassembly subset-generation method. Proc IMechE, Part B: J Engineering Manufacture2020; 234(3): 513–526.
6.
GunjiBMPabbaSKRajaramIRS, et al. Optimal disassembly sequence generation and disposal of parts using stability graph cut-set method for end of life product. Sadhana2021; 46(1): 1–15.
7.
BahubalendruniMVARVarupalaVP. Disassembly sequence planning for safe disposal of End-of-Life Waste Electric and electronic equipment. Natl Acad Sci Lett2021; 44(3): 243–247.
8.
Kumar GulivindalaAV. A. Raju BahubalendruniMChandrasekarR, et al. Automated disassembly sequence prediction for Industry 4.0 using enhanced genetic algorithm. Comput Mater Con2021; 69(2): 2531–2548.
9.
KhederMTriguiMAifaouiN.Disassembly sequence planning based on a genetic algorithm. Proc IMechE, Part C: J Mechanical Engineering Science2015; 229(12): 2281–2290.
10.
SmithSHsuLYSmithGC.Partial disassembly sequence planning based on cost-benefit analysis. J Clean Prod2016; 139: 729–739.
11.
Anil KumarGBahubalendruniMVARPrasadVSS, et al. A multi-layered disassembly sequence planning method to support decision making in de-manufacturing. Sadhana2021; 46(2): 1–16.
12.
TriguiMBelhadjIBenamaraA.Disassembly plan approach based on subassembly concept. Int J Adv Manuf Technol2017; 90(1–4): 219–231.
13.
HanHJYuJMLeeDH.Mathematical model and solution algorithms for selective disassembly sequencing with multiple target components and sequence-dependent setups. Int J Prod Res2013; 51(16): 4997–5010.
JeunetJDella CroceFSalassaF.Heuristic solution methods for the selective disassembly sequencing problem under sequence-dependent costs. Comput Oper Res2021; 127(2): 105151.
16.
BehdadSKwakMKimH, et al. Simultaneous selective disassembly and end-of-life decision making for multiple products that share disassembly operations. J Mech Des2010; 132: 313–321.
17.
PistolesiFLazzeriniB.TeMA: A tensorial memetic algorithm for many-objective parallel disassembly sequence planning in product refurbishment. IEEE Trans Ind Inform2019; 15(6): 3743–3753.
18.
KimHWLeeDH.An optimal algorithm for selective disassembly sequencing with sequence-dependent set-ups in parallel disassembly environment. Int J Prod Res2017; 55(24): 7317–7333.
19.
SmithSHungPY.A novel selective parallel disassembly planning method for green design. J Eng Des2015; 26(10–12): 283–301.
20.
KimHWParkCLeeDH.Selective disassembly sequencing with random operation times in parallel disassembly environment. Int J Prod Res2018; 56(24): 7243–7257.
21.
ZhangLWuYLiZ, et al. A systematic approach in remanufacturing for high efficiency and low cost: the selective parallel disassembly sequence planning. Proc IMechE, Part B: J Engineering Manufacture2022; 236: 572–585.
22.
RenYZhangCZhaoF, et al. An asynchronous parallel disassembly planning based on genetic algorithm. Eur J Oper Res2018; 269(2): 647–660.
23.
ZhuJFXuZGSuKY, et al. Asynchronous parallel disassembly sequence planning for multi-manipulator based on improved shuffled frog leaping algorithm. SN Appl Sci2020; 2(5): 906.
24.
XingYWuDQuL.Parallel disassembly sequence planning using improved ant colony algorithm. Int J Adv Manuf Technol2021; 113(7–8): 2327–2342.
25.
WangKGaoLLiX, et al. Energy-efficient robotic parallel disassembly sequence planning for End-of-Life Products. IEEE Trans Autom Sci Eng2022; 19: 1277–1285.
26.
LambertAJDGuptaSM. Methods for optimum and near optimum disassembly sequencing. Int J Prod Res2008; 46(11): 2845–2865.
27.
SmithSSChenWH.Rule-based recursive selective disassembly sequence planning for green design. Adv Eng Inform2011; 25(1): 77–87.
28.
KimDHDohHHLeeDH.Multi-period disassembly levelling and lot-sizing for multiple product types with parts commonality. Proc IMechE, Part B: J Engineering Manufacture2018; 232(5): 867–878.
29.
JinGLiWWangS, et al. A systematic selective disassembly approach for waste electrical and electronic equipment with case study on liquid crystal display televisions. Proc IMechE, Part B: J Engineering Manufacture2017; 231(13): 2261–2278.
30.
OngSKChangMMLNeeAYC. Product disassembly sequence planning: state-of-the-art, challenges, opportunities and future directions. Int J Prod Res2021; 59(11): 3493–3508.
31.
