Abstract
Electric vehicle production is subjected to high manufacturing cost and environmental impact. Disassembling and remanufacturing the lithium-ion power packs can highly promote electric vehicle market penetration by procuring and regrouping reusable modules as stationary energy storage devices and cut life-cycle cost and environmental impact. Disassembly efficiency is crucial for battery remanufacturing companies in reverse supply chains. However, disassembly planning suffers from high computational complexity and inferior solutions. This paper developed a multi-objective mathematical model and presented a novel hybrid genetic-firework algorithm based on the precedence graph for obtaining solutions to disassemble the electric vehicle power pack into module levels in an efficient manner. The objectives for the model include not only smoothness of working stations, cycle time, and economic returns, but also consider operation safety and energy consumption. The proposed hybrid algorithm explored the performance of the novel solution searching mechanism of combining the firework and genetic algorithms. The proposed approach is compared with the commonly used multi-objective evolutionary algorithms in the literature, showing its feasibility and effectiveness.
References
1.
Gu
, X.
, Zhou
, L.
, Huang
, H.
, Shi
, X.
, and Ieromonachou
, P.
, 2021
, “Electric Vehicle Battery Secondary Use Under Government Subsidy: A Closed-Loop Supply Chain Perspective
,” Int. J. Prod. Econ.
, 234
, pp. 108035
–108049
.2.
Alfaro-Algaba
, M.
, and Ramirez
, F. J.
, 2020
, “Techno-economic and Environmental Disassembly Planning of Lithium-Ion Electric Vehicle Battery Packs for Remanufacturing
,” Resour. Conserv. Recycl.
, 154
, pp. 104461
–104475
. 3.
Fan
, E.
, Li
, L.
, Wang
, Z.
, Lin
, J.
, Huang
, Y.
, Yao
, Y.
, Chen
, R.
, and Wu
, F.
, 2020
, “Sustainable Recycling Technology for Li-Ion Batteries and Beyond: Challenges and Future Prospects
,” Chem. Rev.
, 120
(14
), pp. 7020
–7063
. 4.
van Velzen
, A.
, Annema
, J. A.
, van de Kaa
, G.
, and van Wee
, B.
, 2019
, “Proposing a More Comprehensive Future Total Cost of Ownership Estimation Framework for Electric Vehicles
,” Energy Policy
, 129
, pp. 1034
–1046
. 5.
Ortegon
, K.
, Nies
, L. F.
, and Sutherland
, J. W.
, 2014
, “The Impact of Maintenance and Technology Change on Remanufacturing as a Recovery Alternative for Used Wind Turbines
,” Procedia CIRP
, 15
, pp. 182
–188
. 6.
Xu
, X.
, Hu
, W.
, Liu
, W.
, Wang
, D.
, Huang
, Q.
, and Chen
, Z.
, 2021
, “Study on the Economic Benefits of Retired Electric Vehicle Batteries Participating in the Electricity Markets
,” J. Cleaner Prod.
, 286
(1
), pp. 125414
–125452
.7.
Tang
, Y.
, Zhou
, M. C.
, Zussman
, E.
, and Caudill
, R.
, 2002
, “Disassembly Modeling, Planning, and Application
,” J. Manuf. Syst.
, 21
(3
), pp. 200
–217
. 8.
Tian
, G.
, Zhou
, M. C.
, and Jiangwei
, C.
, 2013
, “A Chance Constrained Programming Approach to Determine the Optimal Disassembly Sequence
,” IEEE Trans. Autom. Sci. Eng.
, 10
(4
), pp. 1004
–1013
. 9.
Marconi
, M.
, Palmieri
, G.
, Callegari
, M.
, and Germani
, M.
, 2019
, “Feasibility Study and Design of an Automatic System for Electronic Components Disassembly
,” ASME J. Manuf. Sci. Eng.
, 141
(2
), p. 021011
. 10.
Hu
, Y.
, and Ameta
, G.
, 2019
, “A Charts-Based Approach to Estimate Disassembly Time: Hypothesis, Model and Validation
,” ASME J. Manuf. Sci. Eng.
, 141
(2
), p. 021009
. 11.
