Optimal procurement strategy of fresh produce retailer under stochastic product qualification and market demand

Consider an optimal procurement strategy for fresh produce,which is a type of perishable product.Due to the different quality provided by each manufacturer,the fresh produce qualification rates are dissimilar.Simultaneously,consumers demand is influenced by the freshness and price of products,as a result,the demand in the market is not fixed.In this scenario,how a particular retailer should develop an optimal procurement strategy will be a core issue in supply chain management.In order to address the above questions,the Bayesian approach is adopted to explore retailer optimal procurement strategies with uncertainty about product supply and market demand.Finally,simulation data are used to analyse the results of the proposed model and expected non-random model to illustrate the validity and feasibility of the proposed model.

Multi-timescale optimization scheduling of interconnected data centers based on model predictive control

With the promotion of“dual carbon”strategy,data center(DC)access to high-penetration renewable energy sources(RESs)has become a trend in the industry.However,the uncertainty of RES poses challenges to the safe and stable operation of DCs and power grids.In this paper,a multi-timescale optimal scheduling model is established for interconnected data centers(IDCs)based on model predictive control(MPC),including day-ahead optimization,intraday rolling optimization,and intraday real-time correction.The day-ahead optimization stage aims at the lowest operating cost,the rolling optimization stage aims at the lowest intraday economic cost,and the real-time correction aims at the lowest power fluctuation,eliminating the impact of prediction errors through coordinated multi-timescale optimization.The simulation results show that the economic loss is reduced by 19.6%,and the power fluctuation is decreased by 15.23%.