Flexible capacity strategy with multiple market periods under demand uncertainty and investment constraint

Liu Yang, Chi To Ng

Research output: Journal article publicationJournal articleAcademic researchpeer-review

19 Citations (Scopus)


We establish a flexible capacity strategy model with multiple market periods under demand uncertainty and investment constraints. In the model, a firm makes its capacity decision under a financial budget constraint at the beginning of the planning horizon which embraces n market periods. In each market period, the firm goes through three decision-making stages: the safety production stage, the additional production stage and the optimal sales stage. We formulate the problem and obtain the optimal capacity, the optimal safety production, the optimal additional production and the optimal sales of each market period under different situations. We find that there are two thresholds for the unit capacity cost. When the capacity cost is very low, the optimal capacity is determined by its financial budget; when the capacity cost is very high, the firm keeps its optimal capacity at its safety production level; and when the cost is in between of the two thresholds, the optimal capacity is determined by the capacity cost, the number of market periods and the unit cost of additional production. Further, we explore the endogenous safety production level. We verify the conditions under which the firm has different optimal safety production levels. Finally, we prove that the firm can benefit from the investment only when the designed planning horizon is longer than a threshold. Moreover, we also derive the formulae for the above three thresholds.
Original languageEnglish
Pages (from-to)511-521
Number of pages11
JournalEuropean Journal of Operational Research
Issue number2
Publication statusPublished - 16 Jul 2014


  • Capacity constraints
  • Demand uncertainty
  • Flexible capacity strategy
  • Multiple market periods

ASJC Scopus subject areas

  • Modelling and Simulation
  • Management Science and Operations Research
  • Information Systems and Management

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