Wang
, Y.
, Mendis
, G.
, Peng
, S.
, and Sutherland
, J.
, 2019
, “Component-Oriented Reassembly in Remanufacturing Systems: Managing Uncertainty and Satisfying Customer Needs
,” ASME J. Manuf. Sci. Eng.
, 141
(2
), p. 021005
. 12.
Ilgin
, M. A.
, 2019
, “A DEMATEL-Based Disassembly Line Balancing Heuristic
,” ASME J. Manuf. Sci. Eng.
, 141
(2
), p. 021002
. 13.
Sutherland
, J. W.
, Skerlos
, S. J.
, Haapala
, K. R.
, Cooper
, D.
, Zhao
, F.
, and Huang
, A.
, 2020
, “Industrial Sustainability: Reviewing the Past and Envisioning the Future
,” ASME J. Manuf. Sci. Eng.
142
(11
), p. 110806
. 14.
Lambert
, A. J. D.
, and Gupta
, S. M.
, 2008
, “Methods for Optimum and Near Optimum Disassembly Sequencing
,” Int. J. Prod. Res.
, 46
(11
), pp. 2845
–2865
. 15.
Liao
, H.
, Chen
, Y.
, Hu
, B.
, and Behdad
, S.
, 2023
, “Optimization-Based Disassembly Sequence Planning Under Uncertainty for Human–Robot Collaboration
,” ASME J. Mech. Des.
, 145
(2
), p. 022001
. 16.
Lee
, M.-L.
, Behdad
, S.
, Liang
, X.
, and Zheng
, M.
, 2022
, “Task Allocation and Planning for Product Disassembly With Human-Robot Collaboration
,” Rob. Comput. Integr. Manuf.
, 76
(2
), p. 102306
. 17.
Laili
, Y.
, Li
, Y.
, Fang
, Y.
, Pham
, D. T.
, and Zhang
, L.
, 2020
, “Model Review and Algorithm Comparison on Multi-objective Disassembly Line Balancing
,” J. Manuf. Syst.
, 56
(1
), pp. 484
–500
. 18.
Rickli
, J.
, and Camelio
, J. A.
, 2013
, “Multi-Objective Partial Disassembly Optimization Based on Sequence Feasibility
,” J. Manuf. Syst.
, 2013
(1
), pp. 281
–293
. 19.
Wang
, Y.
, Huang
, A.
, Quigley
, C. A.
, Li
, L.
, and Sutherland
, J. W.
, 2020
, “Tolerance Allocation: Balancing Quality, Cost, and Waste Through Production Rate Optimization
,” J. Cleaner Prod.
, 2020
(285
), p. 124837
.20.
Ren
, Y.
, Jin
, H.
, Zhao
, F.
, Qu
, T.
, Meng
, L.
, Zhang
, C.
, Zhang
, B.
, Wang
, G.
, and Sutherland
, J. W.
, 2020
, “A Multiobjective Disassembly Planning for Value Recovery and Energy Conservation From End-of-Life Products
,” IEEE Trans. Autom. Sci. Eng.
, 99
(2
), pp. 1
–13
.21.
Nelson
, P.
, Santini
, D.
, and Barnes
, J.
, 2009
, “Factors Determining the Manufacturing Costs of Lithium-Ion Batteries for PHEVs
,” World Electr. Veh. J.
, 3
(3
), pp. 457
–468
. 22.
Hannan
, M. A.
, Hoque
, M. M.
, Hussain
, A.
, Yusof
, Y.
, and Ker
, P. J.
, 2018
, “State-of-the-Art and Energy Management System of Lithium-Ion Batteries in Electric Vehicle Applications: Issues and Recommendations
,” IEEE Access
, 6
, pp. 19362
–19378
. 23.
Heymans
, C.
, Walker
, S. B.
, Young
, S. B.
, and Fowler
, M.
, 2014
, “Economic Analysis of Second Use Electric Vehicle Batteries for Residential Energy Storage and Load-Levelling
,” Energy Policy
, 71
, pp. 22
–30
. 24.
Bobba
, S.
, Podias
, A.
, Di Persio
, F.
, Messagie
, M.
