Optimization of remanufacturing process routes oriented toward eco-efficiency

doi:10.1007/s11465-019-0552-z

Front. Mech. Eng.

2019, Vol. 14

Issue (4) : 422-433 https://doi.org/10.1007/s11465-019-0552-z

RESEARCH ARTICLE

Optimization of remanufacturing process routes oriented toward eco-efficiency

Hong PENG(

), Han WANG, Daojia CHEN

Key Laboratory of Metallurgical Equipment and Control Technology, Wuhan University of Science and Technology, Wuhan 430081, China; Hubei Key Laboratory of Mechanical Transmission and Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China; Academy of Green Manufacturing Engineering, Wuhan University of Science and Technology, Wuhan 430081, China

Download: PDF(1311 KB) HTML
Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks

Abstract

Remanufacturing route optimization is crucial in remanufacturing production because it exerts a considerable impact on the eco-efficiency (i.e., the best link between economic and environmental benefits) of remanufacturing. Therefore, an optimization model for remanufacturing process routes oriented toward eco-efficiency is proposed. In this model, fault tree analysis is used to extract the characteristic factors of used products. The ICAM definition method is utilized to design alternative remanufacturing process routes for the used products. Afterward, an eco-efficiency objective function model is established, and simulated annealing (SA) particle swarm optimization (PSO) is applied to select the manufacturing process route with the best eco-efficiency. The proposed model is then applied to the remanufacturing of a used helical cylindrical gear, and optimization of the remanufacturing process route is realized by MATLAB programming. The proposed model’s feasibility is verified by comparing the model’s performance with that of standard SA and PSO.

Keywords remanufacturing process route optimization eco-efficiency simulated particle swarm optimization algorithm IDEF0

Corresponding Author(s): Hong PENG

Just Accepted Date: 11 September 2019 Online First Date: 17 October 2019 Issue Date: 02 December 2019

Cite this article:

Hong PENG,Han WANG,Daojia CHEN. Optimization of remanufacturing process routes oriented toward eco-efficiency[J]. Front. Mech. Eng., 2019, 14(4): 422-433.

URL:

https://academic.hep.com.cn/fme/EN/10.1007/s11465-019-0552-z
https://academic.hep.com.cn/fme/EN/Y2019/V14/I4/422

Fig.1 Remanufacturing process route optimization flowchart.

Fig.2 Characteristic factors of used products.

Fig.3 IDEF0 for the design of remanufacturing process routes.

Fig.4 Implementation of SAPSO.

Fig.5 Flowchart of remanufacturing process route optimization based on SAPSO.

Fig.6 Characteristic factor analysis of the used helical cylindrical gear. (F1, M1, C1, P1), (F2, M2, C2, P2), and (F3, M3, C3, P3) denote (moderate wear, cast iron, 46HRC,

I T 7

), (moderate crack, cast iron, 50HRC,

I T 5

), and (mild crack, cast iron, 46HRC,

I T 7

), respectively.

Fig.7 IDEF0 for remanufacturing process route design of the used helical cylindrical gear.

Tab.1 Optional process route information of the used helical cylindrical gear

Fig.8 Used cylindrical helical gear (a) before and (b) after remanufacturing.

Tab.2 Optimization results of algorithms

Fig.9 Convergence curve of (a) SA, (b) PSO, and (c) SAPSO.

Table A1 Mechanical equipment information for the remanufacturing process

Tool number	Tool type	Tool specification	Cost/(CNY?h^–1)
T-001	Offset tool	α=90°	3.2
T-002	Grooving tool	$d = 12$ mm	4.5
T-003	Cornish bit	$d = 110$ mm	4.2
T-004	Screw cutting tool	α=45°	5.5

