A review on remanufacturing assembly management and technology

  • Conghu Liu
  • Qinghua ZhuEmail author
  • Fangfang Wei
  • Weizhen Rao
  • JunJun Liu
  • Jing Hu
  • Wei Cai


Remanufacturing has been regarded as a key technology for the sustainable development of the world economy. However, the quality of remanufactured products is difficult to meet the needs of customers, which has become a bottleneck restricting the development of remanufacturing industry. Therefore, remanufacturing assembly management and technology has more important engineering significance to improve production efficiency and quality of remanufactured products. Aiming at the lag of the research on management and control of remanufacturing assembly, this paper summarizes the research status of remanufacturing assembly on the basis of studying the characteristics and connotation of remanufacturing assembly system. Intelligent robot of remanufacturing assembly driven by large data and intelligent manufacturing will become an important development direction in the future. The research direction of remanufacturing assembly is summarized, and the future research trend of remanufacturing assembly is pointed out. It can be predicted that the intelligent robot of remanufacturing assembly will be the key to the flexibility, intellectualization, precision, and integration of remanufacturing assembly system in the future. This study provides a direction for the development of remanufacturing industry.


Remanufacturing Assemble Quality control Uncertainty Intelligent robot 



This research is supported by Humanities and Social Science Research of Ministry of Education (No. 17YJC630082), Key projects funded by the National Natural Science Foundation of China (71632007), Chinese Postdoctoral Science Foundation (No. 2017 M611574), Anhui Science and Technology Innovation Strategy and Soft Science Research Project (No. 201806a02020041), Innovative Research Team in Suzhou University (No.2018kytd01), Innovative Research Team of Anhui Provincial Education Department (2016SCXPTTD), Key Discipline of Material Science and Engineering of Suzhou University (2017XJZDXK3) and Suzhou Mechanical Equipment Co-innovation Engineering Technology Research Center (No. SZ2017ZX07).


  1. 1.
    Zhu Q, Sarkis J, Geng Y (2005) Green supply chain management in China: pressures, practices and performance. Int J Oper Prod Manag 25(5):449–468CrossRefGoogle Scholar
  2. 2.
    Geng Y, Sarkis J, Ulgiati S, Zhang P (2013) Measuring China's circular economy. Science 339(6127):1526–1527CrossRefGoogle Scholar
  3. 3.
    Cai W, Liu C, Zhang C, Ma M, Rao W, Li W, He K, Gao M (2018) Developing the ecological compensation criterion of industrial solid waste based on emergy for sustainable development. Energy 157:940–948CrossRefGoogle Scholar
  4. 4.
    Xu B, Dong S, Shi P (2013) States and prospects of china characterised quality guarantee technology system for remanufactured parts. Journal of Mechanical Engineering 49(20):84–90CrossRefGoogle Scholar
  5. 5.
    Xu BS, Shi PJ, Liu BH, Zhou XY (2012) Engineering management problems of remanufacturing industry. Zhingguo Biaomian Gongcheng (China Surf Eng) 25(6):107–111Google Scholar
  6. 6.
    Liu C, Cai W, Dinolov O, Zhang C, Rao W, Jia S, Li L, Chan FT (2018) Emergy based sustainability evaluation of remanufacturing machining systems. Energy 150:670–680CrossRefGoogle Scholar
  7. 7.
    Liu Z, Li KW, Li BY, Huang J, Tang J (2019) Impact of product-design strategies on the operations of a closed-loop supply chain. Transp Res E Logist Transp Rev 124:75–91CrossRefGoogle Scholar
  8. 8.
    Xu B, Dong S, Zhu S, Shi P (2012) Prospects and developing of remanufacture forming technology. Jixie Gongcheng Xuebao (Chin J Mech Eng) 48(15):96–105CrossRefGoogle Scholar
  9. 9.
    Cai W, Liu C, Lai KH, Li L, Cunha J, Hu L (2019) Energy performance certification in mechanical manufacturing industry: a review and analysis. Energy Convers Manag 186:415–432CrossRefGoogle Scholar
  10. 10.
