Skip to main content

Advertisement

SpringerLink
Log in

Robust aggregate production planning in a green supply chain under uncertainty considering reverse logistics: a case study

  • ORIGINAL ARTICLE
  • Published:
The International Journal of Advanced Manufacturing Technology Aims and scope Submit manuscript

Abstract

In response to the environmental problems in the world, organizations need to apply green indicators to gain competitive advantages. Moreover, manufacturers need to fulfill the demands in such a way as to generate a stable plan in the real world. In this paper, a robust optimization approach is developed to solve a multi-site, multi- period, multi-product aggregate production planning problem in a green supply chain considering potential collection and recycling centers under uncertainty. The specific number of collection and recycling centers among potential centers can be constructed to manufacture the second-class products. Some green principles such as waste management, greenhouse gas emissions related to transportation modes, and production methods are embedded in the model. The objective function sets out to minimize total losses of considered supply chain. Demand fluctuations and cost parameters are subject to the uncertainty. A set of discrete scenarios are employed to illustrate the uncertainties. The robust optimization approach is chosen to reduce the impacts of fluctuations of uncertain parameters concerning all possible scenarios in the future. A case study from an Iranian Wood and Paper Industries Company is studied to indicate the practicability of the proposed model. The computational results demonstrate the effectiveness and robustness of the model. The cost analysis is carried out to provide useful managerial insights. The total costs and the profit in collection and recycling system, is also analyzed to indicate its performance.

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

Access this article

Log in via an institution

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. Eltayeb K, Zailani TS, Ramayah T (2011) Green supply chain initiatives among certified companies in Malaysia and environmental sustainability: investigating the outcomes. Resour Conserv Recycl 55:495–506

    Article  Google Scholar 

  2. Mousazadeh M, Torabi SA, Pishvaee MS (2014) Green and Reverse Logistics Management Under Fuzziness. Studies in fuzziness and. Soft Comput 313:607–637

    Google Scholar 

  3. Kumar S, Teichman S, Timpernagel T (2012) A green supply chain is a requirement for profitability. Int J Prod Res 50:1278–1296

    Article  Google Scholar 

  4. Wang F, Lia X, Shi N (2011) A multi-objective optimization for green supply chain network design. Decis Support Syst 51:262–269

    Article  Google Scholar 

  5. Bai C, Sarkis J (2010) Green supplier development: analytical evaluation using rough set theory. J Clean Prod 18:1200–1210

    Article  Google Scholar 

  6. Jiuh-Biing S (2008) Green supply chain management, reverse logistics and nuclear power generation. Transp Res E 44:19–46

    Article  Google Scholar 

  7. Rettab B and B. A. Ben (2008) Green supply chain in Dubai. UAE: Dubai Chamber Centre for Responsible

  8. Duarte S, Cruz-Machado V (2015) Investigating lean and green supply chain linkages through a balanced scorecard framework. International Journal of Management Science and Engineering Management 10(1):20–29

    Article  Google Scholar 

  9. Yeh W-C, Chuang M-C (2011) Using multi-objective genetic algorithm for partner selection in green supply chain problems. Expert Syst Appl 38(4):4244–4253

    Article  Google Scholar 

  10. Ghayebloo S et al (2015) Developing a bi-objective model of the closed-loop supply chain network with green supplier selection and disassembly of products: the impact of parts reliability and product greenness on the recovery network. J Manuf Syst 36:76–86

    Article  Google Scholar 

  11. Fahimnia B et al (2015) Policy insights from a green supply chain optimisation model. Int J Prod Res 53(21):6522–6533

    Article  Google Scholar 

  12. Bhattacharya A, Kumar Dey P, Ho W (2015) Green manufacturing supply chain design and operations decision support. Int J Prod Res 53(21):6339–6343

    Article  Google Scholar 

  13. Vachon S (2007) Green supply chain practices and the selection of environmental technologies. Int J Prod Res 45(18–19):4357–4379

    Article  MATH  Google Scholar 

  14. Darnall N, Jolley GJ, Handfield R (2008) Environmental management systems and green supply chain management: complements for sustainability? Bus Strateg Environ 17(1):30–45