, Tecchio
, P.
, Cusenza
, M. A.
, Eynard
, U.
, Mathieux
, F.
, and Pfrang
, A.
, 2018
, “Sustainability Assessment of Second Life Application of Automotive Batteries (SASLAB) JRC Exploratory Research (2016-2017): Final Technical Report, August 2018
,”Luxembourg: Publications Office
, Luxembourg
.25.
Zhou
, X.-Y.
, Zou
, Y.-L.
, Zhao
, G.-J.
, and Yang
, J.
, 2013
, “Cycle Life Prediction and Match Detection in Retired Electric Vehicle Batteries
,” Trans. Nonferrous Met. Soc. China
, 23
(10
), pp. 3040
–3045
. 26.
Lai
, X.
, Qiao
, D.
, Zheng
, Y.
, and Yi
, W.
, 2018
, “A Novel Screening Method Based on a Partially Discharging Curve Using a Genetic Algorithm and Back-Propagation Model for the Cascade Utilization of Retired Lithium-Ion Batteries
,” Electronics
, 7
(12
), pp. 399
–413
.27.
Zhou
, Z.
, Liu
, J.
, Pham
, D. T.
, Xu
, W.
, Ramirez
, F. J.
, Ji
, C.
, and Liu
, Q.
, 2018
, “Disassembly Sequence Planning: Recent Developments and Future Trends
,” Proc. Inst. Mech. Eng. B
, 233
(5
), pp. 1450
–1471
. 28.
Behdad
, S.
, Kwak
, M.
, Kim
, H.
, and Thurston
, D.
, 2010
, “Simultaneous Selective Disassembly and End-of-Life Decision Making for Multiple Products That Share Disassembly Operations
,” ASME J. Mech. Des.
, 132
(4
), p. 041002
. 29.
Wang
, K.
, Li
, X.
, Gao
, L.
, Li
, P.
, and Sutherland
, J. W.
, 2022
, “A Discrete Artificial Bee Colony Algorithm for Multiobjective Disassembly Line Balancing of End-of-Life Products
,” IEEE Trans. Cybern.
, 52
(8
), pp. 7415
–7426
. 30.
McGovern
, S. M.
, and Gupta
, S. M.
, 2007
, “A Balancing Method and Genetic Algorithm for Disassembly Line Balancing
,” Eur. J. Oper. Res.
, 179
(3
), pp. 692
–708
. 31.
Liu
, J.
, and Yong
, W.
, 2007
, “Fuzzy Analytical Hierarchy Process-Based Assembly Unit Partition for Complex Products
,” Proceedings of the ASME 2007 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference
, Las Vegas, NV
, Sept. 4–7
, pp. 121
–128
.32.
Ren
, Y.
, Zhang
, C.
, Zhao
, F.
, Tian
, G.
, Lin
, W.
, Meng
, L.
, and Li
, H.
, 2018
, “Disassembly Line Balancing Problem Using Interdependent Weights-Based Multi-criteria Decision Making and 2-Optimal Algorithm
,” J. Cleaner Prod.
, 174
(10
), pp. 1475
–1486
. 33.
Hui
, W.
, Dong
, X.
, and Guanghong
, D.
, 2008
, “A Genetic Algorithm for Product Disassembly Sequence Planning
,” Neurocomputing
, 71
(13–15
), pp. 2720
–2726
. 34.
Shan
, H.
, Li
, S.
, Huang
, J.
, Gao
, Z.
, and Li
, W.
, “Ant Colony Optimization Algorithm-Based Disassembly Sequence Planning
,” Proceedings of the 2007 International Conference on Mechatronics and Automation
, Harbin, China
, Aug. 5–8
, pp. 867
–872
.35.
Azab
, A.
, Ziout
, A.
, and Elmaraghy
, W.
, 2011
, “Modeling and Optimization for Disassembly Planning
,” Jordan J. Mech. Ind. Eng.
, 5
, pp. 1
–8
.36.
Alshibli
, M.
, El Sayed
, A.
, Kongar
, E.
, Sobh
, T. M.
, and Gupta
, S. M.
