Multi-product And Multi-period Replenishment Decision-making Model Under Uncertainty Of Demand And Lead Time

In the operation process of e-commerce platforms,replenishment decision-making often needs to consider multi-product replenishment simultaneously,and the replenishment plan involves multiple cycles,which makes the decision-making process challenging.From the demand side,customer demand individualization,diversity,and market changes can cause varying degrees of demand uncertainty.From the supply side,delivery time from suppliers is uncertain,affected by many factors in the production and transportation processes,and the lead time of products is also uncertain.The demand and lead time for different goods are not the same,and demand and lead time may exceed expectations.Therefore,from the perspective of e-commerce platform enterprises,studying the problem of multi-product,multi-cycle replenishment decision-making under demand and lead time uncertainties has strong practical significance.The research work is summarized as follows:Firstly,the operational models of e-commerce platform enterprises are classified and summarized,understanding the uncertainties on the demand side and the supply side,as well as the risks they may pose to supply chain operations.The research status of singleproduct and multi-product replenishment decision-making problems is summarized,along with the application of robust optimization and Monte Carlo simulation in inventory management and replenishment decision-making.Secondly,based on the above summary of relevant research and theoretical knowledge of replenishment decision-making and inventory control,a multi-product multi-period replenishment decision-making model is constructed based on the singlefacility single-product multi-period inventory control model.Considering demand uncertainty,a robust optimization model for multi-product multi-period replenishment decision-making is built.The Differential Evolution(DE)algorithm is designed and its effectiveness is verified through numerical experiments.The DE algorithm is then applied to solve the multi-product multi-period replenishment decision-making model under demand uncertainty.Monte Carlo simulation is conducted to simulate the obtained decisions,and comparative experiments are designed to demonstrate the robustness of the model and the effectiveness of handling demand uncertainty.Furthermore,a multi-product multi-period replenishment decision-making model considering lead time is constructed.Robust optimization theory is applied,and interval uncertainty sets are used to describe the uncertainty of lead time.A multi-product multiperiod replenishment decision-making model considering lead time uncertainty is built.The DE algorithm is applied to solve the model,and the impact of lead time uncertainty on total system cost is analyzed and demonstrated.Simulation experiments are designed to validate the effectiveness of the model in handling lead time uncertainty.Lastly,a multi-product multi-period replenishment decision-making model considering both demand and lead time uncertainties is constructed.Robust optimization theory is applied,and the DE algorithm is used to solve the multi-product multi-period replenishment decision-making model under different parameter combinations.Simulation experiments are designed to analyze the cost variations under different decisions.Furthermore,simulation experiments with demand disturbances and lead time variations are designed to validate the robustness,practicality,and effectiveness of the model in handling demand and lead time uncertainties.The proposed model can provide scientific and rational decision support for enterprises and reduce the cost increase caused by uncertainties on both the supply and demand sides.