Advertisement

Journal of Combinatorial Optimization

, Volume 35, Issue 1, pp 266–292 | Cite as

Closed-loop supply chain inventory management with recovery information of reusable containers

  • Tianji Yang
  • Chao Fu
  • Xinbao Liu
  • Jun Pei
  • Lin Liu
  • Panos M. Pardalos
Article

Abstract

This paper considers a closed-loop supply chain consisting of one-manufacturer and one-retailer. This supply chain provides single-kind products with reusable containers. The main purpose of this study is to explore and evaluate the value of recovery information captured by embedded sensors in the environment of internet of things. The recovery information of containers dynamically monitors recovery status and provides a reliable estimation of return quantity. The value of information is measured by the cost saving performances with full, partial or no recovery information. When the full or partial recovery information is available, the decisions are made based on the known quantities of the usable or total return flow. When no recovery information is available, the decisions are made based on the stationary distribution of the return flow. A periodic inventory model is built with uncertainties of forward and reverse flows. Then, a myopic order policy is proposed for the different levels of information utilization. Through the optimality analysis, we introduce a farsighted inventory control policy. Using the general result of Markov decision processes, the performance of heuristic policies is displayed. The farsighted policy performs better than the myopic policy. In addition, the farsighted policy helps to lessen the convex impact of utilization rate on the expected cost. Afterwards, we extend the model with the selective disposal behavior. A simulation study is used to depict sensitivity and robustness of the farsighted policy. Finally, we extend the simulation experiment with uniformly distributed in-use time for a more general applicability.

Keywords

Closed-loop supply chain Reusable containers Internet of things Value of information 

Abbreviations

CLSC

Closed-loop supply chain

RFID

Radio frequency identification

IoT

Internet of things

GIS

Geographic information system

RTI

Returnable transportation items

EAGLET

“Event, agent, location, equipment, and thing” ontology model

VOI

Value of information

EOL

End-of-life

MDP

Markov decision process

DTMDP

Discrete-time Markov decision process

VIP

Virtual inventory of products

EC

Expected cost

VOFI

Value of full information

VOPI

Value of partial information

Notes

Acknowledgments

This work was supported by the National Natural Science Foundation of China (Nos. 71231004, 71171071, 71521001), Anhui Province Natural Science Foundation (No. 1608085QG167), and the Fundamental Research Funds for the Central Universities (Nos. JZ2015HGBZ0116, JZ2015HGBZ0117). Panos M. Pardalos is partially supported by the project of “Distinguished International Professor by the Chinese Ministry of Education” (MS2014HFGY026). In this paper, Tianji Yang is responsible for the numerical example and coding work. The detailed results attached with manuscript are uploaded to the submission system. Thanks for the valuable suggestions from the anonymous reviewers.

Supplementary material

10878_2015_9987_MOESM1_ESM.pdf (318 kb)
Supplementary material 1 (pdf 318 KB)

