Skip to main content

Advertisement

SpringerLink
Log in

Scenario-based two-stage stochastic programming for a Hybrid Manufacturing-Remanufacturing System with the uncertainty of returns, quality and demand

  • Published:
Sādhanā Aims and scope Submit manuscript

Abstract

This paper addresses the optimization of make-to-order hybrid manufacturing - remanufacturing system to make capacity and inventory decisions jointly along with production decisions. The proposed system considers a common production facility and the same assembly/disassembly line to perform manufacturing and remanufacturing operations simultaneously. The current study takes into account an environment where new and remanufactured (reman) products competing to each other that is, the common demand stream for both products but different selling prices. Furthermore, the relative capacity consumed by remanufacturing over the manufacturing is explained in two ways, namely less capacity intensive case and more capacity intensive case. Differently, from previous studies, we consider a scenario with a discounted selling price for reman products, shortage penalty costs, lost sales, disposal and uncertainty in demand, amount and yield of returns. Hence, to handle those uncertainties, a scenario-based stochastic programming model in a two-stage setting is presented. In the first stage, the raw material inventory and production capacity levels are planned and in the second stage, the production, inventory and disposal decisions are determined by balancing overage and underage costs. The results indicate that net values associated with new and reman products can be decisive in choosing either manufacturing or remanufacturing.

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

Figure 1
Figure 2
Figure 3
Figure 4

Similar content being viewed by others

Abbreviations

M:

Number of scenarios

α :

Relative capacity required for remanufacturing over manufacturing

γ:

Relative capacity required for manufacturing over remanufacturing

C Z :

Unit capacity investment cost ($)

C h :

Unit inventory holding cost ($)

C w :

Unit disposal cost ($)

C an :

Unit raw materials purchasing cost per unit ($)

C ar :

Unit returns acquiring cost per unit ($)

C n :

Unit processing cost of new product ($)

C r :

Unit processing cost of reman product ($)

C sp :

Shortage penalty cost for new product per unit ($)

ν n :

Net value associated with the new product ($)

ν r :

Net value associated with reman product ($)

p n :

Unit selling price of new product ($)

p r :

Unit selling price of reman product ($)

p m :

Probability associated with each scenario

d :

Product demand (units)

Q r :

Core returns inventory level (units)

y r :

Yield of core returns

Q n :

Raw material inventory level (units)

Z :

Production capacity level (units)

q n :

Optimal manufacturing quantity (units)

q r :

Optimal remanufacturing quantity (units

References

  1. Tsao Y C., Linh V T., Lu J C and Yu V. 2018 A supply chain network with product remanufacturing and carbon emission considerations: a two-phase design. J. Intell. Manuf. 29: 693–705

    Article  Google Scholar 

  2. Kumar A, Chinnam R B and Murat A 2017 Hazard rate models for core return modeling in auto parts remanufacturing. Int. J. Prod. Econ. 183: 354–361

    Article  Google Scholar 

  3. Reddy K N, Kumar A, Sarkis J and Tiwari M K 2019 Effect of carbon tax on reverse logistics network design. Comput. Ind. Eng., 139:106184

  4. Lu Q, Goh M, Garg M and De Souza R 2014 Remanufacturing in Asia: location choice and outsourcing. Int. J. Logist. Manag. 25:20–34

    Article  Google Scholar 

  5. Subramoniam R, Huisingh D, Chinnam R B and Subramoniam S 2013 Remanufacturing decision-making framework (RDMF): research validation using the analytical hierarchical process. J. Clean. Prod. 40:212–220

    Article  Google Scholar 

  6. Reddy K N, Kumar A and Ballantyne E E F 2019 A three-phase heuristic approach for reverse logistics network design incorporating carbon footprint. Int. J. Prod. Res. 57(19):6090–6114

    Article  Google Scholar 

  7. Choudhary N and Singh N K N K 2011 Remanufacturing in India: approaches, potentials & technical challenges. Int. J. Ind. Eng. Technol. 3(3): 223–227

    Google Scholar 

  8. yan Wang Y and yuan Zhang Y 2017 Remanufacturer’s production strategy with capital constraint and differentiated demand. J. Intell. Manuf., 28(4): 869–882

