Multi-objective resource optimization scheduling based on iterative double auction in cloud manufacturing

Abstract

Cloud manufacturing is a new kind of networked manufacturing model. In this model, manufacturing resources are organized and used on demand as market-oriented services. These services are highly uncertain and focus on users. The information between service demanders and service providers is usually incomplete. These challenges make the resource scheduling more difficult. In this study, an iterative double auction mechanism is proposed based on game theory to balance the individual benefits. Resource demanders and providers act as buyers and sellers in the auction. Resource demanders offer a price according to the budget, the delivery time, preference, and the process of auction. Meanwhile, resource providers ask for a price according to the cost, maximum expected profit, optimal reservation price, and the process of auction. A honest quotation strategy is dominant for a participant in the auction. The mechanism is capable of guaranteeing the economic benefits among different participants in the market with incomplete information. Furthermore, the mechanism is helpful for preventing harmful market behaviors such as speculation, cheating, etc. Based on the iterative double auction mechanism, manufacturing resources are optimally allocated to users with consideration of multiple objectives. The auction mechanism is also incentive compatibility.