References

  1. Ahiska SS, Kurtul E (2014) Modeling and analysis of a product substitution strategy for a stochastic manufacturing/remanufacturing system. Comput Ind Eng 72(4):1–11CrossRefGoogle Scholar
  2. Awin F, Zipkin P (1986) An inventory model with limited production capacity and uncertain demands II: the discounted-cost criterion. Math Oper Res 11(2):208–215MathSciNetzbMATHCrossRefGoogle Scholar
  3. Atamer B, Bakal İS, Bayındır ZP (2013) Optimal pricing and production decisions in utilizing reusable containers. Int J Prod Econ 143(2):222–232CrossRefGoogle Scholar
  4. Bellman R (1957) A Markovian decision process. Indiana Univ Math J 6(4):679–684MathSciNetzbMATHCrossRefGoogle Scholar
  5. Bryan N, Srinivasan MM (2014) Real-time order tracking for supply systems with multiple transportation stages. Eur J Oper Res 236(2):548–560MathSciNetzbMATHCrossRefGoogle Scholar
  6. Cao H, Folan P, Potter D, Browne J (2011) Knowledge-enriched shop floor control in end-of-life business. Prod Plan Control 22(2):174–193CrossRefGoogle Scholar
  7. Chew EP, Huang HC, Horiana (2002) Performance measures for returnable inventory: a case study. Prod Plan Control 13(5):462–469CrossRefGoogle Scholar
  8. Clottey T, Benton WC, Srivastava R (2012) Forecasting product returns for remanufacturing operations. Decis Sci 43(4):589–614CrossRefGoogle Scholar
  9. de Brito MP, van der Laan EA (2009) Inventory control with product returns: the impact of imperfect information. Eur J Oper Res 194(1):85–101zbMATHCrossRefGoogle Scholar
  10. Fleischmann M, Kuik R (2003) On optimal inventory control with independent stochastic item returns. Eur J Oper Res 151(1):25–37MathSciNetzbMATHCrossRefGoogle Scholar
  11. Grubic T, Fan I-S (2010) Supply chain ontology: review, analysis and synthesis. Comput Ind 61(8):776–786CrossRefGoogle Scholar
  12. Geerts GL, O’Leary DE (2014) A supply chain of things: the EAGLET ontology for highly visible supply chains. Decis Support Syst 63:3–22CrossRefGoogle Scholar
  13. Guide VDR Jr, Jayaraman V, Srivastava R (1999) Production planning and control for remanufacturing: a state-of-the-art survey. Robot Comput Integr Manuf 15(3):221–230CrossRefGoogle Scholar
  14. Guide VDR Jr (2000) Production planning and control for remanufacturing: industry practice and research needs. J Oper Manag 18(4):467–483CrossRefGoogle Scholar
  15. Ilgin MA, Gupta SM (2011) Performance improvement potential of sensor embedded products in environmental supply chains. Resour Conserv Recycl 55(6):580–592CrossRefGoogle Scholar
  16. Li C, Liu F, Cao H, Wang Q (2009) A stochastic dynamic programming based model for uncertain production planning of re-manufacturing system. Int J Prod Res 47(13):3657–3668Google Scholar
  17. Kenné J, Dejax P, Gharbi A (2012) Production planning of a hybrid manufacturing-remanufacturing system under uncertainty within a closed-loop supply chain. Int J Prod Econ 135(1):81–93CrossRefGoogle Scholar
  18. Ketzenberg M, Bloemhof J, Gaukler G (2015) Managing perishables with time and temperature history. Prod Oper Manag 24(1):54–70CrossRefGoogle Scholar
  19. Ketzenberg ME, Laan EVD, Teunter RH (2006) Value of information in closed loop supply chains. Prod Oper Manag 15(3):393–406CrossRefGoogle Scholar
  20. Ketzenberg ME (2009) The value of information in a capacitated closed loop supply chain. Eur J Oper Res 198(2):491–503MathSciNetzbMATHCrossRefGoogle Scholar
  21. Kim T, Glock CH, Kwon Y (2014) A closed-loop supply chain for deteriorating products under stochastic container return times. Omega 43:30–40CrossRefGoogle Scholar
  22. Kim T, Glock CH (2014) On the use of RFID in the management of reusable containers in closed-loop supply chains under stochastic container return quantities. Transp Res Part E 64:12–27CrossRefGoogle Scholar
  23. Kiziltoprak T, Schumann R, Hahn A, Behrens J (2008) Distributed process control by smart containers. In: Dynamics in logistics. Springer, Heidelberg, , pp 321–328Google Scholar
  24. Kleindorfer PR, Singhal K, Wassenhove LN (2005) Sustainable operations management. Prod Oper Manag 14(4):482–492CrossRefGoogle Scholar
  25. Kroon L, Vrijens G (1995) Returnable containers: an example of reverse logistics. Int J Phys Distrib Logist Manag 25(2):56–68CrossRefGoogle Scholar
  26. Kumar VV, Chan FTS (2011) A superiority search and optimisation algorithm to solve RFID and an environmental factor embedded closed loop logistics model. Int J Prod Res 49(16):4807–4831CrossRefGoogle Scholar
