Pricing strategy and system performance in a cloud-based manufacturing system built on blockchain technology

Blockchain technology has been enjoying rapid development in numerous areas where trust, safety, and global payments are deemed important. In this research, we introduce a framework that integrates blockchain technology with the emerging concept of cloud manufacturing. It has the potential to dramatically improve the feasibility and applicability of cloud manufacturing. Our focus is on the pricing strategies for service providers in bidding for jobs in such a system. A scenario is developed based on cloud additive manufacturing services. A game theoretic approach for pricing simulation is proposed in the research, and a fuzzy algorithm is employed in the pricing decision. Two pricing policies are considered: one is that the information of players is unknown to each other, and the other is that the information of players who are within a limit of Euclidean space is known to each other. The effects of system load and the weight coefficients in KNN recommendation algorithm are also studied. The results show that the pricing policy has significant effect on revenue generation for providers, and the increase of system load helps to grow revenue for providers only when the machine utilization is low and job waiting time is short. In addition, the system seems to be less sensitive to the change of KNN weights in terms of individual provider and system revenues.