Carbon footprint for designing reverse logistics network with hybrid manufacturing-remanufacturing systems

Abstract

This article proposes one further step toward the design of Sustainable Manufacturing Enterprise. This article presents an integrated approach for designing a reverse logistics network by minimizing the carbon emissions and the transportation distances between different candidate centers while considering several system design and operational issues of a Hybrid Manufacturing-Remanufacturing System operating within the above-mentioned reverse logistics network. Accordingly, the article attempts to integrate various sustainability aspects indoctrinated in the Sustainable Manufacturing philosophy. In view of this, a mixed integer programming model for designing a reverse logistics network is developed. The model considers the carbon foot print, facility location, and the material flow aspects of the reverse logistics network; in which a hybrid manufacturing-remanufacturing system is integrated. A detailed discussion of a numerical example is presented to illustrate the proposed model. The model has potential applications for supply chain managers designing a reverse logistics networks as well as for production managers at the operations level.

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

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

References

  1. 1.

    Agrawal S, Singh RK, Murtaza Q (2015) A literature review and perspectives in reverse logistics. Resources Conservation and Recycling 97:76–92

    Article  Google Scholar 

  2. 2.

    Aljuneidi T, Bulgak AA (2016) A mathematical model for designing reconfigurable cellular hybrid manufacturing-remanufacturing systems. Int J Adv Manuf Technol 87(5–8):1585–1596

    Article  Google Scholar 

  3. 3.

    Aljuneidi T, Bulgak AA (2017) Designing a cellular manufacturing system featuring remanufacturing, recycling, and disposal options: A mathematical modeling approach. CIRP J Manuf Sci Technol 19:25–35

    Article  Google Scholar 

  4. 4.

    Aljuneidi T, Bulgak A (2015) Dynamic cellular remanufacturing system (DCRS) design, World Academy of Science, Engineering and Technology, international science index 101, international journal of mechanical, aerospace, industrial. Mechatronic and Manufacturing Engineering 9(5):836–840

    Google Scholar 

  5. 5.

    Alshamsi A, Diabat A (2015) A reverse logistics network design. Journal of Manufacturing Systems 37:589–598

    Article  Google Scholar 

  6. 6.

    Amin SH, Zhang GQ (2012) A proposed mathematical model for closed-loop network configuration based on product life cycle. Int J Adv Manuf Technol 58(5–8):791–801

    Article  Google Scholar 

  7. 7.

    Bazan E, Jaber MY, El Saadany AMA (2015) Carbon emissions and energy effects on manufacturing-remanufacturing inventory models. Computers & Industrial Engineering 88:307–316

    Article  Google Scholar 

  8. 8.

    Chen J, Chen J (2017) Supply chain carbon footprinting and responsibility allocation under emission regulations. J Environ Manag 188:255–267

    Article  Google Scholar 

  9. 9.

    Chen M, Abrishami P (2014) A mathematical model for production planning in hybrid manufacturing-remanufacturing systems. Int J Adv Manuf Technol 71(5–8):1187–1196

    Article  Google Scholar 

  10. 10.

    Chen YT, Chan FTS, Chung SH (2015) An integrated closed-loop supply chain model with location allocation problem and product recycling decisions. Int J Prod Res 53(10):3120–3140

    Article  Google Scholar 

  11. 11.

    Corum A, Vayvay O, Bayraktar E (2014) The impact of remanufacturing on total inventory cost and order variance. Journal of Cleaner Production 85:442–452

    Article  Google Scholar 

  12. 12.

    Dekker R, Bloemhof J, Mallidis I (2012) Operations research for green logistics - an overview of aspects, issues, contributions and challenges. Eur J Oper Res 219(3):671–679

    Article  Google Scholar 

  13. 13.

    Demirel NO, Gokcen H (2008) A mixed integer programming model for remanufacturing in reverse logistics environment. Int J Adv Manuf Technol 39(11–12):1197–1206

    Article  Google Scholar 

  14. 14.

    Dev NK, Shankar R, Choudhary A (2017) Strategic design for inventory and production planning in closed-loop hybrid systems. Int J Prod Econ 183:345–353

    Article  Google Scholar 

  15. 15.