  11. 11.
    Subramoniam R, Huisingh D, Chinnam RB (2009) Remanufacturing for the automotive aftermarket-strategic factors: literature review and future research needs. J Clean Prod 17(13):1163–1174CrossRefGoogle Scholar
  12. 12.
    Peng S, Li T, Zhao J, Guo Y, Lv S, Tan G, Zhang H (2019) Petri net-based scheduling strategy and energy modeling for the cylinder block remanufacturing under uncertainty. Robot Comput Integr Manuf 58:208–219CrossRefGoogle Scholar
  13. 13.
    Liu C, He K, Zhang Y, Liu C (2018) Research on data mining technology for the connotation and measurement of uncertainty for reassembly dimensions. Int J High Perform Syst Archit 8(1-2):13–21CrossRefGoogle Scholar
  14. 14.
    Liu M, Liu C, Xing L, Mei F, Zhang X (2016) Study on a tolerance grading allocation method under uncertainty and quality oriented for remanufactured parts. Int J Adv Manuf Technol 87(5-8):1265–1272CrossRefGoogle Scholar
  15. 15.
    Steeneck DW, Sarin SC (2018) Product design for leased products under remanufacturing. Int J Prod Econ 202:132–144CrossRefGoogle Scholar
  16. 16.
    Chakraborty K, Mondal S, Mukherjee K (2017) Analysis of product design characteristics for remanufacturing using Fuzzy AHP and Axiomatic Design. J Eng Des 28(5):338–368CrossRefGoogle Scholar
  17. 17.
    Cai W, Li L, Jia S, Liu C, Xie J, Hu L (2019) Task-oriented energy benchmark of machining systems for energy-efficient production. Int J Precis Eng Manuf Green Technol:1–14.
  18. 18.
    Um J, Rauch M, Hascoët JY, Stroud I (2017) STEP-NC compliant process planning of additive manufacturing: remanufacturing. Int J Adv Manuf Technol 88(5-8):1215–1230CrossRefGoogle Scholar
  19. 19.
    Jhavar S, Paul CP, Jain NK (2016) Micro-plasma transferred arc additive manufacturing for die and mold surface remanufacturing. JOM 68(7):1801–1809CrossRefGoogle Scholar
  20. 20.
    Smith GM, Sampath S (2018) Sustainability of metal structures via spray-clad remanufacturing. JOM 70(4):512–520CrossRefGoogle Scholar
  21. 21.
    Junior ML, Godinho Filho M (2017) Master disassembly scheduling in a remanufacturing system with stochastic routings. CEJOR 25(1):123–138zbMATHCrossRefGoogle Scholar
  22. 22.
    Liu J, Zhou Z, Pham DT, Xu W, Ji C, Liu Q (2017) Robotic disassembly sequence planning using enhanced discrete bees algorithm in remanufacturing. Int J Prod Res 17:1–18Google Scholar
  23. 23.
    Zhang H, Li M, Liu W, Short T, Liu L, He Y, Li Y, Liu L, Zhang H, Zhang H (2015) Supercritical carbon dioxide cleaning of metal parts for remanufacturing industry. J Clean Prod 93:339–346CrossRefGoogle Scholar
  24. 24.
    Peng S, Tao L, Tang Z, Shi J, Zhang H (2016) Comparative life cycle assessment of remanufacturing cleaning technologies. J Clean Prod 137:475–489CrossRefGoogle Scholar
  25. 25.
    Lei X, Huajun C, Hailong L, Yubo Z (2017) Study on laser cladding remanufacturing process with fecrnicu alloy powder for thin-wall impeller blade. Int J Adv Manuf Technol 90(5-8):1383–1392CrossRefGoogle Scholar
  26. 26.
    Zhu S, Zhou C (2016) Additive remanufacturing for "made in China 2025". Therm Spray Technol 8(3):1–4Google Scholar
  27. 27.
