Logistics Journal: Proceedings
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Nachhaltige Optimierung der Distributionslogistik mit-tels Simulation in einer regionalen Brauerei
Full text linkSowohl ökonomische als auch ökologische Nachhaltigkeit erfordern einen transparenten Blick auf die zu erwarteten als auch realisierten Emissionen, die während der unterschiedlichen Stufen im Wertschöpfungsprozess entstehen können. Die Distributionslogistik bietet hierfür noch entsprechendes Potenzial wie am Beispiel einer regionalen Brauerei gezeigt werden kann. Im Artikel wird dies anhand von 4 Maßnahmenpaketen mittels Simulationsstudien untersucht. Dabei werden die CO₂e-Emissionen als Vergleichsgrößen genutzt
Enhancing Conventional Resistance Calculation via System Simulation: A Digital Twin Concept for Pouch Sorters
Full text linkTaschensorter gewinnen in der Intralogistik, insbesondere im E-Commerce, aufgrund ihrer Fähigkeit zum dynamischen Puffern, Sequenzieren und Sortieren zunehmend an Bedeutung. Üblicherweise werden solche Anlagen für den Betrieb unter Volllast ausgelegt, wobei quasistatische Berechnungsverfahren Anwendung finden. Da diese Systeme im realen Betrieb jedoch überwiegend im Teillastbereich betrieben werden, arbeiten Antrieb, Getriebe und Frequenzumrichter häufig mit reduziertem Wirkungsgrad und sind für einen Großteil der Einsatzzeit überdimensioniert. Zur Steigerung der Energieeffizienz wird in dieser Arbeit ein adaptives Mehrfachantriebskonzept vorgestellt, bei dem ein digitaler Zwilling die Anzahl aktiver Antriebe in Abhängigkeit vom aktuellen Beladungszustand in Echtzeit steuert. Zentrales Element ist ein Simulationsmodell zur Ermittlung der mechanischen Widerstände. Die Simulationsergebnisse zeigen, dass sich der Wirkungsgrad des Antriebs und somit der spezifische Energieverbrauch im Teillastbetrieb signifikant verbessern lassen, ohne Einbußen bei Förderleistung oder Durchsatz. Abschließend werden weitere Schritte zur Validierung sowie mögliche Anwendungsszenarien skizziert.Pouch sorters are gaining increasing importance in intralogistics, particularly in e-commerce, due to their ability to dynamically buffer, sequence, and sort items. These systems are typically designed for full-load operation using quasi-static calculation methods. However, in actual use, they usually operate in partial-load conditions, where drives, gear units, and frequency converters exhibit reduced efficiency and are oversized for most of their operating time. To improve energy efficiency, this paper presents an adaptive multi-drive concept in which a digital twin dynamically adjusts the number of active drives in real time based on the current load of the carriers. The core of this approach is a simulation model that determines the mechanical resistances. The simulation results show that the drive efficiency — and thus the specific energy consumption — can be significantly improved during typical partial-load operation without impairing conveying performance or throughput. Finally, the paper outlines further validation steps and potential application scenarios
Towards Semantically-Aware Few-Shot 3D Reconstruction
Full text linkDas Erfassen reichhaltiger objektbezogener Informationen, einschließlich Form, Textur und Geometrie, bildet eine grundlegende Basis in zahlreichen Anwendungsbereichen. In diesem Zusammenhang hat sich Few-Shot-Rekonstruktion als bedeutendes Forschungsfeld etabliert, da es eine 3D-Rekonstruktion aus einer begrenzten Anzahl von Eingabebildern ermöglicht. Durch die Nutzung von Vorwissen, das in einem trainierten neuronalen Netzwerk kodiert ist, können diese Methoden bislang unbekannte Merkmale rekonstruieren, die über die Informationen der aufgezeichneten Sensordaten hinausgehen. Aktuelle Ansätze modellieren jedoch entweder die gesamte Umgebung, ohne bestimmte Regionen von Interesse hervorzuheben, oder beschränken den Prozess ausschließlich auf das Zielobjekt, indem das umgebende Kontextwissen in einem Vorverarbeitungsschritt vollständig verworfen wird. Ein möglicher Herangehensweiese besteht darin, Objekte in den Bildern zu maskieren und anschließend semantische Informationen direkt von 2D nach 3D mittels Deep Learning abzubilden. Diese Aufgabe weist jedoch hochgradig nichtlineare Eigenschaften auf, und die Integration semantischer Hinweise stellt nach wie vor eine erhebliche Herausforderung dar. In diesem Work-in-Progress-Paper untersuchen wir daher eine Pipeline für semantisch bewusste Few-Shot-3D-Rekonstruktion auf realen Daten.Acquiring rich object-level information, including shape, texture, and geometry, serves as a fundamental building block across multiple domains. In this context, few-shot reconstruction has become a prominent research field due to the ability to achieve 3D reconstruction from a limited set of input images. By leveraging prior knowledge encoded within a trained neural network, these methods can recover unseen features beyond the information obtained from the recorded sensor data. However, current approaches either model the entire environment without emphasizing specific regions of interest or restrict the process to the target object by completley neglecting the surrounding context in a prepossessing step. One potential approach is to apply object masking in the images and then directly map semantic information from 2D to 3D through deep learning. Nevertheless, this task reflecs highly non-linear properties, and integrating semantic cues remains a significant challenge. In this work-in-progress paper, we explore a pipeline for semantically aware few-shot 3D reconstruction on real-world data
