On the Time-consistent Stochastic Dominance Risk Averse Measure for Tactical Supply Chain Planning under Uncertainty

Laureano F. Escudero, Juan Francisco Monge, Dolores Romero Morales

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In this work a modeling framework and a solution approach have been presented for a multi-period stochastic mixed 0–1 problem arising in tactical supply chain planning (TSCP). A multistage scenario tree based scheme is used to represent the parameters’ uncertainty and develop the related Deterministic Equivalent Model. A cost risk reduction is performed by using a new time-consistent risk averse measure. Given the dimensions of this problem in real-life applications, a decomposition approach is proposed. It is based on stochastic dynamic programming (SDP). The computational experience is twofold, a comparison is performed between the plain use of a current state-of-the-art mixed integer optimization solver and the proposed SDP decomposition approach considering the risk neutral version of the model as the subject for the benchmarking. The add-value of the new risk averse strategy is confirmed by the computational results that are obtained using SDP for both versions of the TSCP model, namely, risk neutral and risk averse.
Original languageEnglish
JournalComputers & Operations Research
Pages (from-to)270-286
Number of pages17
Publication statusPublished - Dec 2018


  • Tactical supply chain planning
  • Nonlinear separable objective function
  • Multistage stochastic integer optimization
  • Risk management
  • Time-consistency
  • Stochastic nested decomposition

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