    Ren JL, Liu HC, Song K (2016) The rise and development of metal magnetic memory testing technology. Nondestructive Testing 38(11):7–15Google Scholar
  28. 28.
    Shen G, Zheng Y, Jiang Z, Tan J (2016) The development status of magnetic barkhausen noise technique. Nondestruct Test 38(7):66–74Google Scholar
  29. 29.
    Alinovi A, Bottani E, Montanari R (2012) Reverse Logistics: a stochastic EOQ-based inventory control model for mixed manufacturing/remanufacturing systems with return policies. Int J Prod Res 50(5):1243–1264CrossRefGoogle Scholar
  30. 30.
    Liao TY (2018) Reverse logistics network design for product recovery and remanufacturing. Appl Math Model 60:145–163MathSciNetCrossRefGoogle Scholar
  31. 31.
    Zahraei SM, Teo CC (2018) Optimizing a recover-and-assemble remanufacturing system with production smoothing. Int J Prod Econ 197:330–341CrossRefGoogle Scholar
  32. 32.
    Liu CH, Li WY, Rao WZ, He K (2018) Study on the failure mechanism of the polymorphic mixture for remanufactured machinery parts. Strength Mater 50(1):151–156CrossRefGoogle Scholar
  33. 33.
    Fang JX, Dong SY, Wang YJ, Xu BS, Zhang ZH, Xia D, He P (2015) The effects of solid-state phase transformation upon stress evolution in laser metal powder deposition. Mater Des 87:807–814CrossRefGoogle Scholar
  34. 34.
    Wei S, Liu Y, Tian H, Tong H, Liu Y, Xu B (2015) Microwave absorption property of plasma spray W-type hexagonal ferrite coating. J Magn Magn Mater 377:419–423CrossRefGoogle Scholar
  35. 35.
    Zhao Y, Sun J, Li J (2015) Research on microstructure properties and wear and corrosion resistance of FeCr repaired coating on KMN steel by laser cladding. Aust J Mech Eng 51:37–43CrossRefGoogle Scholar
  36. 36.
    Maropoulos PG, Muelaner JE, Summers MD, Martin OC (2014) A new paradigm in large-scale assembly—research priorities in measurement assisted assembly. Int J Adv Manuf Technol 70(1-4):621–633CrossRefGoogle Scholar
  37. 37.
    Deng Z, Li S, Huang X (2018) Uncertainties evaluation of coordinate transformation parameters in the large-scale measurement for aircraft assembly. Sens Rev 38(4):542–550CrossRefGoogle Scholar
  38. 38.
    Muelaner JE, Wang Z, Keogh PS, Brownell J, Fisher D (2016) Uncertainty of measurement for large product verification: evaluation of large aero gas turbine engine datums. Meas Sci Technol 27(11):115003CrossRefGoogle Scholar
  39. 39.
    Vafadarnikjoo A, Mishra N, Govindan K, Chalvatzis K (2018) Assessment of consumers' motivations to purchase a remanufactured product by applying Fuzzy Delphi method and single valued neutrosophic sets. J Clean Prod 196:230–244CrossRefGoogle Scholar
  40. 40.
    João N, Frota Q, Bloemhof J, Corbett C (2016) Market prices of remanufactured, used and new items: evidence from eBay. Int J Prod Econ 171:371–380CrossRefGoogle Scholar
  41. 41.
    Li G, Reimann M, Zhang W (2018) When remanufacturing meets product quality improvement: the impact of production cost. Eur J Oper Res 271(3):913–925MathSciNetzbMATHCrossRefGoogle Scholar
  42. 42.
    Jochen G (2015) A note on a model to evaluate acquisition price and quantity of used products for remanufacturing. Int J Prod Econ 169:277–284CrossRefGoogle Scholar
  43. 43.
    Guide R, Jayaraman V, Linton JD (2003) Building contingency planning for closed-loop supply shains with product recovery. J Oper Manag 21(3):259–279CrossRefGoogle Scholar
  44. 44.
