A fuzzy-random optimization approach using fuzzy measure and fuzzy integral for emergency risk coordination of supply chain

In this paper, a novel fuzzy-random multi-objective programming model is proposed for the emergency coordination of supply chain outsourcing risk in presence of both fuzzy uncertainty and random uncertainty. It is based on fuzzy measure and fuzzy integral theory, which treat the interdependence among the customers or suppliers of supply chain risk management network Fuzzy set theory is employed to deal with fuzzy information while utility theory is used to handle stochastic information data. At the same time, the proposed model considers the stability and the robustness of the whole supply chain risk coordinating network, so the objective of achieving minimum variance is considered into multi-objective optimization model. An algorithm is presented to solve the designed model for a three stage supply chain example. Based on statistical analysis techniques, the experimental results show that procurement behavior and a suggested analysis of risk averse by computation, which involves the less preference of a more risk-averse customer to ordered quantity under fuzzy and stochastic uncertainty, and also indicate the coordinating decision making program is steady and robust to the little perturbation of fuzzy demand in supply chain network.