Complementary application of mathematical and discrete-event models to solve complex planning and control problems in offshore construction logistics

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Offshore construction logistics pose an exceptionally challenging problem in terms of planning and control. Generally, one can differentiate two approaches: event-discrete simulations as well as mathematical or stochastic optimizations. By themselves, both methods provide their own advantages and disadvantages in terms of computational time, level of detail und optimality.

This project aims to investigate new ways for the complementary utilization of both types of methods in the context of offshore construction logistics. Under the basic assumption that despite formal differences, both types of models describe the same elements of the real world system, this project aims to develop a method to convert in between or to generate each kind of model with its own level of aggregation/abstraction based on a more basic description of the real world system. Consequently, the advantage of both types of models can be used complementary within computer aided planning and control methods.

Duration 01.04.2019 - 30.09.2021, Funded by DFG
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LNG Armaturen Set

Development of a sensitive valve set for high-volume ship to ship LNG transfer

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The project aims at the development of a system which can be used on a large number of different ship types and thus leads to a significantly higher level of safety, installability and maintainability while at the same time reducing costs. The task of BIBA is to develop an Augmented Reality (AR) solution that can be used for maintenance and service purposes alongside the valve set.

By means of a combination of a commercial data goggle, a camera and an embedded PC, an easily configurable application solution is created. This solution should be able to identify the existing components, to read out the corresponding status information both visually and via radio, and to supply the users with maintenance information and checklists.

The AR solution will be developed to support technicians in operation, installation and maintenance of the sensitive LNG valve set. By means of image processing and object recognition techniques, the first step is to collect information on the condition of the valves. Subsequently, an AR-User Interface will be developed, which acts as an assistance system for the users.

Duration 01.03.2019 - 28.02.2021, Funded by BMWi
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LNG Safety

Safety process system for cryogenic fluid transfer

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The handling of cryogenic fluids (e.g. liquefied natural gas) bears major risks with regard to operational safety. If the liquid leaks during a transfer process (e.g. fueling of ships), large amounts of gas can quickly be produced which are highly flammable and explosive. Therefore, an appropriate safety system for process monitoring is necessary.

The aim of the project is to improve operational safety during the LNG transfer process by means of a redundant optical monitoring system. This system should be able to both detect fittings, ship superstructures, and people automatically and to perform an automated visual inspection of the correct coupling.

The multi-camera system consists of a wide-angle, a zoom and an infrared camera and can therefore react to a wide variety of environmental conditions (day, night, weather influences). It automatically monitors the LNG transfer process. By using Deep Machine Learning, the object recognition of fittings, ship superstructures and people is made possible, which is necessary for monitoring the danger zone.

Duration 01.03.2019 - 28.02.2021, Funded by BMWi
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Projektlogo Individual Predictive Maintenance


Individual Predictive Maintenance

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Ziel des Projektes ist die Entwicklung einer Toolbox zur Überwachung von Sensordaten für eine individuelle prädiktive Instandhaltung von Dieselmotoren für Schienenfahrzeuge.


Derzeit werden Instandhaltungsmaßnahmen reaktiv oder in periodischen Intervallen präventiv durchgeführt. Dieses Vorgehen ist jedoch mit hohen Kosten verbunden, da im Schadensfall meist Folgeschäden auftreten. Zudem führen die ausgefallenen Züge nicht nur zu Verspätungen der darin transportierten Personen und Güter, sondern blockieren auch die Bahnstrecke für weitere Transporte und die damit zusammenhängende Logistikkette. Allerdings ergeben sich durch das vorsorgliche Austauschen der Komponenten relativ hohe Instandhaltungskosten, da diese noch für einen längeren Zeitraum hätten genutzt werden können.


Durch eine Instandhaltung im Bedarfsfall (kurz vor Störereignis) können die Instandhaltungskosten minimiert werden, ohne das Risiko eines Zugausfalls signifikant zu erhöhen. Unter Anwendung künstlicher Intelligenz sollen frühzeitig auszutauschende Motorkomponenten identifiziert und damit eine ressourceneffiziente Instandhaltungsplanung ermöglicht werden.

Duration 01.02.2019 - 31.07.2020, Funded by BAB Bremer Aufbau-Bank GmbH
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Projektlogo EIT Manufacturing
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EIT Manufacturing

EIT Manufacturing

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The manufacturing industry is facing major challenges due to increasing global competition, low-cost production in developing countries and scarce raw materials. EIT Manufacturing is an initiative of the European Institute of Innovation and Technology (EIT), in which BIBA is one of 50 core partners.

EIT Manufacturing’s mission is to bring European manufacturing actors together in innovation ecosystems that add unique value to European products, processes, services – and inspire the creation of globally competitive and sustainable manufacturing. To do so, the initiative has six strategic objectives:


  1. Excellent manufacturing skills and talents: adding value through an upskilled workforce and engaged students.
  2. Efficient manufacturing innovation ecosystems: adding value through creating ecosystems for innovation, entrepreneurship and business transformation focused on innovation hotspots.
  3. Full digitalization of manufacturing: adding value through digital solutions and platforms that connect value networks globally.
  4. Customer-driven manufacturing: adding value through agile and flexible manufacturing that meets global personalized demand.
  5. Socially sustainable manufacturing: adding value through safe, healthy, ethical and socially sustainable production and products.
  6. Environmentally sustainable manufacturing: adding value by making industry greener and cleaner.

    EIT Manufacturing aims for the following goals by 2030:

    • Create and support 1000 start-ups

    • 60% of manufacturing companies adopt sustainable production practices

    • EUR 325 million investment attracted by EIT Ventures

    • 50 000 people trained and up- or re- skilled

    • Create 360 new solutions

    • 30% of material use is circular


Duration 01.01.2019 - 01.01.2026, Funded by European Institute of Innovation & Technology (E

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transport logistic 2019
June 4th - 7th, 2019, Munich, Germany
July 22nd - 26th, 2019, BIBA

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