    Mukhopadhya SK, Ma H (2009) Joint procurement and production decisions in remanufacturing under quality and demand uncertainty. Int J Prod Econ 120(1):5–17CrossRefGoogle Scholar
  45. 45.
    Liu C (2016) Tolerance redistributing of the reassembly dimensional chain on measure of uncertainty. Entropy 18(10):348CrossRefGoogle Scholar
  46. 46.
    Ge M, Liu C, Liu M (2014) The online quality control methods for the assembling of remanufactured engines’ cylinder block and cover under uncertainty. Int J Adv Manuf Technol 74(1-4):225–233CrossRefGoogle Scholar
  47. 47.
    Huang M, Yi P, Shi T, Guo L (2018) A modal interval based method for dynamic decision model considering uncertain quality of used products in remanufacturing. J Intell Manuf 29(4):925–935CrossRefGoogle Scholar
  48. 48.
    Liao H, Deng Q, Wang Y, Guo S, Ren Q (2018) An environmental benefits and costs assessment model for remanufacturing process under quality uncertainty. J Clean Prod 178:45–58CrossRefGoogle Scholar
  49. 49.
    Ge M, Hu J, Liu M, Zhang Y (2018) Reassembly classification selection method based on the Markov Chain. Assem Autom 38(4):476–486CrossRefGoogle Scholar
  50. 50.
    Liu M, Liu C, Xing L, Zhang X, Wang Q, Wang X (2014) Quality oriented assembly grouping optimal allocation method for remanufactured complex mechanical products. Aust J Mech Eng 50:150–155CrossRefGoogle Scholar
  51. 51.
    Liu M, Liu C, Zhu Q (2014) Optional classification for reassembly methods with different precision remanufactured parts. Assem Autom 34(4):315–322CrossRefGoogle Scholar
  52. 52.
    Liu M, Liu C, Ge M, Zhang Y, Liu Z (2016) The online quality control method for reassembly based on state space model. J Clean Prod 137:644–651CrossRefGoogle Scholar
  53. 53.
    Chen X, Zhigang J, Xugang Z, Han W (2017) Multi-objective optimization model and application of components reuse combination for used mechanical equipment. J Mech Eng 53(05):76–85CrossRefGoogle Scholar
  54. 54.
    Jin X, Hu SJ, Ni J, Xiao G (2012) Assembly strategies for remanufacturing systems with variable quality returns. IEEE Trans Autom Sci Eng 10(1):76–85CrossRefGoogle Scholar
  55. 55.
    Jin X, Ni J, Koren Y (2011) Optimal control of reassembly with variable quality returns in a product remanufacturing system. CIRP Ann 60(1):25–28CrossRefGoogle Scholar
  56. 56.
    Su B, Huang XM, Ren YH, WANG F, XIAO H, ZHENG B (2017) Research on selective assembly method optimization for construction machinery remanufacturing based on ant colony algorithm. J Mech Eng 53(5):60–68CrossRefGoogle Scholar
  57. 57.
    Liu C, Li W, Cai W, Han J, He K, Wen H (2018) Control method of suiting operation to different conditions of remanufactured parts for reassembly process. Comput Integr Manuf Syst (in Chines) 24(06):35–44Google Scholar
  58. 58.
    Oh Y, Behdad S (2017) Simultaneous reassembly and procurement planning in assemble-to-order remanufacturing systems. Int J Prod Econ 184:168–178CrossRefGoogle Scholar
  59. 59.
    Cho YH, Doh HH, Lee DH (2018) Mathematical model and solution algorithms for capacitated dynamic lot-sizing in remanufacturing systems. Ind Eng Manag Syst 17(1):1–13Google Scholar
  60. 60.
    Jiang Z, Zhou T, Zhang H, Wang Y, Cao H, Tian G (2016) Reliability and cost optimization for remanufacturing process planning. J Clean Prod 135:1602–1610CrossRefGoogle Scholar
  61. 61.
    Guiras Z, Turki S, Rezg N, Dolgui A (2018) Optimization of two-level disassembly/remanufacturing/assembly system with an integrated maintenance strategy. Appl Sci 8(5):666CrossRefGoogle Scholar
  62. 62.
