Weight reduction technology and supply chain network design under carbon emission restriction

Abstract

As policies and regulations related to environmental protection and resource constraints are becoming increasingly tougher, corporations may face the difficulty of determining the optimal trade-offs between economic performance and environmental concerns when selecting product technology and designing supply chain networks. This paper considers weight reduction technology selection and network design problem in a real-world corporation in China which produces, sells and recycles polyethylene terephthalate (PET) bottles used for soft drinks. The problem is addressed while taking consideration of future regulations of carbon emissions restrictions. First, a deterministic mixed-integer linear programming model is developed to analyze the influence of economic cost and carbon emissions for different selections in terms of the weight of PET bottle, raw material purchasing, vehicle routing, facility location, manufacturing and recycling plans, etc. Then, the robust counterpart of the proposed mixed-integer linear programming model is used to deal with the uncertainty in supply chain network resulting from the weight reduction. Finally, results show that though weight reduction is both cost-effective and environmentally beneficial, the increased cost due to the switching of the filling procedure from hot-filling to aseptic cold-filling and the incumbent uncertainties have impacts on the location of the Pareto frontier. Besides, we observe that the feasible range between economic cost and carbon emission shrinks with weightreduction; and the threshold of restricted volume of carbon emission decreases with the increase of uncertainty in the supply chain network.

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

Fig. 1

Source: Euronmonitor International

Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7

Abbreviations

t :

The index of operational periods \(t=1,\ldots , T\)

i :

The index of potential new PET chips suppliers \(i =1,\ldots ,I\)

j :

The index of potential manufacturing centers \(j=1,\ldots , J\)

k :

The index of potential distribution center locations \(k=1,\ldots ,K\)

m :

The index of potential recycling center locations \(m=1,\ldots ,M\)

n :

The index of markets \(n =1,\ldots , N\)

\({PN}_{ijt}\) :

The quantity of new PET chips in manufacturing center j provided by supplier i in period t

\({MD}_{jkt}\) :

The quantity of PET bottles shipped from manufacturing center j to distribution center k in period t

\({DX}_{knt}\) :

The quantity of PET bottles shipped from distribution center k to market n in period t

\({RX}_{mnt}\) :

The quantity of returned PET bottles recycled from market n to recycling center m in period t

\({MR}_{jmt}\) :

The quantity of recovered PET chip from recycling center m to manufacturing center j in period t

\(S_{i}\) :

\(=\left\{ {\begin{array}{ll} {1} &{} \hbox {If a new PET chips supplier } i \hbox { is opened,}\\ {0} &{} \hbox { Otherwise,}\end{array}} \right. \)

\({MC}_{j}\) :

\(=\left\{ {\begin{array}{ll} {1} &{} \hbox { If a manufacturing center }j\hbox { is opened, }\\ 0 &{} \hbox { Otherwise,}\\ \end{array}} \right. \)

\({DC}_{k}\) :

\(=\left\{ {\begin{array}{ll} {1} &{} \hbox { If a distribution center }k \hbox { is opened, }\\ {0} &{} \hbox {Otherwise,}\\ \end{array}} \right. \)

\({RC}_{m}\) :

\(=\left\{ {\begin{array}{ll} 1 &{} \hbox {If a recycling center }m\hbox { is opened, }\\ 0 &{} \hbox {Otherwise,}\\ \end{array} }\right. \)

\(\theta _{t}\) :

Average recovery rate in period t

\(\sigma _{mt}\) :

Discard rate of unusable recycling PET chips at recycling center m in period t

\(\gamma \) :

Conversion coefficient from chips to a bottle

\(\omega \) :

Ratio of recovered chips contained in a bottle

\(\mathrm {\Delta }\) :

Net weight of water contained in a bottle

\(d_{nt}\) :

Demand of market n in period t

References

  1. Abdallah, T., Diabat, A., & Simchi-Levi, D. (2012). Sustainable supply chain design: A closed-loop formulation and sensitivity analysis. Production Planning and Control, 23(2–3), 120–133.

    Google Scholar 

  2. Ben-Tal, A., Golany, B., Nemirovski, A., & Vial, J. P. (2005). Retailer-supplier flexible commitments contracts: A robust optimization approach. Manufacturing and Service Operations Management, 7(3), 248–271.

    Google Scholar 

  3. Bertsimas, D., & Sim, M. (2004). The price of robustness. Operations Research, 52(1), 35–53.

    Google Scholar 

  4. Bojarski, A. D., Laínez, J. M., Espuna, A., & Puigjaner, L. (2009). Incorporating environmental impacts and regulations in a holistic supply chains modeling: An LCA approach. Computers and Chemical Engineering, 33(10), 1747–1759.

    Google Scholar 

  5. Chaabane, A., Ramudhin, A., & Paquet, M. (2012). Design of sustainable supply chains under the emission trading scheme. International Journal of Production Economics, 135(1), 37–49.

    Google Scholar 

  6. Chen, C. (2001). Design for the environment: A quality-based model for green product development. Management Science, 47(2), 250–263.