  27. Lee CKM, Chan TM (2009) Development of RFID-based reverse logistics system. Expert Syst Appl 36(5):9299–9307MathSciNetCrossRefGoogle Scholar
  28. Mason A, Shaw A, Al-Shamma’a A (2012) Peer-to-peer inventory management of returnable transport items: a design science approach. Comput Ind 63(3):265–274CrossRefGoogle Scholar
  29. Menesatti P, Canali E, Sperandio G, Burchi G, Devlin G, Costa C (2012) Cost and waste comparison of reusable and disposable shipping containers for cut flowers. Packag Technol Sci 25(4):203–215CrossRefGoogle Scholar
  30. Mitra Subrata (2013) Periodic review policy for a two-echelon closed-loop inventory system with correlations between demands and returns. OPSEARCH 50(4):604–615MathSciNetzbMATHCrossRefGoogle Scholar
  31. Morton Thomas E (1971) Technical note—on the asymptotic convergence rate of cost differences for Markovian decision processes. Oper Res 19(1):244–248zbMATHCrossRefGoogle Scholar
  32. Nakashima K, Gupta SM (2013) Optimization of a reverse manufacturing system with multiple virtual inventories. Manuf Model Manag Control 7(1):99–104Google Scholar
  33. Nakashima K, Arimitsu H, Nose T, Kuriyama S (2004) Optimal control of a remanufacturing system. Int J Prod Res 42(17):3619–3625(7)CrossRefGoogle Scholar
  34. Ondemir Onder, Gupta Surendra M (2014) Quality management in product recovery using the internet of things: an optimization approach. Comput Ind 65(3):491–504CrossRefGoogle Scholar
  35. Ondemir Onder, Ilgin Mehmet Ali, Gupta Surendra M (2012) Optimal end-of-life management in closed-loop supply chains using RFID and sensors. IEEE Trans Ind Inform 8(3):719–728CrossRefGoogle Scholar
  36. Panagiotidou S, Nenes G, Zikopoulos C (2013) Optimal procurement and sampling decisions under stochastic yield of returns in reverse supply chains. OR Spectr 35(1):1–32MathSciNetzbMATHCrossRefGoogle Scholar
  37. Parvini M, Atashi A, Husseini SMM, Esfahanipour A (2014) A two-echelon inventory model with product returns considering demands dependent return rates. Int J Adv Manuf Technol 72(1–4):107–118CrossRefGoogle Scholar
  38. Pourakbar M, van der Laan E, Dekker R (2014) End-of-life inventory problem with phase-out returns. Gen Inf 23(9):1561–1576Google Scholar
  39. Ruiz-Benítez R, Ketzenberg M, van der Laan EA (2014) Managing consumer returns in high clockspeed industries. Omega 43:54–63CrossRefGoogle Scholar
  40. Shi X, Tao D, Voß S (2011) RFID technology and its application to port-based container logistics. J Organ Comput Electron Commer 21(4):332–347Google Scholar
  41. Toktay LB, Wein LM, Zenios SA (2000) Inventory management of remanufacturable products. Manag Sci 46(11):1412–1426zbMATHCrossRefGoogle Scholar
  42. Teng H, Hsu P, Chiu Y, Wee HM (2011) Optimal ordering decisions with returns and excess inventory. Appl Math Comput 217(22):9009–9018MathSciNetzbMATHGoogle Scholar
  43. Visich JK, Li S, Khumawala BM (2007) Enhancing product recovery value in closed-loop supply chains with RFID. J Manag Issues, pp 436–452Google Scholar
  44. Vorasayan J, Ryan SM (2006) Optimal price and quantity of refurbished products. Prod Oper Manag 15(3):369–383Google Scholar
  45. Yang X, Moore P, Pu JS, Wong CB (2009) A practical methodology for realizing product service systems for consumer products. Comput Ind Eng 56(1):224–235CrossRefGoogle Scholar
  46. Zanoni S, Ferretti I, Tang O (2011) Cost performance and bullwhip effect in a hybrid manufacturing and remanufacturing system with different control policies. Int J Prod Res 44(18):3847–3862zbMATHGoogle Scholar
  47. Zerhouni H, Gayon J-P, Frein Y (2013) Influence of dependency between demands and returns in a reverse logistics system. Int J Prod Econ 143(1):62–71CrossRefGoogle Scholar
  48. Zhou L, Disney SM (2006) Bullwhip and inventory variance in a closed loop supply chain. OR Spectr 28(1):127–149zbMATHCrossRefGoogle Scholar
  49. Zolfagharinia H, Hafezi M, Farahani RZ, Fahimnia B (2014) A hybrid two-stock inventory control model for a reverse supply chain. Transp Res Part E 2014:141–161CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2015

Authors and Affiliations

  • Tianji Yang
    • 1
    • 2
  • Chao Fu
    • 1
    • 2
  • Xinbao Liu
    • 1
    • 2
  • Jun Pei
    • 1
    • 2
  • Lin Liu
    • 1
    • 2
  • Panos M. Pardalos
    • 3
  1. 1.School of ManagementHefei University of TechnologyHefeiChina
  2. 2.Key Laboratory of Process Optimization and Intelligent Decision-making of Ministry of EducationHefeiChina
  3. 3.Department of Industrial and Systems Engineering, Center for Applied OptimizationUniversity of FloridaGainesvilleUSA

Personalised recommendations