, 2016
, “Disassembly Sequencing Using Tabu Search
,” J. Intell. Rob. Syst.
, 82
(1
), pp. 69
–79
. 37.
Kalayci
, C. B.
, and Gupta
, S. M.
, 2013
, “Artificial Bee Colony Algorithm for Solving Sequence-Dependent Disassembly Line Balancing Problem
,” Expert Syst. Appl.
, 40
(18
), pp. 7231
–7241
. 38.
Tseng
, Y.-J.
, Yu
, F.-Y.
, and Huang
, F.-Y. I.
, 2011
, “A Green Assembly Sequence Planning Model With a Closed-Loop Assembly and Disassembly Sequence Planning Using a Particle Swarm Optimization Method
,” Int. J. Adv. Manuf. Technol.
, 57
(9–12
), pp. 1183
–1197
. 39.
Huang
, J.
, Esmaeilian
, B.
, and Behdad
, S.
, 2015
, “Multi-Purpose Disassembly Sequence Planning
,” the ASME International Design Engineering Technical Conferences & Computers & Information in Engineering Conference
, Boston, MA
, Aug. 2–5
, pp. 1
–9
.40.
Zhang
, L.
, Zhao
, X.
, Ke
, Q.
, Dong
, W.
, and Zhong
, Y.
, 2021
, “Disassembly Line Balancing Optimization Method for High Efficiency and Low Carbon Emission
,” Int. J. Precis. Eng. Manuf. - Green Technol.
, 8
(1
), pp. 233
–247
. 41.
Gen
, M.
, and Cheng
, R.
, 2005
, Foundations of Genetic Algorithms
, Springer
, Berlin/Heidelberg
.42.
Hwang
, R.
, and Katayama
, H.
, 2009
, “Uniform Workload Assignments for Assembly Line by GA-Based Amelioration Approach
,” Int. J. Prod. Res.
, 48
(7
), pp. 1857
–1871
. 43.
Tan
, Y.
, 2015
, Fireworks Algorithm: A Novel Swarm Intelligence Optimization Method
, Springer Publishing Company, Incorporated
, Berlin/Heidelberg
.44.
Zheng
, Y.-J.
, Song
, Q.
, and Chen
, S.-Y.
, 2013
, “Multiobjective Fireworks Optimization for Variable-Rate Fertilization in Oil Crop Production
,” Appl. Soft Comput.
, 13
(11
), pp. 4253
–4263
. 45.
Zaenudin
, E.
, and Kistijantoro
, A. I.
, 2016
, “pSPEA2: Optimization Fitness and Distance Calculations for Improving Strength Pareto Evolutionary Algorithm 2 (SPEA2)
,” 2016 International Conference on Information Technology Systems and Innovation (ICITSI)
, Bandung, Indonesia
, Oct. 24–27
.46.
Xu
, Y.
, Yao
, B.
, and Pham
, D. T.
, 2020
, “Research on Intelligent Optimization Algorithm for Multi-Objective Disassembly Line Balancing Problem
,” Proceedings of the ASME 2020 15th International Manufacturing Science and Engineering Conference
, Online
, Sept. 3
, p. V002T07A02
.47.
McDougall
, R.
, and Nokleby
, S.
, 2009
, “On the Application of Multi-Objective Parallel Asynchronous Particle Swarm Optimization to Engineering Design Problems
,” The ASME 2009 International Design Engineering Technical Conferences & Computers and Information in Engineering Conference
, San Diego, CA
, Aug. 30–Sept. 2
.48.
Li
, M.
, Liu
, X.
, and Wang
, K.
, 2013
, “IPESA-II: Improved Pareto Envelope-Based Selection Algorithm II
,” Proceedings of the 7th International Conference on Evolutionary Multi-criterion Optimization
, Sheffield, UK
, Mar. 19–22
, pp. 143
–155
.49.
Li
, K.
, Wang
, R.
, Zhang
, T.
, and Ishibuchi
, H.
, 2018
, “Evolutionary Many-Objective Optimization: A Comparative Study of the State-of-the-Art
,” IEEE Access
, 6
, pp. 26194
–26214
.Copyright © 2023 by ASME
You do not currently have access to this content.
