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Series Proceedings IT in Production and Logistics

Deininger, M.: Modellierungsmethode für die simulationsbasierte Optimierung rekonfigurierbarer Produktionssysteme. Series "Fortschritte in der IT in Produktion und Logistik", Vol 2. Göttingen: Cuvillier 2019.

Manufacturing companies today face many challenges. Competition, technological progress, and the changing expectations of customers result in a constantly changing environment. This leads to a con-tinuously changing product range, which is not only geared towards strategic realignment, but is also driven by customer demands. If a customer makes a request that does not correspond to the current product portfolio, decisions about the acceptance of the order must be made within a short time, often without a reliable forecast of their effects. Simulation studies can be used to investigate potential changes to a production system before applying them in the real world. In particular, the stochastic behavior of processes in production systems, such as varying processing times, can be modeled and thus represent the actual behavior of the staff. How-ever, simulation is only able to evaluate a given system. Another aid to planning are optimization tech-niques. These allow for automated evaluation of various configurations of a system and providing a solution. The combination of simulation and optimization results in a method that supports a planner in the decision-making process, considering the stochastic influences. In the present work, such a method is developed that enables the combination of simulation and opti-mization to determine which changes to a production system can be conducted to fulfill previously unachievable customer orders. These changes include, in the simplest case, the implementation of a new processing order for the customer orders. If this is not sufficient, it will be examined whether the addition of new resources enables the system to fulfill all customer orders. It is also possible to save or replace resources. Likewise, new processes can be added, e.g., for setup or qualification tasks. The implementation of this procedure is carried out by a multi-level simulation-based optimization, which is based on modular modeling. Using modules, individual processes of the production system can be represented and linked together. Further, this approach allows for adding, removing, and exchang-ing processes. As part of an optimization, it is thus possible to determine a collection of processes that enables to fulfill all customer orders. In addition to processes, modules can also represent resources that can be used to determine the necessary resources in parallel to the processes. Each identified con-figuration undergoes job shop scheduling and is evaluated using simulation. After carrying out the simu-lation-based optimization, the planner receives a pareto diagram which contains all the solutions inves-tigated. From these, the solution to be implemented can finally be selected by the planner. Two application examples demonstrate the applicability of the method. It is shown that every stage of simulation-based optimization helps to present a solution to the planner, with which all customer or-ders can be fulfilled. To do this, each level is considered and validated individually before considering their interaction.

Scheidler, A.A.: Methode zur Erschließung von Wissen aus Datenmustern in Supply-Chain-Datenbanken. Series "Fortschritte in der IT in Produktion und Logistik", Vol 1. Göttingen: Cuvillier 2017.

Ein elementarer Schritt zur Beherrschbarkeit der Supply Chain ist die Identifikation von Wirkzusammenhängen, die sich in den logistischen Transaktionen spiegeln. Aufgrund der unüberschaubaren Datenmenge kann die Entdeckung von komplexen Wirkzusammenhängen nicht manuell erfolgen. Die Dissertationsschrift stellt eine Methode zur Entdeckung von Wissen, wie beispielsweise den Wirkzusammenhängen, vor und diskutiert die Berücksichtigung von Kontextwissen in den einzelnen Vorgehensmodellphasen. Ein Schwerpunkt der entwickelten Methode ist die Integration einer modellbegleitenden Verifikation und Validierung in ein Vorgehensmodell der Wissensentdeckung. Durch einen neuartigen Einsatz der Simulation erweitert die Arbeit zudem die existierenden Verifikationsmöglichkeiten des Knowledge Discovery in Databases. Um einen Einsatz des Modells auch bei unzureichender Datenlage zu ermöglichen werden abschließend Konzepte des Data Farmings als Methodenelement eingeführt. Die praktische Anwendbarkeit der in dieser Arbeit entwickelten Methode wird anhand von Transaktionsdaten eines Elektronikkleingeräteherstellers sowie einem Data-Farming-Modell demonstriert.