Towards Improved Data Quality Management Tools in Logistics
Full text linkIn today’s logistics environment, high-quality data is essential for ensuring efficient processes and sustaining competitiveness.However, missing, erroneous, or duplicate entries in master data often lead to significant business consequences, such as inefficient supply chains, increased operating costs, and poor decision-making.Existing data screening, cleaning and scoring (DSCS) tools for detecting data errors and thus measuring data quality are often cumbersome to use and are not tailored to the specific needs of logistical master data.In this paper, we present design knowledge to guide the development of DSCS tools.We gathered requirements through dedicated workshops and distilled them into a set of actionable design features.To evaluate our design features, we implemented them in a software prototype, which was tested in a usability and multi-case study. Our contribution in form of design features equips logistics practitioners with concrete guidance for creating and implementing effective DSCS tools in their organizations
A Knowledge-Based Intralogistic System for a Circular Factory
Full text linkIn the context of circular production, factories must cope with uncertainty arising from the reuse of components with varying availability, quality, and timing. This creates new requirements for intralogistic systems that are both highly flexible and able to adapt to local shifts in transport demand or unforeseen events. We present a novel modular intralogistic system de- signed to meet these challenges, where autonomous mobile robots can mount self-contained modules to provide on-demand reconfiguration of material-handling capabilities. To ensure semantic interoperability within the circular factory, we further introduce the accompanying ontologies that formalize key concepts and support knowledge-driven decision-making.In the context of circular production, factories must cope with uncertainty arising from the reuse of components with varying availability, quality, and timing. This creates new requirements for intralogistic systems that are both highly flexible and able to adapt to local shifts in transport demand or unforeseen events. We present a novel modular intralogistic system de- signed to meet these challenges, where autonomous mobile robots can mount self-contained modules to provide on-demand reconfiguration of material-handling capabilities. To ensure semantic interoperability within the circular factory, we further introduce the accompanying ontologies that formalize key concepts and support knowledge-driven decision-making
Qualitative analysis of potential and simulation-based verification of production and logistics control when used in multi variant production scenarios
No full textDieser Beitrag führt einen Ansatz zur dezentralen Produktionsregelung auf Pull-Basis zu einer integrierten Produktions- und Logistikregelung fort. In Simulationen werden Auswirkungen auf Bestände, Termintreue sowie Flottenauslastung untersucht. Die Ergebnisse zeigen Potenziale zur Reduktion von Terminabweichungen und Umlaufbeständen bei gleichzeitiger Glättung der Flottenauslastung auf. Damit wird ein praktikabler Weg zur KMU-tauglichen, integrierten Produktions- und Logistikregelung aufgezeigt.This paper extends a decentralized, pull-based production control approach to an integrated production and logistics control concept. Effects on inventories, due-date adherence, and fleet utilization are investigated by simulation. The results indicate potential to reduce schedule deviations and work-in-process while simultaneously smoothing fleet utilization. The findings outline a practical path toward an SME-suitable, integrated production and logistics control
A Novel Approach to Transform Theoretical Vehicle Routing Problems to Practical Applications