    Article  Google Scholar 

  15. Zhu Q, Sarkis J (2004) Relationships between operational practices and performance among early adopters of green supply chain management practices in Chinese manufacturing enterprises. J Oper Manag 22(3):265–289

    Article  Google Scholar 

  16. Zhu Q et al (2008a) Firm-level correlates of emergent green supply chain management practices in the Chinese context. Omega 36:577–591

    Article  Google Scholar 

  17. Zhu Q, Sarkis J, Lai K-h (2008b) Green supply chain management implications for “closing the loop”. Transp Res E 44:1–8

    Article  Google Scholar 

  18. Zhu Q, Sarkis J, Lai K-h (2008c) Confirmation of a measurement model for green supply chain management practices implementation. Int J Prod Econ 111:261–273

    Article  Google Scholar 

  19. Srivastava SK (2007) Green supply-chain management: a state-of-the-art literature review. Int J Manag Rev 9(1):53–80

    Article  Google Scholar 

  20. Min H, Kim I (2012) Green supply chain research: past, present, and future. Logist Res 4(1–2):39–47

    Article  Google Scholar 

  21. 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 

  22. Blumberge DF (1999) Strategic examination of reverse logistics and repair service requirements, needs, market size, and opportunities. J Bus Logist 20(2):141–159

    Google Scholar 

  23. Álvarez-Gil MJ et al (2007) Reverse logistics, stakeholders’ influence, organizational slack, and managers’ posture. J Bus Res 60(5):463–473

    Article  Google Scholar 

  24. Glenn R et al (2005) Monitoring reverse logistics programs: a roadmap to sustainable development in emerging markets. Multinatl Bus Rev 13(3):41–65

    Article  Google Scholar 

  25. Galvez D et al. (2015) Reverse logistics network design for a biogas plant: an approach based on MILP optimization and Analytical Hierarchical Process (AHP). Journal of Manufacturing Systems

  26. Alshamsi A Diabat A (2015) A reverse logistics network design. Journal of Manufacturing Systems

  27. Mishra N, Kumar V, Chan FTS (2012) A multi-agent architecture for reverse logistics in a green supply chain. Int J Prod Res 50(9):2396–2406

    Article  Google Scholar 

  28. Wang R-C, Fang H-H (2001) Aggregate production planning with multiple objectives in fuzzy environment. Eur J Oper Res 133:521–536

    Article  MATH  Google Scholar 

  29. Gomes da Silva C et al (2006) An interactive decision support system for an aggregate production planning model based on multiple criteria mixed integer linear programming. Omega 34(2):167–177

    Article  MathSciNet  Google Scholar 

  30. Orcun S, Uzsoy R, Kempf KG (2009) An integrated production planning model with load-dependent lead-times and safety stocks. Comput Chem Eng 33(12):2159–2163

    Article  Google Scholar 

  31. Zhang R et al (2012) The activity-based aggregate production planning with capacity expansion in manufacturing systems. Comput Ind Eng 62:491–503

    Article  Google Scholar 

  32. Xue G et al (2011) Integrated production planning with sequence-dependent family setup times. Int J Prod Econ 131(2):674–681

    Article  Google Scholar 

  33. Masud ASM, Hwang CL (2007) An aggregate production planning model and application of three multiple objective decision methods. Int J Prod Res 18(6):741–752

    Article  Google Scholar 

  34. David AG, Goal Programming A (1974) Approach to aggregate planning of production and work force. Manag Sci:1569–1575

  35. Kazemi A, Fazel Zarandi MH, Moattar Husseini SM (2008) A multi-agent system to solve the production–distribution planning problem for a supply chain: a genetic algorithm approach. Int J Adv Manuf Technol 44(1–2):180–193

    Google Scholar 

  36. Lim SJ et al (2005) A simulation approach for production-distribution planning with consideration given to replenishment policies. Int J Adv Manuf Technol 27(5–6):593–603

    Google Scholar 

  37. Nam S (1992) J. And R. Logendran, Aggregate production planning—a survey of mdels and methodologies. Eur J Oper Res 61:255–272

