This paper describes a new transfer learning method for modeling sensor time series following multiple different distributions, e.g. originating from multiple different tool settings. The method aims at removing distribution specific information before the modeling of the individual time series takes place. This is done by mapping the data to a new space such that the representations of different distributions are aligned. Domain knowledge is incorporated by means of corresponding parameters, e.g. physical dimensions of tool settings. Results on a real-world problem of industrial manufacturing show that our method is able to significantly improve the performance of regression models on time series following previously unseen distributions.

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This paper deals with the approach of application of intelligent software agents to improve information logistics in the area of process planning and production control. Thus, enterprises will be able to fulfil the requirement of flexible, reliable and fault-tolerant manufacturing. Fulfilment of these requirements is a prerequisite for successful participation in modem business alliances like supply chains, temporal logistic networks and virtual enterprises. Thus, agent-based improvements of information logistics enable enterprises to face the challenges of competition successfully. Current research activitiesfocus on the development of agent-based systems for integrated process planning and production control. They led to the "lntaPS" approach, which is presented in this paper.

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Abstract. Intelligent software agents are a promising approach to improve information logistics in manufacturing enterprises. This paper deals with the application of agents in the area of process planning and production control. Thus, enterprises will be able to fulfil the requirement of flexible, reliable and fault-tolerant manufacturing. Fulfilment of these requirements is a prerequisite for successful participation in modern business alliances like supply chains and virtual enterprises. Thus, agent-based improvements of information logistics enable enterprises to face the challenges of competition successfully. Current research activities focus on the development of agent-based systems for integrated process planning and production control. They led to the "IntaPS" approach, which is presented in this paper. Furthermore, the integration of different agent-based systems in context of collaborative manufacturing in supply chains will be discussed.