Skip to main content

Advertisement

Log in

Optimization for a three-stage production system in the Internet of Things: procurement, production and product recovery, and acquisition

The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

Recovery of the end-of-use products has become a topic of considerable interest in the advanced manufacturing industry due in part to uncertainties in the quality and volume of product returns. The Internet of Things (IoT) that enables the tracing, detecting, storing, and analyzing the product life cycle data for each individual item can mitigate or eliminate these uncertainties. In this paper, an integrated three-stage model is presented based on IoT technology for the optimization of procurement, production and product recovery, pricing and strategy of return acquisition. The remaining value is used to measure the return condition. The model considers three recovery options related to refurbishing, component reuse and disposal, and the value deterioration for satisfying the product demand in each stage of product life cycle (PLC). A novel particle swarm optimization (PSO) algorithm based on two heuristic methods is proposed to solve the problem. A numerical example and sensitivity analysis are used to illustrate the performance of both algorithm and applicability of the model.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Similar content being viewed by others

References

  1. Neto JQF, Walther G, Bloemhof J, van Nunen JAEE, Spengler T (2010) From closed-loop to sustainable supply chains: the WEEE case. Int J Prod Res 48(15):4463–4481

    Article  MATH  Google Scholar 

  2. Savage M (2005) Implementation of waste electric and electronic equipment directive in EU 25. Technical report, European Commission, Joint Research Centre

  3. Sarma S, Brock DL, Ashton K (2000) The networked physical world. TR MIT-AUTOID-WH-001, MIT Auto-ID Center

  4. Gubbi J, Buyya R, Marusic S (2013) Palaniswami M (2013) internet of things (IoT): a vision, architectural elements, and future directions. Futur Gener Comput Syst 29:1645–1660

    Article  Google Scholar 

  5. Kiritsis D (2011) Closed-loop PLM for intelligent products in the ear of the internet of things. Comput Aided Des 43(5):479–501

    Article  Google Scholar 

  6. Robotis A, Bhattacharya S, Van Wassenhove LN (2005) The effect of remanufacturing on procurement decisions for resells in secondary markets. Eur J Oper Res 163(3):688–705

    Article  MATH  Google Scholar 

  7. Zhou W, Piramuthu S (2013) Remanufacturing with PFID item-level information: optimization, waste reduction and quality improvement. Int J Prod Econ 145(2):647–657

    Article  Google Scholar 

  8. Lu DX (2011) Information architecture for supply chain quality management. Int J Prod Res 49(1):183–198

    Article  Google Scholar 

  9. Jammes F, Smit H (2005) Service-oriented paradigms in industrial automation. IEEE T Ind Inform 1(1):62–70

    Article  Google Scholar 

  10. Tao F, Cheng Y, Xu LD, Zhang L, Li BH (2014) CCIoT-CMfg: cloud computing and internet of things-based cloud manufacturing service system. IEEE T Ind Inform 10(2):1435–1442

    Article  Google Scholar 

  11. Guide VDR, Daniel R, Souza GC, Van Wassenhove LN et al (2006) Time value of commercial production returns. Manag Sci 52(8):1200–1214

    Article  MATH  Google Scholar 

  12. Guide VDR, Van Wassenhove LN (2009) The evolution of closed-loop supply chain research. Oper Res 57(1):10–18

    Article  MATH  Google Scholar 

  13. Bai H (2009) Reverse supply chains coordination and design for profitable returns: an example of ink cartridge. Master Thesis, Worcester Polytechnic Institute

  14. Anderson CR, Zeithaml CP (1984) Stage of the product life cycle, business strategy, and business performance. Acad Manag J 27(1):5–24

    Article  Google Scholar 

  15. van der Laan E, Salomon M (1997) Production planning and inventory control with remanufacturing and disposal. Eur J Oper Res 102(2):264–278

    Article  MATH  Google Scholar 

  16. Kurkin O, Januska M (2010) Product life cycle in digital factory. In Knowledge management and innovation: a business competitive edge perspective. 15th International Business Information Management Association, Cairo, Egypt, Nov 06–07, ISBN: 978-0-9821489-4-5, pp 1881–1886

