Strategic capacity portfolio planning under demand uncertainty and technological change

Abstract

High-tech industries are experiencing accelerated technological change, which causes rapid obsolescence of invested manufacturing equipment. In such environment, the impact of the new technologies must be carefully considered while making decisions involved in resource acquisition or replacement. In this study, we consider a capacity planning problem with the presence of uncertain magnitude and timing of new technologies and stochastic demands. This study targets of determining (1) the most profitable technology portfolio investment, (2) the corresponding resource levels, and (3) the production plan to fully utilize the capacity. The research problem is modeled by Markov decision process (MDP). The objective is to maximize expected profit under demand and technology uncertainties. For solution efficiency, the Intlinprog solver is utilized to find the optimal action at each decision epoch, accompanying with a parallel computing mechanism to relax the computational burden of the MDP model. Through experiments, we demonstrate the effectiveness of the proposed model, and highlight the importance of considering uncertain factors.

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

References

  1. Badri H, Bashiri M, Hejazi TH (2013) Integrated strategic and tactical planning in a supply chain network design with a heuristic solution method. Comput Oper Res 40(4):1143–1154

    MathSciNet  Article  Google Scholar 

  2. Bashyam TCA (1996) Competitive capacity expansion under demand uncertainty. Eur J Oper Res 95(1):89–114

    Article  Google Scholar 

  3. Bean JC, Lohmann JR, Smith RL (1984) A dynamic infinite horizon replacement economy decision model. Eng Econ 30(2):99–120

    Article  Google Scholar 

  4. Chen TL, Lin JT, Wu CH (2014) Coordinated capacity planning in two-stage thin-film-transistor liquid-crystal-display (TFT-LCD) production networks. Omega Int J Manag Sci 42(1):141–156

    Article  Google Scholar 

  5. Chien C-F, Wu C-H, Chiang Y-S (2012) Coordinated capacity migration and expansion planning for semiconductor manufacturing under demand uncertainties. Int J Prod Econ 135(2):860–869

    Article  Google Scholar 

  6. Hartman JC, Rogers J (2006) Dynamic programming approaches for equipment replacement problems with continuous and discontinuous technological change. IMA J Manag Math 17(2):143–158

    MathSciNet  Article  Google Scholar 

  7. Hopp WJ, Nair SK (1994) Markovian deterioration and technological change. IIE Trans 26(6):74–82. https://doi.org/10.1080/07408179408966640

    Article  Google Scholar 

  8. ITRS (2013) International technology roadmap for semiconductors (ITRS): executive summary by the International Roadmap Committee (IRC). Retrieved 05 Sept 2018, from http://www.itrs.net/Links/2013ITRS/Summaryx2013.htm

  9. Lin JT, Chen T-L, Chu H-C (2014) A stochastic dynamic programming approach for multi-site capacity planning in TFT-LCD manufacturing under demand uncertainty. Int J Prod Econ 148:21–36

    Article  Google Scholar 

  10. Martínez-Costa C, Mas-Machuca M, Benedito E, Corominas A (2014) A review of mathematical programming models for strategic capacity planning in manufacturing. Int J Prod Econ 153:66–85

    Article  Google Scholar 

  11. MatWorks (2018) Mixed-integer linear programming algorithms. Retrieved 05 Sept 2018, from https://ww2.mathworks.cn/help/optim/ug/mixed-integer-linear-programming-algorithms.html#btwyo05

  12. Mieghem JAV (2003) Commissioned paper: capacity management, investment, and hedging: review and recent developments. Manuf Serv Oper Manag 5(4):269–302

    Article  Google Scholar 

  13. Moore GE (2006) Cramming more components onto integrated circuits, reprinted from Electronics, volume 38, number 8, April 19, 1965, pp.114 ff. IEEE Solid-State Circuits Soc Newsl 11(5):33–35. https://doi.org/10.1109/n-ssc.2006.4785860

