Advertisement

Optimal Quality-Based Recycling and Reselling Prices of Returned SEPs in IoT Environment

  • Siyu Zong
  • Sijie LiEmail author
  • You Shang
Conference paper
  • 43 Downloads
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 193)

Abstract

In IoT (Internet of Things) environment, OEM (Original Equipment Manufacturer) can perceive and monitor the quality status of SEPs (sensor-embedded products) in a real-time way. In the paper, considering the characteristic of SEPs, an algorithm based on GA (genetic algorithm) is proposed for OEM to determine recycling and reselling prices of returned SEPs. A benchmark example is utilized to verify the effectiveness of the proposed algorithm. We find that OEM’s profit is positively related to the maximum amount of available SEPs and the demand on returned SEPs in the secondary market; however, they are negatively related to customers’ expected rewards. For the purpose of protecting environment, governments should subsidize OEM with insufficient production capacity but are regulated to handle their end-of-use SEPs.

Keywords

Internet of Things Quality Pricing SEPs 

References

  1. 1.
    Fang, C., Liu, X., Pardalos, P.M., Pei, J.: Optimization for a three-stage production system in the Internet of Things: procurement, production and product recovery, and acquisition. Int. J. Adv. Manuf. Technol. 83, 689–710 (2016)CrossRefGoogle Scholar
  2. 2.
    Ondemir, O., Gupta, S.M.: Quality management in product recovery using the Internet of Things: an optimization approach. Comput. Ind. 65(3), 491–504 (2014)CrossRefGoogle Scholar
  3. 3.
    Minner, S., Kiesmüller, G.P.: Dynamic product acquisition in closed loop supply chains. Int. J. Prod. Res. 50(11), 2836–2851 (2012)CrossRefGoogle Scholar
  4. 4.
    Subulan, K., Taşan, A.S., Baykasoğlu, A.: Designing an environmentally conscious tire closed-loop supply chain network with multiple recovery options using interactive fuzzy goal programming. Appl. Math. Model. 39(9), 2661–2702 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    Govindan, K., Jha, P.C., Garg, K.: Product recovery optimization in closed-loop supply chain to improve sustainability in manufacturing. Int. J. Prod. Res. 54(5), 463–1486 (2016)CrossRefGoogle Scholar
  6. 6.
    Masoudipour, E., Amirian, H., Sahraeian, R.: A novel closed-loop supply chain based on the quality of returned products. J. Clean. Prod. 151, 344–355 (2017)CrossRefGoogle Scholar
  7. 7.
    Zhang, Y., Liu, S., Liu, Y., Yang, H., Li, M., Huisingh, D., Wang, L.: The ‘Internet of Things’ enabled real-time scheduling for remanufacturing of automobile engines. J. Clean. Prod. 185, 562–575 (2018)CrossRefGoogle Scholar
  8. 8.
    Niknejad, A., Petrovic, D.: Optimisation of integrated reverse logistics networks with different product recovery routes. Eur. J. Oper. Res. 238(1), 143–154 (2014)MathSciNetCrossRefGoogle Scholar
  9. 9.
    Xiong, Y., Zhao, P., Xiong, Z., Li, G.: The impact of product upgrading on the decision of entrance to a secondary market. Eur. J. Oper. Res. 252(2), 443–454 (2016)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Jun, H.B., Shin, J.H., Kim, Y.S., Kiritsis, D., Xirouchakis, P.: A framework for RFID applications in product lifecycle management. Int. J. Comput. Integr. Manuf. 22(7), 595–615 (2009)CrossRefGoogle Scholar
  11. 11.
    Goldberg, D.E., Holland, J.H.: Genetic algorithms and machine learning. Mach. Learn. 3(7), 95–99 (1988)CrossRefGoogle Scholar
  12. 12.
    Radhi, M., Zhang, G.: Optimal configuration of remanufacturing supply network with return quality decision. Int. J. Prod. Res. 54(5), 1487–1502 (2016)CrossRefGoogle Scholar

Copyright information

© The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.School of Economics and ManagementSoutheast UniversityNanjingChina

Personalised recommendations