  17. Prince M, Smith JC, Geunes J (2014) A three-stage procurement optimization problem under uncertainty. Nav Res Logist 60(5):395–412

    Article  MathSciNet  Google Scholar 

  18. Chen K (2012) Procurement strategy and coordination mechanism of the supply chain with one manufacturer and multiple suppliers. Int J Prod Econ 138(1):125–135

    Article  Google Scholar 

  19. Goyal SK, Deshmukh SG (1992) Integrated procurement production systems—a review. Eur J Oper Res 62(1):1–10

    Article  MATH  Google Scholar 

  20. Guide VDR, Van Wassenhove LN (2001) Managing product returns for remanufacturing. Prod Oper Manag 10(2):142–155

    Article  Google Scholar 

  21. Guide VDR, Jayaraman V (2000) Production acquisition management: current industry practice and a proposed framework. Int J Prod Res 38(16):3779–3800

    Article  MATH  Google Scholar 

  22. Mukhopadhyay SK, Ma H (2009) Joint procurement and production decisions in remanufacturing under quality and demand uncertainty. Int J Prod Econ 120(1):5–17

    Article  Google Scholar 

  23. Li X, Li YJ, Saghafian S (2013) A hybrid manufacturing/remanufacturing system with random remanufacturing yield and marked-driven product acquisition. IEEE T Eng Manag 60(2):424–437

    Article  Google Scholar 

  24. Minner S, Kiesmuller GP (2012) Dynamic product acquisition in closed loop supply chains. Int J Prod Res 50(11):2836–2851

    Article  Google Scholar 

  25. Niknejad A, Petrovic D (2014) Optimization of integrated reverse logistics networks with different product recovery routes. Eur J Oper Res 238(1):143–154

    Article  MathSciNet  Google Scholar 

  26. Ondemir O, Ilgin MA, Gupta SM (2012) Optimal end-of-life management in closed-loop supply chains using RFID and sensors. IEEE T Ind Inform 8(3):719–728

    Article  Google Scholar 

  27. Baki MF, Chaouch BA, Abdul-Kader W (2014) A heuristic procedure for the dynamic lot sizing problem with remanufacturing and product recovery. Comput Oper Res 43:225–236

    Article  MathSciNet  Google Scholar 

  28. Ilgin MA, Gupta SM (2010) Environmental conscious manufacturing and product recovery (ECMPRO): a review of the state of the art. J Environ Manag 91(3):563–591

    Article  Google Scholar 

  29. Gungor A, Gupta SM (1999) Issues in environmental conscious manufacturing and product recovery: a survey. Comput Ind Eng 36(4):811–853

    Article  Google Scholar 

  30. Fu Q, Lee CY, Teo CP (2010) Procurement management using option contracts: random spot price and the portfolio effect. IIE Trans 43(11):793–811

    Article  Google Scholar 

  31. Xu H (2010) Managing production and procurement through option contracts in supply chains with random yield. Int J Prod Econ 126(2):306–131

    Article  Google Scholar 

  32. Yu MC, Goh M, Lin HC (2012) Fuzzy multi-objective vendor selection under lean procurement. Euro J Oper Res 219(2):305–311

    Article  MATH  Google Scholar 

  33. Kim SH, Netessine S (2013) Collaborative cost reduction and component procurement under information asymmetry. Manag Sci 59(1):189–206

    Article  Google Scholar 

  34. Das BC, Das B, Mondal SK (2013) Integrated supply chains model for a deteriorating item with procurement cost dependent credit period. Comput Ind Eng 64(3):788–796

    Article  Google Scholar 

  35. Hu F, Lim CC, Lu Z (2014) Optimal production and procurement decisions in a supply chain with an option contract and partial backordering under uncertainties. Appl Math Comput 232(1):1225–1234

    Article  MathSciNet  Google Scholar 

  36. Galbreth MR, Blackburn JD (2006) Optimal acquisition and sorting policies for remanufacturing. Prod Oper Manag 15(3):384–392