    Yu JM, Lee DH (2018) Scheduling algorithms for job-shop-type remanufacturing systems with component matching requirement. Comput Ind Eng 120:266–278CrossRefGoogle Scholar
  63. 63.
    Cai X, Lai M, Li X et al (2014) Optimal acquisition and production policy in a hybrid manufacturing/remanufacturing system with core acquisition at different quality levels. Eur J Oper Res 233(2):374–382MathSciNetzbMATHCrossRefGoogle Scholar
  64. 64.
    Wen H, Liu M, Liu C, Liu C (2015) Remanufacturing production planning with compensation function approximation method. Appl Math Comput 256:742–753MathSciNetzbMATHGoogle Scholar
  65. 65.
    Wang L, Xia XH, Xiong YQ (2016) Modular method of remanufacturing service resources. Comput Integr Manuf Syst 22(9):2204–2216Google Scholar
  66. 66.
    Cao X, Zheng B, Wen H (2016) Joint decision of production and pricing for remanufacturing system based on dfd theory. J Ind Eng Eng Manag 30(1):117–123Google Scholar
  67. 67.
    Cui L, Wu KJ, Tseng ML (2017) Selecting a remanufacturing quality strategy based on consumer preferences. J Clean Prod 161:1308–1316CrossRefGoogle Scholar
  68. 68.
    Su C, Shi Y, Dou J (2017) Multi-objective optimization of buffer allocation for remanufacturing system based on TS-NSGAII hybrid algorithm. J Clean Prod 166:756–770CrossRefGoogle Scholar
  69. 69.
    Zhou J, Deng Q, Li T (2018) Optimal acquisition and remanufacturing policies considering the effect of quality uncertainty on carbon emissions. J Clean Prod 186:180–190CrossRefGoogle Scholar
  70. 70.
    Vasanthakumar C, Vinodh S, Ramesh K (2016) Application of interpretive structural modelling for analysis of factors influencing lean remanufacturing practices. Int J Prod Res 54(24):1–14CrossRefGoogle Scholar
  71. 71.
    Kurilova-Palisaitiene J, Sundin E, Poksinska B (2018) Remanufacturing challenges and possible lean improvements. J Clean Prod 172:3225–3236CrossRefGoogle Scholar
  72. 72.
    Jiang Z, Jiang Y, Wang Y, Zhang H, Cao H, Tian G (2019) A hybrid approach of rough set and case-based reasoning to remanufacturing process planning. J Intell Manuf 30(1):19–32CrossRefGoogle Scholar
  73. 73.
    Liu Z, Tang J, Li BY, Wang Z (2017) Trade-off between remanufacturing and recycling of WEEE and the environmental implication under the Chinese Fund Policy. J Clean Prod 167:97–109CrossRefGoogle Scholar
  74. 74.
    Wang H, Jiang Z, Zhang H, Wang Y, Yang Y, Li Y (2019) An integrated MCDM approach considering demands-matching for reverse logistics. J Clean Prod 208:199–210CrossRefGoogle Scholar
  75. 75.
    Zheng XX, Liu Z, Li KW, Huang J, Chen J (2019) Cooperative game approaches to coordinating a three-echelon closed-loop supply chain with fairness concerns. Int J Prod Econ 212:92–110CrossRefGoogle Scholar
  76. 76.
    Ding Z, Jiang Z, Zhang H, Cai W, Liu Y (2018) An integrated decision-making method for selecting machine tool guideways considering remanufacturability. Int J Comput Integr Manuf:1–12.
  77. 77.
    Tian G, Zhang H, Feng Y, Jia H, Zhang C, Jiang Z, Li Z, Li P (2017) Operation patterns analysis of automotive components remanufacturing industry development in China. J Clean Prod 164:1363–1375CrossRefGoogle Scholar
  78. 78.
    Xu B, Xia D, Tan J, Dong S (2018) Status and development of intelligent remanufacturing in China. China Surface Engineering 31(5):1–13Google Scholar
  79. 79.
