Sijmen Boersma
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The increasing decentralization and digitalization of energy distribution systems pose significant challenges to the management of operational and maintenance processes. The integration of renewable energy sources and the resulting dynamic grid conditions necessitate more adaptive, efficient, and technologically advanced workforce management (WFM) solutions. This work presents a systematic approach to modeling WFM processes tailored to the needs of modern smart grids. Based on a detailed evaluation of modeling languages and a requirements analysis informed by industry workshops, Business Process Model and Notation (BPMN) was identified as the most suitable formalism. An iterative development process was established, combining user story specification, process modeling, validation, and equirement refinement. The resulting WFM processes integrate real-time reactivity and AI-based decision support mechanisms. These include intelligent task classification, personnel selection, and resource planning, enabling human operators to make faster, more consistent decisions in dynamic grid environments. The work lays the foundation for a digitalized, flexible WFM-System that addresses the future demands of sustainable and secure energy distribution.
Ausgangssituation:
Im Straßengüterverkehr werden Transporte zur Einhaltung der Lenk- und Ruhezeiten unterbrochen. Diese Unterbrechungen verlängern nicht nur die Transportzeit. Es werden außerdem Lkw-Stellplätze belegt, von denen laut BASt bzw. BGL an deutschen Autobahnen ca. 35.000 fehlen. Die Stellplatzsuche führt zu CO2-intensivem Parksuchverkehr, Lenkzeitüberschreitungen und Frustration bei Lkw-Fahrer*innen. Wildparkende Lkw verursachen Unfälle, stören Anwohner*innen und begünstigen laut VEDA Ladungsüberfälle mit Schäden in Milliardenhöhe.
Ergebnis & Nutzen für die Zielgruppe:
Die Unterbrechung von Transporten kann vermieden werden, indem Trailer am Ende der Lenkzeit durch ein IoT-Schloss gesichert und anschließend ausgeruhten Fahrer*innen übergeben werden. Im Projekt wurde ein sicherer, speditionsübergreifender „Staffelverkehr“ erforscht: Auf einer Internetplattform wurden Langstrecken mithilfe von KI-Algorithmen in Teilstrecken zerlegt, die dann über einen Lenkzeiten-Marktplatz zwischen Speditionen und Frachtführer*innen anhand von Echtzeitdaten (z. B. Verkehr, Infrastruktur, IoT, Telematik) vermittelt wurden.
Bei näherem Interesse, z. B. an der Demoanwendung, können sich Unternehmen an unseren Partner MANSIO wenden: https://www.mansio-logistics.com/kontakt/demo-termin-vereinbaren/.
Crisis situations can lead to extreme consequences for society and the economy, such as the disruption of supply chains and the collapse of critical infrastructure. The challenge for optimal crisis preparation lies in the unpredictability of causes, duration and scope, and severity. AI-based resilience services can aid in crisis preparation by providing software-based warnings, recommendations, and countermeasures. The aim of this paper is to present a method for evaluating such services in terms of their usefulness and acceptance. A questionnaire is presented, and the results of its piloting phase are disseminated. With these results, existing and projected AI-based services for crisis prevention can be evaluated.
In road haulage, transports are interrupted by truck drivers to comply with driving and rest times. On long-distance routes, these interruptions lead to a considerable increase in transport time. Transport interruption can be avoided by so-called relay traffic: a vehicle (e. g. semi-trailer) is handed over to a rested driver at the end of the driving time. This type of transport requires a certain company size. In Germany, however, transport companies have 11 employees on average. Intra-company relay traffic is therefore not economically viable for most transport companies. To organize an intermodal transport across forwarding companies, long-distance routes need to be split into partial routes to divide them between freight forwarders and carriers. This paper presents a data concept for an algorithm to find the best possible route sections along a previously defined start and endpoint. The developed data concept includes order-specific data, forwarder-specific data, real-time traffic data, geographical data as well as data from freight forwarding software and telematics to be the basis for the route sectioning algorithm. In this paper, different data sources, external services and logistic systems are analyzed and evaluated. It is shown which data is needed and what the best ways are to select and derive this data from the different data sources.
