Optimization of remanufacturing process routes oriented toward eco-efficiency

Hong PENG , Han WANG , Daojia CHEN

Front. Mech. Eng. ›› 2019, Vol. 14 ›› Issue (4) : 422 -433.

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Front. Mech. Eng. ›› 2019, Vol. 14 ›› Issue (4) : 422 -433. DOI: 10.1007/s11465-019-0552-z
RESEARCH ARTICLE
RESEARCH ARTICLE

Optimization of remanufacturing process routes oriented toward eco-efficiency

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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

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Hong PENG, Han WANG, Daojia CHEN. Optimization of remanufacturing process routes oriented toward eco-efficiency. Front. Mech. Eng., 2019, 14(4): 422-433 DOI:10.1007/s11465-019-0552-z

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References

[1]

Xu B S. Innovation and development of remanufacturing with Chinese characteristics for a new era. China Surface Engineering, 2018, 31(1): 1–6 (in Chinese)

[2]

Xu B S, Li E Z, Zheng H D, . The remanufacturing industry and its development strategy in China. Engineering and Science, 2017, 19(3): 61–65 (in Chinese)

[3]

Liao H, Shi Y, Liu X, . A non-probabilistic model of carbon footprints in remanufacture under multiple uncertainties. Journal of Cleaner Production, 2019, 211: 1127–1140

[4]

Shen N, Liao H, Deng R, . 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

[5]

Liao H, Deng Q. A carbon-constrained EOQ model with uncertain demand for remanufactured products. Journal of Cleaner Production, 2018, 199, 334–347

[6]

Behret H, Korugan A. Performance analysis of a hybrid system under quality impact of returns. Computers & Industrial Engineering, 2009, 56(2): 507–520

[7]

Quariguasi-Frota-Neto J, Bloemhof J. An analysis of the eco-efficiency of remanufactured personal computers and mobile phones. Production and Operations Management, 2012, 21(1): 101–114

[8]

Li C B, Feng Y, Du Y B, . Decision-making method for used components remanufacturing process plan based on modified FNN. Computer Integrated Manufacturing Systems, 2016, 22(3): 729–737 (in Chinese)

[9]

Golinska-Dawson P, Kosacka M, Mierzwiak R, 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]

Subramoniam R, Huisingh D, Chinnam R B, . Remanufacturing decision-making framework (RDMF): Research validation using the analytical hierarchical process. Journal of Cleaner Production, 2013, 40: 212–220

[11]

Wang H, Jiang Z G, Zhang X G, . A fault feature characterization based method for remanufacturing process planning optimization. Journal of Cleaner Production, 2017, 161: 708–719

[12]

Jiang Z G, Jiang Y, Wang Y, . A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. Journal of Intelligent Manufacturing, 2019, 30(1): 19–32

[13]

Yazdi M, Nikfar F, Nasrabadi M. Failure probability analysis by employing fuzzy fault tree analysis. International Journal of System Assurance Engineering and Management, 2017, 8(Suppl 2): 1177–1193

[14]

Ang C L, Luo M, Khoo L P, . A knowledge-based approach to the generation of IDEF0 models. International Journal of Production Research, 1997, 35(5): 1385–1412

[15]

Jiang Z, Zhou T, Zhang H Y, . Reliability and cost optimization for remanufacturing process planning. Journal of Cleaner Production, 2016, 135(4): 1602–1610

[16]

Zhang X G, Zhang H, Jiang Z G, . An integrated model for remanufacturing process route decision. International Journal of Computer Integrated Manufacturing, 2015, 28(5): 451–459

[17]

Schaltegger U, Krähenbühl U. Heavy rare-earth element enrichment in granites of the Aar Massif (Central Alps, Switzerland). Chemical Geology, 1990, 89(1–2): 49–63

[18]

Schmidheiny S. Changing Course: A Global Business Perspective on Development and the Environment. Cambridge: MIT Press, 1992

[19]

Huisman J, Stevels A L N, Stobbe I. 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]

Kicherer A, Schaltegger S, Tschochohei H, . Eco-efficiency. The International Journal of Life Cycle Assessment, 2007, 12(7): 537–543

[21]

Derwall J, Guenster N, Bauer R, . The eco-efficiency premium puzzle. Financial Analysts Journal, 2005, 61(2): 51–63

[22]

Kerr W, Ryan C. Eco-efficiency gains from remanufacturing: A case study of photocopier remanufacturing at Fuji Xerox Australia. Journal of Cleaner Production, 2001, 9(1): 75–81

[23]

Bonyadi M R, Michalewicz Z. Particle swarm optimization for single objective continuous space problems: A review. Evolutionary Computation, 2017, 25(1): 1–54

[24]

Gong Y J, Li J J, Zhou Y, . Genetic learning particle swarm optimization. IEEE Transactions on Cybernetics, 2016, 46(10): 2277–2290

[25]

Li L, Cheng F X, Cheng X Q, . 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)

[26]

Chen Y J, Liu D B. 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]

Chatterjee S, Sarkar S, Hore S, . Particle swarm optimization trained neural network for structural failure prediction of multistoried RC buildings. Neural Computing & Applications, 2017, 28(8): 2005–2016

[28]

Jiang P, Ge Y, Wang C. 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

[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

[30]

Wang H, Jiang Z G, Zhang H, An integrated MCDM approach considering demands-matching for reverse logistics. Journal of Cleaner Production, 2019, 208: 199–210

[31]

Liao H, Deng Q, Wang Y, . An environmental benefits and costs assessment model for remanufacturing process under quality uncertainty. Journal of Cleaner Production, 2018, 178: 45–58

[32]

Liao H, Deng Q, Wang Y. 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]

Yu S, Wei Y M, Guo H, . 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

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