    Google Scholar 

  7. Chiang, T. A., & Che, Z. H. (2015). A decision-making methodology for low-carbon electronic product design. Decision Support Systems, 71, 1–13.

    Google Scholar 

  8. Chiu, C. H., & Choi, T. M. (2016). Supply chain risk analysis with mean-variance models: A technical review. Annals of Operations Research, 240(2), 489–507.

    Google Scholar 

  9. Chiu, M. C., & Chu, C. H. (2012). Review of sustainable product design from life cycle perspectives. International Journal of Precision Engineering and Manufacturing, 13(7), 1259–1272.

    Google Scholar 

  10. Cholette, S., & Venkat, K. (2009). The energy and carbon intensity of wine distribution: A study of logistical options for delivering wine to consumers. Journal of Cleaner Production, 17(16), 1401–1413.

    Google Scholar 

  11. Dong, C., Shen, B., Chow, P. S., Yang, L., & Ng, C. T. (2016). Sustainability investment under cap-and-trade regulation. Annals of Operations Research, 240(2), 509–531.

    Google Scholar 

  12. Euromonitor International. http://www.euromonitor.com/soft-drinks.

  13. Evans, L., Lohse, N., & Summers, M. (2013). A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information. Expert Systems with Applications, 40(16), 6412–6426.

    Google Scholar 

  14. Fahimnia, B., Sarkis, J., Dehghanian, F., Banihashemi, N., & Rahman, S. (2013). The impact of carbon pricing on a closed-loop supply chain: An Australian case study. Journal of Cleaner Production, 59(18), 210–225.

    Google Scholar 

  15. Fattahi, M., Mahootchi, M., & Husseini, S. M. M. (2016). Integrated strategic and tactical supply chain planning with price-sensitive demands. Annals of Operations Research, 242(2), 423–456.

    Google Scholar 

  16. FrotaNeto, J. Q., Bloemhof-Ruwaard, J. M., van Nunen, J. A. E. E., & van Heck, E. (2008). Designing and evaluating sustainable logistics networks. International Journal of Production Economics, 111(2), 195–208.

    Google Scholar 

  17. Govindan, K., & Sivakumar, R. (2016). Green supplier selection and order allocation in a low-carbon paper industry: Integrated multi-criteria heterogeneous decision-making and multi-objective linear programming approaches. Annals of Operations Research, 238(1), 1–34.

    Google Scholar 

  18. Iyengar, G. N. (2005). Robust dynamic programming. Mathematics of Operations Research, 30(2), 257–280.

    Google Scholar 

  19. Jamdar, V., Kathalewar, M., Dubey, K. A., & Sabnis, A. (2017). Recycling of PET wastes using electron beam radiations and preparation of polyurethane coatings using recycled material. Progress in Organic Coatings, 107(2017), 54–63.

    Google Scholar 

  20. Kuo, T. C., Chen, H. M., Liu, C. Y., Tu, J. C., & Yeh, T. C. (2014). Applying multi-objective planning in low-carbon product design. International Journal of Precision Engineering and Manufacturing, 15(2), 241–249.

    Google Scholar 

  21. Lalmazloumian, M., Wong, K. Y., Govindan, K., & Kannan, D. (2016). A robust optimization model for agile and build-to-order supply chain planning under uncertainties. Annals of Operations Research, 2, 1–36.

    Google Scholar 

  22. Leung, S. C. H., Tsang, S. O. S., Ng, W. L., & Wu, Y. (2007). A robust optimization model for multi-site production planning problem in an uncertain environment. European Journal of Operational Research, 181(1), 224–238.

    Google Scholar 

  23. Li, X., & Zhu, D. (2011). Object technology software selection: A case study. Annals of Operations Research, 185(1), 5–24.

    Google Scholar 

  24. Mavrotas, G. (2009). Effective implementation of the \(\varepsilon \)-constraint method in multi-objective mathematical programming problems. Applied Mathematics and Computation, 213(2), 455–465.

    Google Scholar 

  25. Mulvey, J. M., Vanderbei, R., & Zenios, S. (1995). Robust optimization of large-scale systems. Operations Research, 43(2), 264–280.

    Google Scholar 

  26. Nouira, I., Hammami, R., Frein, Y., et al. (2016). Design of forward supply chains: Impact of a carbon emissions-sensitive demand. International Journal of Production Economics, 173, 80–98.

    Google Scholar 

  27. Pan, S., Ballot, E., & Fontane, F. (2013). The reduction of greenhouse gas emissions from freight transport by pooling supply chains. International Journal of Production Economics, 143(1), 86–94.

    Google Scholar 

  28. Petridis, K. (2015). Optimal design of multi-echelon supply chain networks under normally distributed demand. Annals of Operations Research, 227(1), 63–91.

    Google Scholar 

  29. Pishvaee, M. S., Rabbani, M., & Torabi, S. A. (2011). A robust optimization approach to closed-loop supply chain network design under uncertainty. Applied Mathematical Modelling, 35(2), 637–649.