    Article  Google Scholar 

  37. Vadde S, Kamarthi SV, Gupta SM (2007) Optimal pricing of reusable and recyclable components under alternative product acquisition mechanisms. Int J Prod Res 45(18–19):4621–4652

    Article  MATH  Google Scholar 

  38. Teunter RH, Flapper SDP (2011) Optimal core acquisition and remanufacturing policies under uncertain core quality fractions. Eur J Oper Res 210(2):241–248

    Article  MATH  Google Scholar 

  39. Zhou SX, Yu Y (2011) Optimal product acquisition, pricing, and inventory management for systems with remanufacturing. Oper Res 59(2):514–521

    Article  MathSciNet  MATH  Google Scholar 

  40. Kleber R, Schulz T, Voigt G (2012) Dynamic buy-back for product recovery in end of life spare parts procurement. Int J Prod Res 50(6):1476–1488

    Article  Google Scholar 

  41. Zeng AZ (2013) Coordination mechanisms for a three-stage reverse supply chain to increase profitable returns. Nav Res Logist 60(1):32–45

    Article  Google Scholar 

  42. Inderfurth K, Kleber R (2013) An advanced heuristic for multiple-option space parts procurement after end-of-production. Prod Oper Manag 22(1):54–70

    Article  Google Scholar 

  43. Konstantaras I, Skouri K (2010) Lot sizing for a single product recovery system with variable setup numbers. Eur J Oper Res 203(2):326–335

    Article  MATH  Google Scholar 

  44. Vadde S, Zeid A, Kamarthi SV (2011) Pricing decisions in a multi-criteria setting for product recovery facilities. Omega-Int J Manag S 39(2):186–193

    Article  Google Scholar 

  45. Kim T, Goyal SK (2011) Determination of the optimal production policy and product recovery policy: the impacts of sales margin of recovered product. Int J Prod Res 49(9):2535–2550

    Article  Google Scholar 

  46. Ondemir O, Gupta SM (2014) Quality management in product recovery using the internet of things: an optimization approach. Comput Ind 65(3):491–504

    Article  Google Scholar 

  47. Miorandi D, Sicari S, Pellegrini FD, Chlamtac I (2012) Internet of things: vision, applications and research challenges. Ad Hoc Netw 10(7):1497–1516

    Article  Google Scholar 

  48. Yang L, Yang SH, Plotnick L (2013) How the internet of things technology enhances emergency response operations. Technol Forecast Soc 80(9):1854–1867

    Article  Google Scholar 

  49. Zhang L, Luo Y, Tao F et al (2014) Cloud manufacturing: a new manufacturing paradigm. Enterp Inf Syst-UK 8(2):167–187

    Article  Google Scholar 

  50. Sung WT, Chiang YC (2012) Improved particle swarm optimization algorithm for android medical care IOT using modified parameters. J Med Syst 36(6):3755–3763

    Article  Google Scholar 

  51. Li HS, Dimitrovski A, Song JB, Han Z, Qian LJ (2014) Communication infrastructure design in cyber physical systems with applications in smart grids: a hybrid system framework. IEEE Commun Surv Tutorials 16(3):1689–1708

    Article  Google Scholar 

  52. Jun HB, Shin JH, Kiritsis D, Xirouchakis P (2007) System architecture for closed-loop PLM. Int J Comput Integ Matuf 20(7):684–698

    Article  Google Scholar 

  53. Jun HB, Kiritsis D, Xirouchakis P (2007) Research issues on closed-loop PLM. Comput Ind 58(8–9):855–868

    Article  Google Scholar 

  54. Yang XY, Moore P, Chong SK (2009) Intelligent products: from lifecycle data acquisition to enabling product related services. Comput Ind 60(3):184–194

    Article  Google Scholar 

  55. Luttropp C, Johansson J (2010) Improved recycling with life cycle information tagged to the product. J Clean Prod 18(4):346–354

    Article  Google Scholar 

  56. Parlikad AK, McFarlane D (2007) RFID-based production information in end-of-life decision making. Control Eng Pract 15(11):1348–1363

