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

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.

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Fig. 1
Fig. 2

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

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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.

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Correspondence to Xinbao Liu.

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Appendix

Appendix

The results of simulation study are represented in a graphical format in Figs. 3, 4, 5 and 6 for intuitively expressing the change of performances.

Fig. 3
figure3

Value of information at given percentiles

Fig. 4
figure4

The residual value of imperfect life information with different utilization rate

Fig. 5
figure5

The residual value of imperfect utilization information with different utilization rate

Fig. 6
figure6

Value of information with uniformly distributed in-use time at given percentile

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Yang, T., Fu, C., Liu, X. et al. Closed-loop supply chain inventory management with recovery information of reusable containers. J Comb Optim 35, 266–292 (2018). https://doi.org/10.1007/s10878-015-9987-2

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Keywords

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