    Diabat A, Kannan D, Kaliyan M, Svetinovic D (2013) An optimization model for product returns using genetic algorithms and artificial immune system. Resources. Conservation and Recycling 74:156–169

    Article  Google Scholar 

  16. 16.

    Du SF, Tang WZ, Song M (2016) Low-carbon production with low-carbon premium in cap-and-trade regulation. Journal of Cleaner Production 134:652–662

    Article  Google Scholar 

  17. 17.

    Eskandarpour M, Dejax P, Péton O (2017) A large neighborhood search heuristic for supply chain network design. Comput Oper Res 80:23–37

    MathSciNet  MATH  Article  Google Scholar 

  18. 18.

    Garbie IH (2013) DFSME: design for sustainable manufacturing enterprises (an economic viewpoint). Int J Prod Res 51:479–503

    Article  Google Scholar 

  19. 19.

    Govindan K, Soleimani H, Kannan D (2015) Reverse logistics and closed-loop supply chain: A comprehensive review to explore the future. Eur J Oper Res 240(3):603–626

    MathSciNet  MATH  Article  Google Scholar 

  20. 20.

    Gunasekaran A, Spalanzani A (2012) Sustainability of manufacturing and services: investigations for research and applications. Int J Prod Econ 140:35–47

    Article  Google Scholar 

  21. 21.

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

    Article  Google Scholar 

  22. 22.

    Haapala KR, Zhao F, Camelio J, Sutherland JW, Skerlos SJ, Dornfeld DA, Jawahir IS, Clarens AF, Rickli JL (2013) A review of engineering research in sustainable manufacturing. Journal of Manufacturing Science and Engineering-Transactions of the Asme 135:16

    Article  Google Scholar 

  23. 23.

    Hao H, Qiao Q, Liu Z, Zhao F (2017) Impact of recycling on energy consumption and greenhouse gas emissions from electric vehicle production: the China 2025 case. Resour Conserv Recycl 122:114–125

    Article  Google Scholar 

  24. 24.

    Hasanov P, Jaber MY, Zolfaghari S (2012) Production, remanufacturing and waste disposal models for the cases of pure and partial backordering. Appl Math Model 36(11):5249–5261

    MathSciNet  MATH  Article  Google Scholar 

  25. 25.

    He B, Wang J, Huang S, Wang Y (2015) Low-carbon product design for product life cycle. J Eng Des 26(10–12):321–339

    Article  Google Scholar 

  26. 26.

    Inderfurth K (2004) Optimal policies in hybrid manufacturing/remanufacturing systems with product substitution. Int J Prod Econ 90(3):325–343

    Article  Google Scholar 

  27. 27.

    “Investment Dictionary". Carbon Credit Definition. Investopedia Inc.

  28. 28.

    Jayal AD, Badurdeen F, Dillon OW Jr, Jawahir IS (2010) Sustainable manufacturing: modeling and optimization challenges at the product, process and system levels. CIRP J Manuf Sci Technol 2(3):144–152

    Article  Google Scholar 

  29. 29.

    Jindal A, Sangwan KS, Saxena S (2015) Network design and optimization for multi-product, multi-time, multi-echelon closed-loop supply chain under uncertainty. 22nd Cirp Conference on Life Cycle Engineering 29:656–661

    Google Scholar 

  30. 30.

    John ST, Sridharan R (2015) Modelling and analysis of network design for a reverse supply chain. Journal of Manufacturing Technology Management 26(6):853–867

    Article  Google Scholar 

  31. 31.

    Kannan D, Diabat A, Alrefaei M, Govindan K, Yong G (2012) A carbon footprint based reverse logistics network design model. Resources Conservation and Recycling 67:75–79

    Article  Google Scholar 

  32. 32.

    Kannan G (2009) Fuzzy approach for the selection of third party reverse logistics provider. Asia Pac J Mark Logist 21(3):397–416

    Article  Google Scholar 

  33. 33.

    Kim E, Saghafian S, Van Oyen MP (2013) Joint control of production, remanufacturing, and disposal activities in a hybrid manufacturing-remanufacturing system. Eur J Oper Res 231(2):337–348

    MathSciNet  MATH  Article  Google Scholar 

  34. 34.

