On the time-consistent stochastic dominance risk averse measure for tactical supply chain planning under uncertainty

• Published in: Computers & operations research Vol. 100; pp. 270 - 286
• Main Authors: Escudero, Laureano F., Monge, Juan Francisco, Morales, Dolores Romero
• Format: Journal Article
• Language: English
• Published: New York Elsevier Ltd 01.12.2018; Pergamon Press Inc
• Subjects: Computation; Decomposition; Dynamic programming; Integer programming; Mixed integer; Multistage stochastic integer optimization; Nonlinear separable objective function; Operations research; Parameter uncertainty; Risk; Risk aversion; Risk management; State of the art; Stochastic nested decomposition; Studies; Supply chains; Tactical supply chain planning; Time-consistency; Uncertainty; Stochastic nested decomposition; Multistage stochastic integer optimization; Time-consistency; Tactical supply chain planning; Risk management; Nonlinear separable objective function
• ISSN: 0305-0548; 1873-765X; 1873-765X; 0305-0548
• DOI: 10.1016/j.cor.2017.07.011

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.