    Article  Google Scholar 

  38. Leung SCH, Wu Y, Lai KK (2006) A stochastic programming approach for multi-site aggregate production planning. J Oper Res Soc 57(2):123–132

    Article  MATH  Google Scholar 

  39. Fung YK, Tang RJ, Wang D (2003) Multiproduct aggregate production planning with fuzzy demands and fuzzy capacities. IEEE Transactions on Systems, Man, and Cybernetics, Part A 33(3):302–313

    Article  Google Scholar 

  40. Mulvey M, Vanderbei JR, Zenios SA (1995) Robust optimization of large-scale systems. Oper Res 43:264–281

    Article  MathSciNet  MATH  Google Scholar 

  41. Leung SCH, Wu Y (2004) A robust optimization model for stochastic aggregate production planning. Prod Plan Control 15(5):502–514

    Article  Google Scholar 

  42. Leung SCH et al (2007) A robust optimization model for multi-site production planning problem in an uncertain environment. Eur J Oper Res 181(1):224–238

    Article  MATH  Google Scholar 

  43. Kazemi Zanjani M, Ait-Kadi D, Nourelfath M (2010) Robust production planning in a manufacturing environment with random yield: a case in sawmill production planning. Eur J Oper Res 201(3):882–891

    Article  MATH  Google Scholar 

  44. Pan F, Nagi R (2010) Robust supply chain design under uncertain demand in agile manufacturing. Comput Oper Res 37(4):668–683

    Article  MATH  Google Scholar 

  45. Niknamfar AH, Niaki STA, Pasandideh SHR (2014) Robust optimization approach for an aggregate production–distribution planning in a three-level supply chain. Int J Adv Manuf Technol 76(1–4):623–634

    Google Scholar 

  46. Mirzapour Al-e-hashem SMJ, Malekly H, Aryanezhad MB (2011b) A multi-objective robust optimization model for multi-product multi-site aggregate production planning in a supply chain under uncertainty. Int J Prod Econ 134(1):28–42

    Article  Google Scholar 

  47. Al-e-Hashem SMJM, Aryanezhad MB, Sadjadi SJ (2011) An efficient algorithm to solve a multi-objective robust aggregate production planning in an uncertain environment. Int J Adv Manuf Technol 58(5–8):782–765

    Google Scholar 

  48. Mirzapour Al-e-hashem SMJ, Baboli A, Sazvar Z (2013) A stochastic aggregate production planning model in a green supply chain: considering flexible lead times, nonlinear purchase and shortage cost functions. Eur J Oper Res 230(1):26–41

    Article  MathSciNet  MATH  Google Scholar 

  49. Yu C-S, Li H-L (2000) A robust optimization model for stochastic logistic problems. Int J Prod Econ 64:385–397

    Article  Google Scholar 

  50. Martin N et al. (2000) Opportunities to improve energy efficiency and reduce greenhouse gas emissions in the U.S. pulp and paper industry

  51. Network for Transport and Environment, (NTM). http://www.ntmcalc.se/index.html.

  52. Benn H Milliband E (2009) Guidance on how to measure and report your greenhouse gas emissions

  53. Rahmani D et al (2013) A robust optimization model for multi-product two-stage capacitated production planning under uncertainty. Appl Math Model 37(20–21):8957–8971

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial Engineering, Iran University of Science and Technology, Tehran, Iran

    Arezoo Entezaminia & Mahdi Heidari

  2. Department of Industrial Engineering, K. N. Toosi University, Tehran, Iran

    Donya Rahmani

Authors
  1. Arezoo Entezaminia
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Mahdi Heidari
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Donya Rahmani
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Arezoo Entezaminia.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Entezaminia, A., Heidari, M. & Rahmani, D. Robust aggregate production planning in a green supply chain under uncertainty considering reverse logistics: a case study. Int J Adv Manuf Technol 90, 1507–1528 (2017). https://doi.org/10.1007/s00170-016-9459-6

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00170-016-9459-6

Keywords

Search

Navigation