    Lee JE, Gen M, Rhee KG (2009) Network model and optimization of reverse logistics by hybrid genetic algorithm. Comput Ind Eng 56(3):951–964

    Article  Google Scholar 

  35. 35.

    Li HC (2015) Optimal delivery strategies considering carbon emissions, time-dependent demands and demand-supply interactions. Eur J Oper Res 241(3):739–748

    MATH  Article  Google Scholar 

  36. 36.

    Lund R. (1983) Remanufacturing: the experience of the United States and implications for developing countries. CPA/83-17, the World Bank, Washington, D.C.

  37. 37.

    Mitra S (2016) Optimal pricing and core acquisition strategy for a hybrid manufacturing/remanufacturing system. Int J Prod Res 54(5):1285–1302

    Article  Google Scholar 

  38. 38.

    Mutha A, Pokharel S (2009) Strategic network design for reverse logistics and remanufacturing using new and old product modules. Comput Ind Eng 56(1):334–346

    Article  Google Scholar 

  39. 39.

    Ozceylan E, Paksoy T (2013a) A mixed integer programming model for a closed-loop supply-chain network. Int J Prod Res 51(3):718–734

    Article  Google Scholar 

  40. 40.

    Parkinson HJ, Thompson G (2003) Analysis and taxonomy of remanufacturing industry practice. Proceedings of the Institution of Mechanical Engineers Part E-Journal of Process Mechanical Engineering 217(E3):243–256

    Article  Google Scholar 

  41. 41.

    Pedram A, Yusoff NB, Udoncy OE, Mahat AB, Pedram P, Babalola A (2017) Integrated forward and reverse supply chain: A tire case study. Waste Manag 60:460–470

    Article  Google Scholar 

  42. 42.

    Pishvaee MS, Kianfar K, Karimi B (2010) Reverse logistics network design using simulated annealing. Int J Adv Manuf Technol 47(1–4):269–281

    Article  Google Scholar 

  43. 43.

    Plassmann K, Norton A, Attarzadeh N, Jensen MP, Breton P, Edwards-Jones G (2010) Methodological complexities of product carbon Footprinting: A sensitivity analysis of key variables in a developing country. Environ Sci Policy 13:393–404

    Article  Google Scholar 

  44. 44.

    Polotski V, Kenne JP, Gharbi A (2015) Optimal production scheduling for hybrid manufacturing-remanufacturing systems with setups. Journal of Manufacturing Systems 37:703–714

    MATH  Article  Google Scholar 

  45. 45.

    Polotski V, Kenne J, Gharbi A (2017) Production and setup policy optimization for hybrid manufacturing–remanufacturing systems. Int J Prod Econ 183:322–333

    Article  Google Scholar 

  46. 46.

    Rogers DS, Tibben-Lembke R (1999) Going backwards: reverse logistics trends and practices. Reno, NV, Reverse Logistics Executive Council

    Google Scholar 

  47. 47.

    Salema MIG, Barbosa-Povoa AP, Novais AQ (2007) An optimization model for the design of a capacitated multi-product reverse logistics network with uncertainty. Eur J Oper Res 179(3):1063–1077

    MATH  Article  Google Scholar 

  48. 48.

    Shaw K, Irfan M, Shankar R, Yadav SS (2016) Low carbon chance constrained supply chain network design problem: a benders decomposition based approach. Computers & Industrial Engineering 98:483–497

    Article  Google Scholar 

  49. 49.

    Soysal M, Bloemhof-Ruwaard JM, van der Vorst J (2014) Modelling food logistics networks with emission considerations: the case of an international beef supply chain. International Journal of Production Economics 152:57–70

    Article  Google Scholar 

  50. 50.

    Su C, Xu AJ (2014) Buffer allocation for hybrid manufacturing/remanufacturing system considering quality grading. Int J Prod Res 52(5):1269–1284

    Article  Google Scholar 

  51. 51.

    Sundin E. (2004) Product and process Design for Successful Remanufacturing, Linköping studies in science and technology, dissertation no. 906, Department of Mechanical Engineering, Linköping University, SE-581 83 Linköping, Sweden

  52. 52.