    Article  Google Scholar 

  14. Nair S, Hopp W (1992) A model for equipment replacement due to technological obsolescence. Eur J Oper Res 63(2):207–221

    Article  Google Scholar 

  15. Puterman ML (1994) Markov Decision Processes: Discrete Stochastic Dynamic Programming. John Wiley & Sons, New York

    Google Scholar 

  16. Rajagopalan S, Singh MR, Morton TE (1998) Capacity expansion and replacement in growing markets with uncertain technological breakthroughs. Manag Sci 44(1):12–30. https://doi.org/10.1287/mnsc.44.1.12

    Article  MATH  Google Scholar 

  17. Sethi S, Chand S (1979) Planning horizon procedures for machine replacement models. Manag Sci 25(2):140–151

    MathSciNet  Article  Google Scholar 

  18. Shapiro A (2001) Monte Carlo simulation approach to stochastic programming. Paper presented at the proceeding of the 2001 winter simulation conference (cat. no. 01CH37304)

  19. Wang K-J, Wang S-M (2013) Simultaneous resource portfolio planning under demand and technology uncertainty in the semiconductor testing industry. Robot Comput Integr Manuf 29(5):278–287

    Article  Google Scholar 

  20. Wang KJ, Wee HM, Gao SF, Chung S-L (2005) Production and inventory control with chaotic demands. Omega Int J Manag Sci 33(2):97–106

    Article  Google Scholar 

  21. Wang KJ, Wang SM, Yang SJ (2007a) A resource portfolio model for equipment investment and allocation of semiconductor testing industry. Eur J Oper Res 179(2):390–403

    Article  Google Scholar 

  22. Wang S-M, Chen J, Wang KJ (2007b) Resource portfolio planning of make-to-stock products using a constraint programming based genetic algorithm. Omega Int J Manag Sci 35(2):237–246

    Article  Google Scholar 

  23. Wu C-H, Chuang Y-T (2010) An innovative approach for strategic capacity portfolio planning under uncertainties. Eur J Oper Res 207(2):1002–1013

    MathSciNet  Article  Google Scholar 

Download references

Acknowledgements

This work is partially supported by Ministry of Science & Technology of the Republic of China (Taiwan) under the Grant # MOST 105-2221-E-011-106-MY2, 107-2221-E-011-101-MY3, and 107-2811-E-011-515.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Kung-Jeng Wang.

Additional information

Publisher's Note

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

Appendix: Input data

Appendix: Input data

Problem size

  • Number of planning periods: 5

  • Number of technologies: 3

  • Number of product types: 2

  • Number of product demand states: 3

Technology-related parameters

See Tables 5, 6 and 7.

Table 5 Productivity
Table 6 Purchasing cost and salvage value
Table 7 Uniform transition probabilities of technological changes

Product-related parameters

See Tables 8, 9 and 10.

Table 8 Unit profit
Table 9 Value of demand states
Table 10 Uniform transition probabilities of demand states

Others

  • Upper bound of total number of machines can be acquired: 10

  • Lower bound of total number of machines can be acquired: 0

  • Initial state in period 0: [1, 2, 2,5,0, 0].

  • Discount factor: 0.98.

  • Number of working hours per period: 1800 (hours).

  • Target utilization of each machine: 0.8.

Different types of transition probability (used in Sect. 5.3)

Transition probabilities of technological changes

See Tables 11 and 12.

Table 11 High-correlated
Table 12 Low-correlated

Transition probabilities of demand

See Tables 13 and 14.

Table 13 High-correlated
Table 14 Low-correlated

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Nguyen, P.H., Wang, K. Strategic capacity portfolio planning under demand uncertainty and technological change. Flex Serv Manuf J 31, 926–944 (2019). https://doi.org/10.1007/s10696-019-09335-w

Download citation

Keywords

  • Capacity planning
  • Markov modeling
  • Stochastic models
  • Technology management