The invention discloses a multi-objective optimization method and
system for centralized procurement with quantity based on MODDPG. The method comprises the following steps: constructing a collection chain multi-objective optimization model which comprises a supply enterprise, a procurement alliance and a third-party group purchase organization and is based on deep
reinforcement learning and a multi-objective optimization
algorithm; based on the historical state of the supply enterprise, the purchase alliance or the third-party group purchase organization, control operation is generated through a strategy network, and random variation is carried out on the control operation according to the
mutation probability; executing control operation, obtaining rewards and states fed back by the environment, and adding the historical state, the control operation, the rewards and the states into an experience
pool; selecting a high-quality
population sample from the experience
pool by using a multi-objective optimization
algorithm; training a strategy network and a value evaluation network of a set sampling chain multi-objective optimization model based on the high-quality
population samples; and using the trained model to generate a control operation strategy according to the current states of the supply enterprise, the purchase alliance and the third-party group purchase organization.