On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty (2018). Computers and Operations Research, 100, 270–286.

[:es]Laureano F. Escudero (Rey Juan Carlos University), Juan Francisco Monge (University Miguel Hernández of Elche) and Dolores Romero Morales (Copenhagen Business School).
Abstract. 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 compar- ison 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 compu- tational results that are obtained using SDP for both versions of the TSCP model, namely, risk neutral and risk averse.
Keywords. Tactical supply chain planning; Nonlinear separable objective function; Multistage stochastic integer optimization; Risk management; Time-consistency; Stochastic nested decomposition[:]