    Article  Google Scholar 

  57. Nawa K, Chandrasiri NP, Yannagihara T, Oguchi K (2014) Cyber physical system for vehicle application. T I Meas Control 36(7):898–905

    Article  Google Scholar 

  58. Wan JF, Zhang DQ, Zhao SJ, Yang LT, Lloret J (2014) Context-aware vehicular cyber-physical systems with cloud support: architecture, challenges, and solutions. IEEE Commun Mag 52(8):106–113

    Article  Google Scholar 

  59. Jia DY, Lu KJ, Wang JP (2014) On the network connectivity of platoon-based vehicular cyber-physical systems. Transp Res C-Emer 40:215–230

    Article  Google Scholar 

  60. Facchinetti T, Della Vedova ML (2011) Real-time modeling for direct load control in cyber-physical power systems. IEEE T Ind Inform 7(4):689–698

    Article  Google Scholar 

  61. Li HS, Lai LF, Poor HV (2012) Multicast routing for decentralized control of cyber physical systems with an application in smart grid. IEEE J Sel Areas Commun 30(6):1097–1107

    Article  Google Scholar 

  62. Li L, Li S, Zhao S (2014) QoS-aware scheduling of service-oriented internet of things. IEEE T Ind Inform 10(2):1497–1505

    Article  Google Scholar 

  63. Haque SA, Aziz SM, Rahman M (2014) Review of cyber-physical system in healthcare. Int J Distrib Sens Netw 2014:1–20

    Article  Google Scholar 

  64. Kagermann H, Wolfgang W, Helbi J (2013) Recommendations for implementing the strategic initiative INDUSTRIE 4.0. http://www.plattform-i40.de/finalreport2013

  65. Leber J (2012) General electric pitches an industrial internet. MIT Technology Review. http://www.technologyreview.com

  66. Evans PC, Annunziata M (2012) Industrial internet: pushing the boundaries of minds and machines. http://files.gereports.com/wp-content/uploads/2012/11/ge-industrial-internet-vision-paper.pdf

  67. Zolfagharinia H, Hafezi M, Farahani RZ, Fahimnia B (2014) A hybrid two-stock inventory control model for a reverse supply chain. Transp Res E-Log 67:141–161

    Article  Google Scholar 

  68. Blackburn JD, Guide VDR, Souza GC, Van Wassenhove LN (2004) Reverse supply chains for commercial returns. Calif Manag Rev 46(2):6–22

    Article  Google Scholar 

  69. Jayaraman V (2006) Production planning for closed-loop supply chains with product recovery and reuse: an analytical approach. Int J Prod Res 44(5):981–998

    Article  MATH  Google Scholar 

  70. Kumar M, Husian M, Upreti N, Gupta D (2010) Genetic algorithm: review and application. Int J Inform Tech Knowl Manag 2(2):451–454

    Google Scholar 

  71. Dorigo M, Blum C (2005) Ant colony optimization theory: a survey. Theor Comput Sci 344:243–278

    Article  MathSciNet  MATH  Google Scholar 

  72. Suman B, Kumar P (2006) A survey of simulated annealing as a tool for single and multiobjective optimization. J Oper Res Soc 57:1143–1160

    Article  MATH  Google Scholar 

  73. Banks A, Vincent J, Anyakoha C (2008) A review of particle swarm optimization. Part II: hybridization, combinatorial, multicriteria and constrained optimization, and indicative applications. Nat Comput 7(1):109–124

    Article  MathSciNet  MATH  Google Scholar 

  74. Kennedy J, Eberhart RC (1997) A discrete binary version of the particle swarm algorithm. Proceedings of the International Conference on System, Man and Cybernetics, October 12–15, Florida, USA, Piscataway, NJ: IEEE Service Centre: 4104–4109

  75. Eberhart RC, Shi Y (1998) Comparison between genetic algorithms and particle swarm optimization. Proceedings of the 7th Annual Conference on Evolutionary Programming, Springer, Berlin, Germany, pp 611–616

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Chang Fang.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Fang, C., Liu, X., Pardalos, P.M. et al. 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). https://doi.org/10.1007/s00170-015-7593-1

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-015-7593-1

Keywords

Navigation