Strategic capacity portfolio planning under demand uncertainty and technological change


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.

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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.

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Correspondence to Kung-Jeng Wang.

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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


  • 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

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Nguyen, P.H., Wang, K. Strategic capacity portfolio planning under demand uncertainty and technological change. Flex Serv Manuf J 31, 926–944 (2019).

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