LuQRenYJinH, et al. A hybrid metaheuristic algorithm for a profit-oriented and energy-efficient disassembly sequencing problem. Robot Comp Integr Manuf2020; 61: 101828.
32.
LuoYPengQGuP.Integrated multi-layer representation and ant colony search for product selective disassembly planning. Comput Ind2016; 75: 13–26.
33.
XiaXZhuHZhangZ, et al. 3D-based multi-objective cooperative disassembly sequence planning method for remanufacturing. Int J Adv Manuf Technol2020; 106(9–10): 4611–4622.
34.
RenYTianGZhaoF, et al. Selective cooperative disassembly planning based on multi-objective discrete artificial bee colony algorithm. Eng Appl Artif Intell2017; 64: 415–431.
35.
SenthilkumarCGanesanGKarthikeyanR.Bi-performance optimization of electrochemical machining characteristics of Al/20%SiCp composites using NSGA-II. Proc IMechE, Part B: J Engineering Manufacture2010; 224(9): 1399–1407.
36.
KondayyaDKrishnaAG.An integrated evolutionary approach for modelling and optimization of wire electrical discharge machining. Proc IMechE, Part B: J Engineering Manufacture2011; 225(4): 549–567.
37.
BabazadehHAlavidoostMHFazel ZarandiMH, et al. An enhanced NSGA-II algorithm for fuzzy bi-objective assembly line balancing problems. Comput Ind Eng2018; 123: 189–208.
38.
YehCT.An improved NSGA2 to solve a bi-objective optimization problem of multi-state electronic transaction network. Reliab Eng Syst Saf2019; 191: 106578.
39.
DebKPratapAAgarwalS, et al. A fast and elitist multiobjective genetic algorithm: NSGA-II. IEEE Trans Evol Comput2002; 6(2): 182–197.
40.
WangKLiXGaoL.Modeling and optimization of multi-objective partial disassembly line balancing problem considering hazard and profit. J Clean Prod2019; 211: 115–133.
41.
FangYMingHLiM, et al. Multi-objective evolutionary simulated annealing optimisation for mixed-model multi-robotic disassembly line balancing with interval processing time. Int J Prod Res2020; 58(3): 846–862.
42.
VanneschiLHenriquesRCastelliM.Multi-objective genetic algorithm with variable neighbourhood search for the electoral redistricting problem. Swarm Evol Comput2017; 36: 37–51.
43.
QiuYWangLXuX, et al. A variable neighborhood search heuristic algorithm for production routing problems. Appl Soft Comput2018; 66: 311–318.
44.
ZeidIBDohHHShinJH, et al. Fast and meta heuristics for part selection in flexible manufacturing systems with controllable processing times. Proc IMechE, Part B: J Engineering Manufacture2021; 235(4): 650–662.
45.
RiponKSNGletteKKhanKN, et al. Adaptive variable neighborhood search for solving multi-objective facility layout problems with unequal area facilities. Swarm Evol Comput2013; 8(1): 1–12.
46.
LiuMYiSWenP.Quantum-inspired hybrid algorithm for integrated process planning and scheduling. Proc IMechE, Part B: J Engineering Manufacture2018; 232(6): 1105–1122.
47.
ZhuLZhangZGuanC.Multi-objective partial parallel disassembly line balancing problem using hybrid group neighbourhood search algorithm. J Manuf Syst2020; 56: 252–269.
48.
LiuJZhouZPhamDT, et al. Collaborative optimization of robotic disassembly sequence planning and robotic disassembly line balancing problem using improved discrete bees algorithm in remanufacturing. Robot Comp Integr Manuf2020; 61: 101829.1–101829.18.
49.
GuoJZhongJLiY, et al. A hybrid artificial fish swam algorithm for disassembly sequence planning considering setup time. Assem Autom2019; 39(1): 140–153.
50.
LiuLZhouH.Hybridization of harmony search with variable neighborhood search for restrictive single-machine earliness/tardiness problem. Inf Sci2013; 226: 68–92.
51.
ShiRLeeKY.Multi-objective optimization of Electric Vehicle Fast Charging stations with SPEA-II. IFAC-PapersOnline2015; 48(30): 535–540.
52.
EricksonMMayerAHornJ.Multi-objective optimal design of groundwater remediation systems: application of the niched Pareto genetic algorithm (NPGA). Adv Water Resour2002; 25(1): 51–65.
53.
WangKLiXGaoL, et al. Partial disassembly line balancing for energy consumption and profit under uncertainty. Robot Comp Integr Manuf2019; 59: 235–251.
54.
CoelloCACCortésNC. Solving multiobjective optimization problems using an artificial immune system. Genet Program Evolvable Mach2005; 6: 163–190.
55.
GuoJPuZDuB, et al. Multi-objective optimisation of stochastic hybrid production line balancing including assembly and disassembly tasks. Int J Prod Res2022; 60: 2884–2900.
