Optimization of multiagent auctioning processes in flexible production networks Shorter product lifecycles and increasing product complexity are important trends that drive the need for increased flexibility of production companies. One way to cope with this problem is to engage in flexible production networks where companies can find missing production capabilities and offer their own ones. To enable this connection across company borders and software platforms, an agentbased communication approach is proposed that is based on the OPC UA standard. This paper also develops an optimization concept that can increase a companyxe2x80x99s benefits, i.e. profits, by applying a learning algorithm as well as an approach to calculate optimal margins, which is encapsulated in an optimization agent.

Potential Game based Distributed Optimization of Modular Production Units We present a novel approach for distributed optimization of highly flexible, modular production units enabling plugandplay production with online optimization capabilities to adapt fast to changing production requirements. The approach is fully distributed in the sense that each production module to be optimized is equipped with its own optimization agent which local objective is the optimization of its own production objectives. To assure the necessary coordination between the agents, the resulting distributed optimization problem is designed using concepts of game theory. To this end, we model the production environment in terms of a potential game where each module is modeled as a player of the game. By assigning suitable utility functions to the players coordination of the agents behavior is achieved to find an optimal collective behavior. We apply the approach to a laboratory scale distributed bulk good production testbed with very encouraging results. In addition, due to the computational simplicity of the approach, an implementation in IEC61131 compatible code is possible allowing a direct implementation of the approach in existing production units.