An intuitionistic fuzzy grey-Markov method with application to demand forecasting for emergency supplies during major epidemics

Abstract

PurposeForecasting demand of emergency supplies under major epidemics plays a vital role in improving rescue efficiency. Few studies have combined intuitionistic fuzzy set with grey-Markov method and applied it to the prediction of emergency supplies demand. Therefore, this article aims to establish a novel method for emergency supplies demand forecasting under major epidemics.Design/methodology/approachEmergency supplies demand is correlated with the number of infected cases in need of relief services. First, a novel method called the Intuitionistic Fuzzy TPGM(1,1)-Markov Method (IFTPGMM) is proposed, and it is utilized for the purpose of forecasting the number of people. Then, the prediction of demand for emergency supplies is calculated using a method based on the safety inventory theory, according to numbers predicted by IFTPGMM. Finally, to demonstrate the effectiveness of the proposed method, a comparative analysis is conducted between IFTPGMM and four other methods.FindingsThe results show that IFTPGMM demonstrates superior predictive performance compared to four other methods. The integration of the grey method and intuitionistic fuzzy set has been shown to effectively handle uncertain information and enhance the accuracy of predictions.Originality/valueThe main contribution of this article is to propose a novel method for emergency supplies demand forecasting under major epidemics. The benefits of utilizing the grey method for handling small sample sizes and intuitionistic fuzzy set for handling uncertain information are considered in this proposed method. This method not only enhances existing grey method but also expands the methodologies used for forecasting demand for emergency supplies.Highlights (for review)An intuitionistic fuzzy TPGM(1,1)-Markov method (IFTPGMM) is proposed.The safety inventory theory is combined with IFTPGMM to construct a prediction method.Asymptomatic infected cases are taken to forecast the demand for emergency supplies.

Integrating Trade-In Strategies for Optimal Pre-Positioning Decisions in Relief Supply-Chain Systems

“…In the realm of relief supply chains, scholars focus on precise prediction of relief supplies needs and optimizing stockpile decisions. For instance, predictive models combining intuitionistic fuzzy sets and grey Markov methods have been employed to accurately forecast relief supplies demands during large-scale pandemics, aiming to optimize inventory levels and enhance distribution efficiency [21]. Simultaneously, research has utilized Bayesian decision models, integrating official forecast models from the National Hurricane Center, to balance the trade-off between forecast accuracy and cost efficiency, thereby optimizing strategies for relief supplies stockpiling [22].…”