@inproceedings{RolfReggelinLangetal., author = {Rolf, Benjamin and Reggelin, Tobias and Lang, Sebastian and Galka, Stefan}, title = {Developing a Decision Support System for Integrated Decision-Making in Purchasing and Scheduling under Lead Time Uncertainty}, series = {Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS55): January 3-7, 2022, Maui, Hawaii, USA}, volume = {2022}, booktitle = {Proceedings of the 55th Hawaii International Conference on System Sciences (HICSS55): January 3-7, 2022, Maui, Hawaii, USA}, doi = {10.24251/HICSS.2022.247}, pages = {1964 -- 1973}, abstract = {Decision-making in supply chain management is complex because of the relations between planning tasks from different stages and planning levels. Uncertainties such as unpredictable supplier lead times and supply chain disruptions further complicate decision-making. Considering the case study of a company in printed circuit board assembly, a three-level concept is proposed that includes a decision support system. The global single-source supply network is characterized by highly variable lead times. Hence, the company maintains high inventory levels to prevent running out of stock. The decision support system considers the purchasing and scheduling decision problems in an integrated way. The prototypical implementation of the purchasing algorithm uses a genetic algorithm that recommends reorder days and order quantities using a simulation model. In addition, it evaluates the risks of the recommended solution by calculating the probability of stockouts for each order cycle.}, language = {en} } @article{KlenkGalka, author = {Klenk, Eva Marianne and Galka, Stefan}, title = {Analysis of real-time tour building and scheduling strategies for in-plant milk-run systems with volatile transportation demand}, series = {IFAC - PapersOnLine}, volume = {52}, journal = {IFAC - PapersOnLine}, number = {13}, publisher = {Elsevier}, doi = {10.1016/j.ifacol.2019.11.517}, pages = {2110 -- 2115}, abstract = {In modern manufacturing systems, milk run (MR) systems represent route-based, cyclic material-handling systems that are used widely and enable frequent deliveries of containerized parts in small lot sizes from a central storage area to multiple points of use on the factory floor. Usually, deliveries take place at predetermined, constant time intervals. As in real-world MR systems, the number of containers to deliver in each interval varies in the short term, peaks in the number of deliveries may lead to capacity bottlenecks and late deliveries. In this paper, we develop three alternative strategies to determine starting times of MR cycles in real-time based on the current number of delivery orders in the system aiming to better handle variations in the number of orders. We evaluate the three strategies for different system load scenarios with respect to delivery service levels, order lead times, systems utilization and necessary number of MR trains using a discrete-event simulation model.}, language = {en} } @inproceedings{SchuheggerArmbrusterGalka, author = {Schuhegger, Lukas and Armbruster, Thomas and Galka, Stefan}, title = {Factory Data Model for Planning Variant Sequences, Expansion Stages and Reconfigurations}, series = {IFAC-PapersOnLine}, volume = {56}, booktitle = {IFAC-PapersOnLine}, number = {2}, publisher = {IFAC / Elsevier}, doi = {10.1016/j.ifacol.2023.10.1573}, pages = {227 -- 232}, abstract = {A factory has to adapt to a future production program and increasing volumes. The planning of new factories must therefore already take expansion stages and reconfigurations into account. This leads to an increase in the complexity of planning, as different variants have to be planned and put into a chronologically reasonable and cost-optimized sequence. A factory data model that covers expansion stages manages the planning data amount and reduces complexity. Based on existing models from the literature, a factory data model was developed and modeled in the Unified Modeling Language standard. The factory data model shows which classes are interdependent and how planning results are stored reusable for other variants. An example is used to explain the developed factory data model and highlighting its advantages. The aim is to enable factory planners to document variant sequences and expansion stages in a comprehensible way, which supports the interdisciplinarity of factory planning projects.}, language = {en} } @article{GalkaWappler, author = {Galka, Stefan and Wappler, Mona}, title = {Integration of cash flow management and further aspects of the supply chain management in production system design}, series = {IFAC-PapersOnLine}, volume = {56}, journal = {IFAC-PapersOnLine}, number = {2}, publisher = {IFAC / Elsevier}, doi = {10.1016/j.ifacol.2023.10.1687}, pages = {947 -- 952}, abstract = {One major goal of production system design is to decide on space requirements in the plant. In this phase, detailed information about the supply chain network (e.g. which suppliers deliver which parts) is often unavailable. Nevertheless, typical decisions in supply chain management, like the definition of replenishment processes and quantities or make-or-buy decisions, impact space requirements and the capital investment in raw materials. Therefore, it should be considered in factory and production planning. This research article proposes a simulation approach for a production simulation in which raw material replenishment is integrated to evaluate the capital investment and space requirements for raw materials.}, language = {en} }