大规模时变需求下按需服务平台动态定价优化OA

The on-demand service platform provides real-time quotes for customers who are sensitive to waiting time,based on delivery time and production capacity.To optimize dynamic pricing and construct a platform profit-maximization model with waiting time and dynamic capacity as key factors,a partial differential equation is used to simulate the time-varying evolution of order density in a mass production system,motivated by vehicle traffic flow and density theory.The order departure rate,i.e.,the number of orders delivered to customers,is derived.Under full production capacity,the optimal price is obtained through mathematical derivation.Under limited production capacity,the continuous profit-maximization model is discretized,and an algorithm is designed and validated to obtain the approximate optimal price.Through extensive numerical experiments,the optimal delivery time and static productivity are shown to exist.Finally,the existence of optimal production capacity is further verified by extending the model to cases of sudden production interruption and continuous linear decay.The application of partial differential equation theory provides a new perspective for characterizing large-scale order production system.