Bernd Scholz-Reiter

Manufacturing and logistic service companies are increasingly confronted with high dynamics and complexity. Due to its particular suitability for short-term and situation-dependent decision-making, autonomous control can improve planning and control of production and related transportation processes. This chapter gives an overview of the research that the BIBA—Bremer Institut für Produktion und Logistik GmbH has performed over the past years in the field of autonomously controlled production and transportation networks. The chapter focuses on the modeling approaches and the autonomous control methods that have been developed. These methods have been evaluated using both theoretical and real-world scenarios. The results show the applicability and suitability of autonomous control in complex and dynamic production and transportation environments. In addition, influences on the methods’ performance and the integration of autonomous control into conventional planning and control systems are discussed. Finally, the chapter looks at the significance of autonomous control in the context of Industry 4.0 and shows the relations between both concepts.

Autonomous control methods are a promising approach for manufacturing companies to cope with dynamic influences. An important issue for the acceptance of autonomous control is to provide sufficient planning accuracy. Therefore, transferring autonomous control into practical application requires the harmonization of autonomous control with commonly used, central planning methods. This paper introduces coupling strategies with different degrees of adherence to planning parameters for the harmonization of central planning and autonomous control. The simulation based evaluation considers both logistic objectives achievement and the adherence to predefined planning parameters. Besides varying the level of dynamic influences, the simulation study also analyzes the impact of production schedules with different levels of temporal flexibility by inserting additional idle time into the schedule. The results serve as a guideline for the selection of suitable coupling strategies depending on dynamic influences and user specific requirements to the strategies' performance.