Table A2 Tool information for the remanufacturing process

1	B S Xu. Innovation and development of remanufacturing with Chinese characteristics for a new era. China Surface Engineering, 2018, 31(1): 1–6 (in Chinese) https://doi.org/10.11933/j.issn.1007-9289.20180131002
2	B S Xu, E Z Li, H D Zheng, et al.. The remanufacturing industry and its development strategy in China. Engineering and Science, 2017, 19(3): 61–65 (in Chinese) https://doi.org/10.15302/J-SSCAE-2017.03.009
3	H Liao, Y Shi, X Liu, et al.. A non-probabilistic model of carbon footprints in remanufacture under multiple uncertainties. Journal of Cleaner Production, 2019, 211: 1127–1140 https://doi.org/10.1016/j.jclepro.2018.11.218
4	N Shen, H Liao, R Deng, et al.. Different types of environmental regulations and the heterogeneous influence on the environmental total factor productivity: Empirical analysis of China’s industry. Journal of Cleaner Production, 2019, 211: 171–184 https://doi.org/10.1016/j.jclepro.2018.11.170
5	H Liao, Q Deng. A carbon-constrained EOQ model with uncertain demand for remanufactured products. Journal of Cleaner Production, 2018, 199, 334–347 https://doi.org/10.1016/j.jclepro.2018.07.108
6	H Behret, A Korugan. Performance analysis of a hybrid system under quality impact of returns. Computers & Industrial Engineering, 2009, 56(2): 507–520 https://doi.org/10.1016/j.cie.2007.11.001
7	J Quariguasi-Frota-Neto, J Bloemhof. An analysis of the eco-efficiency of remanufactured personal computers and mobile phones. Production and Operations Management, 2012, 21(1): 101–114 https://doi.org/10.1111/j.1937-5956.2011.01234.x
8	C B Li, Y Feng, Y B Du, et al.. Decision-making method for used components remanufacturing process plan based on modified FNN. Computer Integrated Manufacturing Systems, 2016, 22(3): 729–737 (in Chinese) https://doi.org/10.13196/j.cims.2016.03.016
9	P Golinska-Dawson, M Kosacka, R Mierzwiak, et al.et al.The mixed method for sustainability assessment of remanufacturing process using grey decision making. In: Golinska-Dawson P, Kübler F, eds. Sustainability in Remanufacturing Operations. EcoProduction (Environmental Issues in Logistics and Manufacturing). Cham: Springer, 2018, 125–139 doi:10.1007/978-3-319-60355-1_9
10	R Subramoniam, D Huisingh, R B Chinnam, et al.. Remanufacturing decision-making framework (RDMF): Research validation using the analytical hierarchical process. Journal of Cleaner Production, 2013, 40: 212–220 https://doi.org/10.1016/j.jclepro.2011.09.004
11	H Wang, Z G Jiang, X G Zhang, et al.. A fault feature characterization based method for remanufacturing process planning optimization. Journal of Cleaner Production, 2017, 161: 708–719 https://doi.org/10.1016/j.jclepro.2017.05.178
12	Z G Jiang, Y Jiang, Y Wang, et al.. A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. Journal of Intelligent Manufacturing, 2019, 30(1): 19–32 https://doi.org/10.1007/s10845-016-1231-0
13	M Yazdi, F Nikfar, M Nasrabadi. Failure probability analysis by employing fuzzy fault tree analysis. International Journal of System Assurance Engineering and Management, 2017, 8(Suppl 2): 1177–1193 https://doi.org/10.1007/s13198-017-0583-y
14	C L Ang, M Luo, L P Khoo, et al.. A knowledge-based approach to the generation of IDEF0 models. International Journal of Production Research, 1997, 35(5): 1385–1412 https://doi.org/10.1080/002075497195380
15	Z Jiang, T Zhou, H Y Zhang, et al.. Reliability and cost optimization for remanufacturing process planning. Journal of Cleaner Production, 2016, 135(4): 1602–1610 https://doi.org/10.1016/j.jclepro.2015.11.037
16	X G Zhang, H Zhang, Z G Jiang, et al.. An integrated model for remanufacturing process route decision. International Journal of Computer Integrated Manufacturing, 2015, 28(5): 451–459 https://doi.org/10.1080/0951192X.2014.880804
17	U Schaltegger, U Krähenbühl. Heavy rare-earth element enrichment in granites of the Aar Massif (Central Alps, Switzerland). Chemical Geology, 1990, 89(1–2): 49–63 https://doi.org/10.1016/0009-2541(90)90059-G
18	S Schmidheiny. Changing Course: A Global Business Perspective on Development and the Environment. Cambridge: MIT Press, 1992
19	J Huisman, A L N Stevels, I Stobbe. Eco-efficiency considerations on the end-of-life of consumer electronic products. IEEE International Symposium on Electronics and the Environment, 2009, 27(1): 9–25 doi:10.1109/TEPM.2004.832214
20	A Kicherer, S Schaltegger, H Tschochohei, et al.. Eco-efficiency. The International Journal of Life Cycle Assessment, 2007, 12(7): 537–543 https://doi.org/10.1065/lca2007.01.305
21	J Derwall, N Guenster, R Bauer, et al.. The eco-efficiency premium puzzle. Financial Analysts Journal, 2005, 61(2): 51–63 https://doi.org/10.2469/faj.v61.n2.2716
22	W Kerr, C Ryan. Eco-efficiency gains from remanufacturing: A case study of photocopier remanufacturing at Fuji Xerox Australia. Journal of Cleaner Production, 2001, 9(1): 75–81 https://doi.org/10.1016/S0959-6526(00)00032-9
23	M R Bonyadi, Z Michalewicz. Particle swarm optimization for single objective continuous space problems: A review. Evolutionary Computation, 2017, 25(1): 1–54 https://doi.org/10.1162/EVCO_r_00180
24	Y J Gong, J J Li, Y Zhou, et al.. Genetic learning particle swarm optimization. IEEE Transactions on Cybernetics, 2016, 46(10): 2277–2290 https://doi.org/10.1109/TCYB.2015.2475174
25	L Li, F X Cheng, X Q Cheng, et al.. Enterprise remanufacturing logistics network optimization based on modified multi-objective particle swarm optimization algorithm. Computer Integrated Manufacturing Systems, 2018, 24(8): 240–250 (in Chinese) https://doi.org/CNKI:SUN:JSJJ.0.2018-08-026
26	Y J Chen, D B Liu. An uncertain programming model for manufacturing/remanufacturing hybrid system in reverse logistics environment. Applied Mechanics and Materials, 2013, 288: 251–255 doi:10.4028/www.scientific.net/amm.288.251
27	S Chatterjee, S Sarkar, S Hore, et al.. Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Computing & Applications, 2017, 28(8): 2005–2016 https://doi.org/10.1007/s00521-016-2190-2
28	P Jiang, Y Ge, C Wang. Research and application of a hybrid forecasting model based on simulated annealing algorithm: A case study of wind speed forecasting. Journal of Renewable and Sustainable Energy, 2016, 8: 015501 https://doi.org/10.1063/1.4940408
29	World Business Council for Sustainable Development. The business case for sustainable development: Making a difference towards the Earth Summit 2002 and Beyond. Corporate Environmental Strategy, 2002, 9(3): 226–235 https://doi.org/10.1016/s1066-7938(02)00071-4
30	H Wang, Z G Jiang, H Zhang, et al.et al.An integrated MCDM approach considering demands-matching for reverse logistics. Journal of Cleaner Production, 2019, 208: 199–210 https://doi.org/10.1016/j.jclepro.2018.10.131
31	H Liao, Q Deng, Y Wang, et al.. An environmental benefits and costs assessment model for remanufacturing process under quality uncertainty. Journal of Cleaner Production, 2018, 178: 45–58 https://doi.org/10.1016/j.jclepro.2017.12.256
32	H Liao, Q Deng, Y Wang. Optimal acquisition and production policy for end-of-life engineering machinery recovering in a joint manufacturing/remanufacturing system under uncertainties in procurement and demand. Sustainability, 2017, 9(3): 338 doi:10.3390/su9030338
33	S Yu, Y M Wei, H Guo, et al.. Carbon emission coefficient measurement of the coal-to-power energy chain in China. Applied Energy, 2014, 114(2): 290–300 doi:10.1016/j.apenergy.2013.09.062