    Google Scholar 

  30. Ramudhin, A., Chaabane, A., & Paquet, M. (2010). Carbon market sensitive sustainable supply chain network design. International Journal of Management Science and Engineering Management, 5(1), 30–38.

    Google Scholar 

  31. Ravi, S., Sudheer, G., & Brian, T. (2009). Product design and supply chain coordination under extended producer responsibility. Production and Operations Management, 18(3), 259–277.

    Google Scholar 

  32. Sabri, E. H., & Beamon, B. M. (2000). A multi-objective approach to simultaneous strategic and operational planning in supply chain design. Omega, 28(5), 581–598.

    Google Scholar 

  33. Soyster, A. (1973). Convex programming with set-inclusive constraints and applications to inexact linear programming. Operations Research, 21(5), 1154–1157.

    Google Scholar 

  34. Steele, L. W. (1989). Managing technology: The strategic view. New York: McGraw-Hill.

    Google Scholar 

  35. Stranlund, J. K. (2007). The regulatory choice of noncompliance in emissions trading programs. Environmental and Resource Economics, 38(1), 99–117.

    Google Scholar 

  36. Taki, P., Barzinpour, F., & Teimoury, E. (2016). Risk-pooling strategy, lead time, delivery reliability and inventory control decisions in a stochastic multi-objective supply chain network design. Annals of Operations Research, 244(2), 1–28.

    Google Scholar 

  37. Yu, C. S., & Li, H. L. (2000). A robust optimization model for stochastic logistics problems. International Journal of Production Economics, 64(1–3), 385–397.

    Google Scholar 

Download references

Acknowledgements

This work described in this paper was partially supported by National Scientific Foundation of China (Project No. 71671152 Grant No. 61750110536),Guangdong Natural Science Foundation fund (2015A030313782), SUSTech Startup fund (Y01236215), National Scientific Foundation of Fujian Province (Project No. 2015J01288), the Program for New Century Excellent Talents in University (NCET-12-0321) andthe Fundamental Research Funds for the Central Universities (No. 20720151004).

Author information

Affiliations

Authors

Corresponding author

Correspondence to Zongwei Luo.

Appendices

Appendix: Related parameters

Other parameters
\(\bar{msc}_{it}\) Maximal supply capacity of new PET chips from supplier i in period t
\(\bar{mmc}_{jt}\) Maximal production capacity of PET bottles at manufacturing center j in period t
\(\bar{mdc}_{kt}\) Maximal distribution capacity of PET bottles at distribution center k in period t
\(\bar{mrc}_{mt}\) Maximal processing capacity of returned PET bottles at recycling center k in period t
\({fs}_{i}\) Fixed cost of selecting supplier i to establish a long-term business
\({fm}_{j}\) Fixed cost of opening manufacturing center j
\({fd}_{k}\) Fixed cost of opening distribution center k
\({fr}_{m}\) Fixed cost of opening recycling center m
\({ps}_{ijt}\) Unit purchase price of new PET chips from supplier i to manufacturing center j in period t
\({pp}_{kt}\) Unit processing cost in distribution center k
\({pr}_{mnt}\) Unit repurchase price of returned PET bottles from market n torecycling center m in period t
\({pt}_{t}\) Unit cost of delivering cargoes (chips or bottles) per unit weight per unit distance in period t
\({pd}_{mt}\) Unit cost of discarding unusable recycling PET chips at recycling center m in period t
\({prr}_{mt}\) Unit cost of regenerating recovery PET chips at recycling center m in period t
\({lsm}_{ij}\) Shortest shipping distances from supplier i to manufacturing center j
\({lmd}_{jk}\) Shortest shipping distances from manufacturing center j to distribution center k
\({ldx}_{kn}\) Shortest shipping distances from distribution center k to market n
\({lxr}_{nm}\) Shortest shipping distances from market n torecycling center m
\({lrm}_{mj}\) Shortest shipping distances from recycling center m to manufacturing center j
\({cn}_{ijt}\) Unit carbon emission of purchasing new PET chips from supplier i to manufacturing center j in period t
\({crr}_{mt}\) Unit carbon emission of regenerating recovery PET chips at recycling center m in period t
\({crd}_{mt}\) Unit carbon emission of discarding unusable recycling PET chips at recycling center m in period t
\({ct}_{t}\) Unit carbon emission of delivering cargoes (chips or bottles) per unit weight per unit distance in period t

Appendix: Related data

See Tables 23456 and  7.

Table 2 Data on purchasing process
Table 3 Data on distribution process
Table 4 Data on recycling process
Table 5 Demand of each market (Unit)
Table 6 Unit purchase price of returned product from market (Yuan/unit)
Table 7 Data about transportation

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Han, S., Jiang, Y., Zhao, L. et al. Weight reduction technology and supply chain network design under carbon emission restriction. Ann Oper Res 290, 567–590 (2020). https://doi.org/10.1007/s10479-017-2696-8

Download citation

Keywords

  • Supply chain networks
  • Weight-reduction technology
  • Product development
  • Carbon emission
  • Sustainability