    Sundin E, Björkman M, Eklund M, Eklund J, Engkvist I-L (2011) Improving the layout of recycling centres by use of lean production principles. Waste Management, Issue 31:1121–1132

    Article  Google Scholar 

  53. 53.

    Supply Chain Management: Strategy, Planning, and Operation, 6th edition, ISBN 978–0–13-380020-3, by Sunil Chopra and Peter Meindl, published by Pearson Education © 2016

  54. 54.

    Tao ZG, Guang ZY, Hao S, Song HJ, Xin DG (2015) Multi-period closed-loop supply chain network equilibrium with carbon emission constraints. Resources Conservation and Recycling 104:354–365

    Article  Google Scholar 

  55. 55.

    Tonanont, A., Yimsiri, S., Jitpitaklert, W., & Rogers, K.J. (2008). Performance evaluation in reverse logistics with data envelopment analysis. In: proceedings of the 2008 industrial engineering research conference (pp. 764–769)

  56. 56.

    Vercraene S, Gayon JP, Flapper SD (2014) Coordination of manufacturing, remanufacturing and returns acceptance in hybrid manufacturing/remanufacturing systems. International Journal of Production Economics:148, 62–170

  57. 57.

    Wang J, Zhao J, Wang X (2011) Optimum policy in hybrid manufacturing/remanufacturing system. In: Optimum policy in hybrid manufacturing/remanufacturing

    Google Scholar 

  58. 58.

    Wiedmann T, Minx J (2008) A definition of 'Carbon Footprint'. In: C. C. Pertsova, ecological economics research trends: chapter 1. Nova Science Publishers, Hauppauge NY, USA, pp 1–11

    Google Scholar 

  59. 59.

    Zanoni S, Ferretti I, Tang O (2006) Cost performance and bullwhip effect in a hybrid manufacturing and remanufacturing system with different control policies. Int J Prod Res 44(18–19):3847–3862

    MATH  Article  Google Scholar 

  60. 60.

    Zhang GT, Sun H, Hu JS, Dai GX (2014) The closed-loop supply chain network equilibrium with products lifetime and carbon emission constraints in multiperiod planning horizon. Discret Dyn Nat Soc 16

  61. 61.

    Zhao, P. X., Liu, B., Xu, L. L. & Wan, D. 2013. Location optimization of multidistribution centers based on low-carbon constraints. Discrete Dynamics in Nature and Society,6

Download references

Acknowledgments

This research was in part supported by grants from the Natural Sciences and Engineering Research Council of Canada (NSERC) and funds from the Faculty of Engineering and Computer Science (ENCS) of Concordia University.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Akif Asil Bulgak.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1

Appendix 1

Tables 2, 3, 4 and 5 present the example input data. Table 2 shows the setup cost for each potential center and the equivalent CO2 to be emitted by each potential center. Table 3 gives the capacity for each manufacturing and the demand for each component type. Table 4 presents the capacity of the disposal and recycling centers and the number of components contained in a product. Distances between each and every pair of centers are shown in Table 5. Percentage rates of returned products are as follows: M1 = 0.3 and M2 = 0.5. CO2. Transportation emissions factor per unit of returned product/component in g/km is 0.01, and the cost of carbon credits in $ per ton CO2 is 10, and the legal limit of the CO2 quantity can be emitted each year is 5000. Transportation cost for components 1, 2, and 3 are 2, 3, and 4 dollars per component respectively, while transportation cost for products 1 and 2 are 8 and 9 dollars per product respectively.

Table 2 Setup cost and CO2 equivalent for each center
Table 3 Demand and Manufacturing facility capacity in terms of components
Table 4 Disposal and recycling centers capacity and number of components contained in product
Table 5 Distances between disassembly, manufacturing, disposal, recycling, and collection centers

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Aljuneidi, T., Bulgak, A.A. Carbon footprint for designing reverse logistics network with hybrid manufacturing-remanufacturing systems. Jnl Remanufactur 10, 107–126 (2020). https://doi.org/10.1007/s13243-019-00076-5

Download citation

Keywords

  • Reverse logistics
  • Facility location
  • Sustainable supply chain
  • Sustainable manufacturing
  • Carbon footprints
  • Hybrid manufacturing-remanufacturing systems