In this study, an agent-based fuzzy collaborative intelligence (FCI) approach with entropy as a measure of consensus was proposed for estimating the unit cost of a product, which is a critical task for manufacturers. However, the unit cost of a product declines according to a learning process that involves considerable uncertainty, rendering this task difficult. Although a few FCI methods have been proposed to estimate the unit cost of a product under uncertainty, they are inefficient or based on an insufficient consensus. To resolve these problems and enhance the efficiency of estimating the unit cost of a product, an entropy-consensus agent-based FCI approach was proposed in this study. In the proposed method, an agent autonomously applies one of several mathematical programming methods to model a fuzzy unit cost learning process, which is then used to estimate the unit cost. The fuzzy unit cost estimates by the agents are subsequently aggregated through fuzzy intersection. If aggregation result entropy is higher than a threshold, the agents have an insufficient consensus, and the agents must modify their settings and re-estimate the unit cost. After a consensus has been reached, a back propagation network defuzzifies the aggregation result for deriving a crisp value. The proposed methodology was applied to a dynamic random access memory case. The experimental results indicated that using autonomous agents accelerated collaboration and increased efficiency. Moreover, deriving a representative value only after reaching a consensus was conducive to estimation performance.
- Fuzzy collaborative intelligence
- Unit cost