Pollution risk combination optimization control method for drinking water’s intelligent cold chain supply chain

Abstract

In the process of selecting environmental pollution risk factors in the supply chain, aiming at the problems of more pollution risk, large amount of data, and high time and cost of combination optimization control in the intelligent cold chain supply chain of drinking water, a combination optimization control method of pollution risk in the intelligent cold chain supply chain of drinking water is proposed. AHP-OWA operator is selected to screen the risk factors of pollution in intelligent drinking water cold chain supply chain. Conditional value at risk (CVAR) is used to build the pollution risk portfolio optimization control model of intelligent drinking water cold chain supply chain. The risk portfolio optimization control model is solved by genetic algorithm, and the optimal risk portfolio optimization control scheme is obtained. The simulation results show that this method can improve the accuracy of risk portfolio optimization control, reduce the time, and cost of risk portfolio optimization control, the maximum is only 2268.64€, and the control accuracy can reach 98.69%.