    Liang X, Liu B, Shi P, Li E, Zhang Z, Xu B (2016) Intelligent remanufacturing engineering system. Sci Technol Rev 34(24):74–79Google Scholar
  80. 80.
    Połap D (2018) Human-machine interaction in intelligent technologies using the augmented reality. Inf Technol Control 47(4):691–703MathSciNetGoogle Scholar
  81. 81.
    Liu M, Ma J, Lin L, Ge M, Wang Q, Liu C (2017) Intelligent assembly system for mechanical products and key technology based on internet of things. J Intell Manuf 28(2):271–299CrossRefGoogle Scholar
  82. 82.
    Shiraishi M, Ashiya H, Konno A, Morita K, Noro T, Nomura Y, Kataoka S (2019) Development of real-time collection, integration, and sharing technology for infrastructure damage Information. Journal of Disaster Research 14(2):333–347CrossRefGoogle Scholar
  83. 83.
    Saez M, Maturana FP, Barton K, Tilbury DM (2018) Real-time manufacturing machine and system performance monitoring using internet of things. IEEE Trans Autom Sci Eng 15(4):1735–1748CrossRefGoogle Scholar
  84. 84.
    Örsdemir A, Kemahlıoğlu-Ziya E, Parlaktürk AK (2014) Competitive quality choice and remanufacturing. Prod Oper Manag 23(1):48–64CrossRefGoogle Scholar
  85. 85.
    Xu BS (2018) Innovation and development of remanufacturing with Chinese characteristics for a new era. China Surf Eng 31(1):1–6MathSciNetGoogle Scholar
  86. 86.
    Yin YH, Nee AY, Ong SK, Zhu JY, Gu PH, Chen LJ (2015) Automating design with intelligent human–machine integration. CIRP Ann 64(2):655–677CrossRefGoogle Scholar
  87. 87.
    Wang X, Liu M, Ge M, Ma J, Liu C (2016) Online control threshold optimization for complex mechanical products assembly process based on hybrid genetic particle swarm optimization. Journal of Mechanical Engineering 52(1):130–138CrossRefGoogle Scholar
  88. 88.
    Altintas Y, Aslan D (2017) Integration of virtual and on-line machining process control and monitoring. CIRP Ann 66(1):349–352CrossRefGoogle Scholar
  89. 89.
    Koike R, Ohnishi K, Aoyama T (2016) A sensorless approach for tool fracture detection in milling by integrating multi-axial servo information. CIRP Ann 65(1):385–388CrossRefGoogle Scholar
  90. 90.
    Li Z, Mei J, Cao S (2018) Modeling and control of automatic precision assembly robot with multi-information fusion measuring system and control algorithm. Acta Electron Sin 46(3):636–640Google Scholar
  91. 91.
    Wu Y, Chen C (2018) An automatic generation method of the coordinate system for automatic assembly tolerance analysis. Int J Adv Manuf Technol 95(1-4):889–903CrossRefGoogle Scholar
  92. 92.
    Paggi H, Soriano J, Lara JA (2018) A multi-agent system for minimizing information indeterminacy within information fusion scenarios in peer-to-peer networks with limited resources. Inf Sci 451:271–294MathSciNetCrossRefGoogle Scholar
  93. 93.
    Duan Z, Wu T, Guo S, Shao T, Malekian R, Li Z (2018) Development and trend of condition monitoring and fault diagnosis of multi-sensors information fusion for rolling bearings: a review. Int J Adv Manuf Technol 96(1-4):803–819CrossRefGoogle Scholar
  94. 94.
    Qi F, Tianjiang W, Fang L, Fang L, HeFei L (2018) Research on multi-camera information fusion method for intelligent perception. Multimed Tools Appl 77(12):15003–15026CrossRefGoogle Scholar
  95. 95.
    Bengson J (2016) Practical perception and intelligent action. Philos Issues 26(1):25–58CrossRefGoogle Scholar
  96. 96.