  9. Guide V D R Jr., Teunter R H and Van Wassenhove L N 2003 Matching demand and supply to maximize profits from remanufacturing. Manuf. Serv. Oper. Manag. 5(4):303–316

    Article  Google Scholar 

  10. Pokharel S and Mutha A 2009 Perspectives in reverse logistics: a review. Resour. Conserv. Recycl. 53:175–182

    Article  Google Scholar 

  11. Van Der Laan E, Salomon M, Dekker R and Van Wassenhove L 1999 Inventory control in hybrid systems with remanufacturing. Manage. Sci. 45(5): 733–747

    Article  Google Scholar 

  12. Takahashi K, Doi Y, Hirotani D and Morikawa K 2014 An adaptive pull strategy for remanufacturing systems. J. Intell. Manuf. 25(4): 629–645

    Article  Google Scholar 

  13. Benjaafar S and ElHafsi M 2006 Production and inventory control of a single product assemble-to-order system with multiple customer classes. Manage. Sci. 52(12): 1896–1912

    Article  Google Scholar 

  14. Zhang F, Guan Z, Zhang L, Cui Y, Yi P and Ullah S 2019 Inventory management for a remanufacture-to-order production with multi-components (parts). J. Intell. Manuf. 30(1): 59–78

    Article  Google Scholar 

  15. Gong X and Chao X 2013 Optimal control policy for capacitated inventory systems with remanufacturing. Oper. Res. 61(3): 603–611

    Article  MathSciNet  Google Scholar 

  16. Bulmuş S C, Zhu S X and Teunter R 2013 Capacity and production decisions under a remanufacturing strategy. Int. J. Prod. Econ. 145(1): 359–370

    Article  Google Scholar 

  17. Van Mieghem J A 2003 Capacity management, investment, and hedging: review and recent developments. Manuf. Serv. Oper. Manag. 5(4): 269–302

    Article  Google Scholar 

  18. Van Mieghem J A and Rudi N 2002 Newsvendor networks: inventory management and capacity investment with discretionary activities. Manuf. Serv. Oper. Manag. 4(4): 313–335

    Article  Google Scholar 

  19. Birge J R and Louveaux F 2011 Introduction to Stochastic Programming. Springer Science+Business Media, New York

  20. El-Sayed M, Afia N and El-Kharbotly A 2010 A stochastic model for forward-reverse logistics network design under risk. Comput. Ind. Eng. 58(3): 423–431

    Article  Google Scholar 

  21. Reddy K N and Kumar A Capacity investment and inventory planning for a hybrid manufacturing–remanufacturing system in the circular economy. Int. J. Prod. Res.,7543

  22. Macedo P B, Alem D, Santos M, Junior M L and Moreno A 2016 Hybrid manufacturing and remanufacturing lot-sizing problem with stochastic demand, return, and setup costs. Int. J. Adv. Manuf. Technol. 82: 1241–1257

    Article  Google Scholar 

  23. Soleimani H, Seyyed-Esfahani M and Shirazi M A 2016 A new multi-criteria scenario-based solution approach for stochastic forward/reverse supply chain network design. Ann. Oper. Res. 242: 399–421

    Article  MathSciNet  Google Scholar 

  24. Kaut M and Wallace S W 2003 Evaluation of scenario-generation methods for stochastic programming. Pacific J. Optim. 3(2): 257–271

    MathSciNet  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Industrial and Systems Engineering, Indian Institute of Technology Kharagpur, Kharagpur, West Bengal, 721302, India

    K Nageswara Reddy & Akhilesh Kumar

  2. Civil Engineering Department, Indian Institute of Technology Bombay, Mumbai, India

    Nagendra R Velaga

Authors
  1. K Nageswara Reddy
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Akhilesh Kumar
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Nagendra R Velaga
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to K Nageswara Reddy.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Reddy, K.N., Kumar, A. & Velaga, N.R. Scenario-based two-stage stochastic programming for a Hybrid Manufacturing-Remanufacturing System with the uncertainty of returns, quality and demand. Sādhanā 46, 59 (2021). https://doi.org/10.1007/s12046-021-01579-3

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1007/s12046-021-01579-3

Keywords

Search

Navigation