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A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints
Management Science (2014)
  • Minjiao Zhang
  • Simge Kucukyavuz
  • Saumya Goel
In this paper, we consider a finite-horizon stochastic mixed-integer program involving dynamic decisions under a constraint on the overall performance or reliability of the system. We formulate this problem as a multistage (dynamic) chance-constrained program, whose deterministic equivalent is a large-scale mixed-integer program. We study the structure of the formulation and develop a branch-and-cut method for its solution. We illustrate the efficacy of the proposed model and method on a dynamic inventory control problem with stochastic demand in which a specific service level must be met over the entire planning horizon. We compare our dynamic model with a static chance-constrained model, a dynamic risk-averse optimization model, a robust optimization model, and a pseudo-dynamic approach and show that significant cost savings can be achieved at high service levels using our model.
  • chance constraints; branch-and-cut; multistage; probabilistic lot sizing; service levels
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Citation Information
Minjiao Zhang, Simge Kucukyavuz and Saumya Goel. "A Branch-and-Cut Method for Dynamic Decision Making Under Joint Chance Constraints" Management Science (2014)
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