    Choi H, O’Donoghue P, Hughes M (2007) An investigation of inter-operator reliability tests for real-time analysis system. Int J Perform Anal Sport 7(1):49–61CrossRefGoogle Scholar
  97. 97.
    Jozefczyk J, Hojda M (2010) Decision making for autonomous vehicles in the presence of uncertain information. In: 2010 Conference on Control and Fault-Tolerant Systems (SysTol). IEEE, Piscataway, pp 648–653CrossRefGoogle Scholar
  98. 98.
    Wang X, Liu M, Ge M, Ling L, Liu C (2015) Research on assembly quality adaptive control system for complex mechanical products assembly process under uncertainty. Comput Ind 74:43–57CrossRefGoogle Scholar
  99. 99.
    Bi ZM, Liu Y, Baumgartner B, Culver E, Sorokin JN, Peters A, Blaine C, Jessica H, John Y, Stephen O (2015) Reusing industrial robots to achieve sustainability in small and medium-sized enterprises (SMEs). Ind Robot 42(3):264–273CrossRefGoogle Scholar
  100. 100.
    Remanufacturing Engineering Institute of Chinese Mechanical Engineering Society (2016) Remanufacturing technology roadmaps. China Science and Technology Press, Beijing (in Chinese)Google Scholar
  101. 101.
    China Academy of Engineering (2017) Research Report on the Development Strategy of China's Intelligent ManufacturingGoogle Scholar
  102. 102.
    Zhang C, Liu C, Zhao X (2017) Optimization control method for carbon footprint of machining process. Int J Adv Manuf Technol 92(5-8):1601–1607CrossRefGoogle Scholar
  103. 103.
    ZHU Q, LAI K (2019) Enhancing supply chain operations with extended corporate social responsibility practices by multinational enterprises: social capital perspective from Chinese suppliers. Int J Prod Econ 213:1–12CrossRefGoogle Scholar
  104. 104.
    Cai W, Lai KH, Liu C, Wei F, Ma M, Jia S, Jiang Z, Lv L (2019) Promoting sustainability of manufacturing industry through the lean energy-saving and emission-reduction strategy. Sci Total Environ 665:23–32CrossRefGoogle Scholar
  105. 105.
    Xu L, Wang C (2018) Sustainable manufacturing in a closed-loop supply chain considering emission reduction and remanufacturing. Resour Conserv Recycl 131:297–304CrossRefGoogle Scholar
  106. 106.
    Zhang C, Liu C, Chen J, Li Q, He K, Gao M, Cai W (2019) The coupling mechanism of reassembly quality with uncertainty of remanufactured parts. Assem Autom. CrossRefGoogle Scholar
  107. 107.
    Liu C, Cai W, Jia S, Zhang M, Guo H, Hu L, Jiang Z (2018) Emergy-based evaluation and improvement for sustainable manufacturing systems considering resource efficiency and environment performance. Energy conversion and management, 177:176–189.CrossRefGoogle Scholar
  108. 108.
    Zhang Y, Zhang H, Zhao J, Zhou Z, Wang J (2013) Review of non-destructive testing for remanufacturing of high-end equipment. Jixie Gongcheng Xuebao (Chin J Mech Eng) 49(7):80–90CrossRefGoogle Scholar

Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2019

Authors and Affiliations

  • Conghu Liu
    • 1
    • 2
  • Qinghua Zhu
    • 1
    Email author
  • Fangfang Wei
    • 1
  • Weizhen Rao
    • 1
  • JunJun Liu
    • 1
  • Jing Hu
    • 3
  • Wei Cai
    • 4
  1. 1.Sino-US Global Logistics InstituteShanghai Jiao Tong UniversityShanghaiChina
  2. 2.Key Laboratory of Spin Electron and Nanomaterials of Anhui Higher Education InstitutesSuzhou UniversitySuzhouChina
  3. 3.School of mechanical engineeringHefei University of TechnologyHeFeiChina
  4. 4.College of Engineering and TechnologySouthwest UniversityChongqingChina

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