A two-stage stochastic model considering conditional risk in a multi-level multi-product supply chain

Abstract

This study introduces a novel two-stage stochastic model of mean value exposed to conditional risk to allocate locations and to calculate the flow of materials and manufactured goods in a multi-level, multi-product supply chain. In this model, distributors and suppliers face potential disruptions and could spend money to prevent them. The suggested model considered several sources of uncertainty, such as transportation costs, final customer demand, and the possibility of disruptions at distribution centers and suppliers. The model used the conditional risk-exposed value and the risk-aversion coefficient to control for the risk caused by significant deviations from expected values. The designed model was transformed into a single-level linear programming model using a Monte Carlo simulation. Finally, the model was implemented through a numerical example, and its sensitivity analysis was conducted. The results of the model show that increasing the risk-aversion coefficient led to a decrease of more than 20% in the objective function across all confidence levels for the test problem, indicating the effectiveness of the proposed two-stage stochastic model in proactively mitigating disruptions.

Keywords

Conditional value-at-risk / Disruption / Monte Carlo simulation / Multi-level supply chain / Supply chain / Two-stage stochastic programming

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