Long-/Short-Term Preference Based Dynamic Pricing and Manufacturing Service Collaboration Optimization

Manufacturing service (MS) collaboration promotes the social collaboration of distributed enterprises, which makes profits through manufacturing resource sharing on platforms. Therefore, the pricing strategy for MSs will affect the collaboration results, and the satisfaction of enterprises with the platform further. Hence, in this article, a personalized dynamic pricing based MS collaboration optimization method is proposed. First, due to the poor information of the enterprise preference, long-term and short-term preferences of enterprises are estimated based on scarcity, including service features, service quantity, and available time. Then, the personalized pricing method is proposed to adapt to the dynamic collaboration process. And the consumer utility model that considers time decay and price changes is constructed, which reflects the utility characteristics of consumers in the actual collaboration process, such as the utility decrease and deceleration rate increase with time consuming. Finally, Q-learning algorithm based MS collaboration optimization verifies the effectiveness and superiority of the method.