[1]	Jiali ZHAO, Shitong PENG, Tao LI, Shengping LV, Mengyun LI, Hongchao ZHANG. Energy-aware fuzzy job-shop scheduling for engine remanufacturing at the multi-machine level[J]. Front. Mech. Eng., 2019, 14(4): 474-488.
[2]	Le CHEN, Xianlin WANG, Hua ZHANG, Xugang ZHANG, Binbin DAN. Timing decision-making method of engine blades for predecisional remanufacturing based on reliability analysis[J]. Front. Mech. Eng., 2019, 14(4): 412-421.
[3]	Haiyang LU, Yanle LI, Fangyi LI, Xingyi ZHANG, Chuanwei ZHANG, Jiyu DU, Zhen LI, Xueju RAN, Jianfeng LI, Weiqiang WANG. Damage mechanism and evaluation model of compressor impeller remanufacturing blanks: A review[J]. Front. Mech. Eng., 2019, 14(4): 402-411.
[4]	Yue SHI,Lihong DONG,Haidou WANG,Guolu LI,Shenshui LIU. Fatigue features study on the crankshaft material of 42CrMo steel using acoustic emission[J]. Front. Mech. Eng., 2016, 11(3): 233-241.
[5]	He LIU, Guochen SHI, Cui WANG, Peijing SHI, Yi XU, . Artificial lift equipment repairing techniques in Daqing Oilfield[J]. Front. Mech. Eng., 2010, 5(1): 111-117.

Viewed

Full text

Abstract

Cited

Shared

Discussed