Michael Lütjen

1. The increasing complexity of infrastructure planning, driven by the integration of multimodal transport systems, sustainability goals, and evolving stakeholder expectations, demands new approach...

1. Offshore wind farm installation planning combines logistical, technical, and environmental complexities that demand robust and transparent decision-support models. These models support decisions...

Deutsche Fahrzeughersteller, Klebstoffhersteller und Maschinenbauer sind weltweit führend bei der Entwicklung von Klebstoffsystemen und tragen erheblich zur deutschen Wertschöpfung bei. Die Digitalisierung in der Klebetechnik birgt großes wirtschaftliches Potenzial, wird jedoch aufgrund der Komplexität der Prozesse bislang unzureichend genutzt. Im Beitrag wird der Stand der Technik zur Digitalisierung im Sinne von Industrie 4.0 dargestellt und aktuelle Herausforderungen herausgearbeitet.

Die Herstellung von Myzel-gebundenen Biomaterialien hat in den letzten Jahren steigende Aufmerksamkeit gefunden. Aufgrund einzigartiger biologischer Eigenschaften von Myzel als Binder in Kompositmaterial bieten sich interessante Verwendungsmöglichkeiten in der Verpackungs-, Bau- und Lebensmittelindustrie. Trotz verschiedener erfolgreicher Anwendungen stehen einer höheren Marktdurchdringung einige wesentliche Herausforderungen gegenüber. Nachfolgend wurden die verschiedenen Herausforderungen einer hochskalierten Produktion analysiert und die jeweiligen Potenziale bzw. Lösungsmöglichkeiten zur Überwindung der Herausforderungen aufgezeigt.

In the literature, different authors attribute between 15% to 30% of a wind farm’s costs to logistics during the installation, e.g., for vessels or personnel. Currently, there exist only a few approaches for crew scheduling in the offshore area. However, current approaches only satisfy subsets of the offshore construction area’s specific terms and conditions. This article first presents a literature review to identify different constraints imposed on crew scheduling for offshore installations. Afterward, it presents a new Mixed-Integer Linear Model that satisfies these crew scheduling constraints and couples it with a scheduling approach using a Model Predictive Control scheme to include weather dynamics. The evaluation of this model shows reliable scheduling of persons/teams given weather-dependent operations. Compared to a conventionally assumed full staffing of vessels and the port, the model decreases the required crews by approximately 50%. Moreover, the proposed model shows good runtime behavior, obtaining optimal solutions for realistic scenarios in under an hour.

Text mining tools are now widely used for the efficient management of information and resources in business, academic and research organizations. This paper provides a comprehensive overview of research articles on the application of text mining techniques in the field of Supply Chain Risk Management and the apparel industry. Research articles published between 2000 and 2020, were obtained from various journals through two online databases, i.e., SCOPUS and IEEE Xplore. Through a systematic approach following PRISMA guidelines, 370 research papers were screened, filtered and finally classified into three main areas: Supply Chain Risk Management and outsourcing in the apparel industry, application of text mining in Supply Chain Risk Management and application of text mining in the apparel industry. In this study, we have identified a comprehensive list of various available data sources for text mining, methodologies and risks associated with outsourcing in the apparel industry. We classify the gaps in expanding the application of text mining in the apparel industry’s Supply Chain Risk Management. Extracting useful information from online newspapers through text mining could vividly enhance the ability to monitor supply chain risks and provide the ability to link data to provide decision makers with the right information at the right time.

The installation of offshore wind farms poses particular challenges due to expensive resources and quickly changing weather conditions. Model-based decision-support systems are required to achieve an efficient installation. In the literature, there exist several models for scheduling offshore operations, which focus on vessels but neglect the influence of resource restrictions at the base port and uncertainties involved with weather predictions. This article proposes a Mixed-Integer Linear Programming model for the scheduling of installation activities, which handles several installation vessels as well as restrictions about available cargo bridges at the port. Additionally, the article explains how this model can be combined with a Model Predictive Control scheme to provide decision support for the scheduling of offshore installation operations. The article presents numerical studies of the effects induced by resource restrictions and of different parametrizations for this approach. Results show that even small planning windows, paired with comparably low computational times, achieve reasonably good results. Moreover, the results show that an increase in vessels comes at diminishing returns concerning the installation efficiency. Therefore, the results indicate that available good-weather windows primarily limit efficiency.

Dough fermentation plays an essential role in the bread production process, and its success is critical to producing high-quality products. In Germany, the number of stores per bakery chain has increased within the last years as well as the trend to finish the bakery products local at the stores. There is an unsatisfied demand for skilled workers, which leads to an increasing number of untrained and inexperienced employees at the stores. This paper proposes a method for the automatic monitoring of the fermentation process based on optical techniques. By using a combination of machine learning and superellipsoid model fitting, we have developed an instance segmentation and parameter estimation method for dough objects that are positioned inside a fermentation chamber. In our method we measure the given topography at discrete points in time using a movable laser sensor system that is located at the back of the fermentation chamber. By applying the superellipsoid model fitting method, we estimated the volume of each object and achieved results with a deviation of approximately 10% on average. Thereby, the volume gradient is monitored continuously and represents the progress of the fermentation state. Exploratory tests show the reliability and the potential of our method, which is particularly suitable for local stores but also for high volume production in bakery plants.