Full text linkIn procurement logistics, manual route planning often leads to inefficiencies such as high costs, congestion, and unbalanced truck arrivals. This paper presents a framework for applying Vehicle Routing Problem (VRP) heuristics to inbound logistics, formulated as an Open VRP with real-world constraints such as vehicle capacities, time windows, and maximum tour duration. Two classical construction heuristics, the Nearest Neighbor Heuristic and the Insertion Heuristic, are adapted and implemented in a configurable tool that enables scenario definition and reproducible evaluation.The approach is motivated by a case from a German manufacturing company, whose situation served as a reference point for designing fictitious but realistic datasets. The evaluation across 18 scenarios shows that both heuristics generate feasible solutions suitable as baselines for routing decisions. On average, the Insertion Heuristic achieves 13 % higher loading meter utilization (83 % compared to 69 % for the Nearest Neighbor Heuristic) and requires fewer tours, while overall travel times remain nearly identical between the two methods.Overall, the study demonstrates that heuristic methods provide systematic and time-efficient support for inbound routing in procurement logistics, offering a foundation for practical decision-making and further methodological refinements.In procurement logistics, manual route planning often leads to inefficiencies such as high costs, congestion, and unbalanced truck arrivals. This paper presents a framework for applying Vehicle Routing Problem (VRP) heuristics to inbound logistics, formulated as an Open VRP with real-world constraints such as vehicle capacities, time windows, and maximum tour duration. Two classical construction heuristics, the Nearest Neighbor Heuristic and the Insertion Heuristic, are adapted and implemented in a configurable tool that enables scenario definition and reproducible evaluation.The approach is motivated by a case from a German manufacturing company, whose situation served as a reference point for designing fictitious but realistic datasets. The evaluation across 18 scenarios shows that both heuristics generate feasible solutions suitable as baselines for routing decisions. On average, the Insertion Heuristic achieves 13 % higher loading meter utilization (83 % compared to 69 % for the Nearest Neighbor Heuristic) and requires fewer tours, while overall travel times remain nearly identical between the two methods.Overall, the study demonstrates that heuristic methods provide systematic and time-efficient support for inbound routing in procurement logistics, offering a foundation for practical decision-making and further methodological refinements
Synthetic Datasets for Data-Driven Localization Monitoring
Full text linkReliable self-localization is fundamental to safe and efficient navigation in autonomous mobile robots and driverless industrial trucks. However, localization failures in highly dynamic or feature-poor environments can lead to safety hazards and costly workflow disruptions. While probabilistic methods such as particle filters mitigate sensing and actuation uncertainties, they lack mechanisms to recognize impending failures. To address this gap, we propose a systematic, physics-based simulation methodology for generating datasets that enable predictive failure detection. The datasets include localization estimates, ground-truth poses, sensor data, and automatically labeled failure cases. By systematically introducing challenging conditions, such as dynamic obstacles, featureless areas, and map ambiguities, we provoke diverse failure modes in a reproducible manner. These datasets establish a scalable foundation for training models that anticipate localization failures, supporting proactive fault detection and enhancing the safety and reliability of autonomous navigation in complex environments.Reliable self-localization is fundamental to safe and efficient navigation in autonomous mobile robots and driverless industrial trucks. However, localization failures in highly dynamic or feature-poor environments can lead to safety hazards and costly workflow disruptions. While probabilistic methods such as particle filters mitigate sensing and actuation uncertainties, they lack mechanisms to recognize impending failures. To address this gap, we propose a systematic, physics-based simulation methodology for generating datasets that enable predictive failure detection. The datasets include localization estimates, ground-truth poses, sensor data, and automatically labeled failure cases. By systematically introducing challenging conditions, such as dynamic obstacles, featureless areas, and map ambiguities, we provoke diverse failure modes in a reproducible manner. These datasets establish a scalable foundation for training models that anticipate localization failures, supporting proactive fault detection and enhancing the safety and reliability of autonomous navigation in complex environments
Heavy-duty conveyor modules for safe interaction between people, forklift vehicles and goods in intralogistics
Full text linkAs technical developments in intralogistics continue to advance, the production efficiency of large corporations and companies is also increasing. Their warehouses are becoming increasingly fully automated, while small and medium-sized enterprises (SMEs) are still heavily reliant on manual intralogistics processes. To enable SMEs to remain competitive, this research is directed towards a multidirectional heavy-duty conveyor module that is designed to support warehouse staff by automatically moving goods across the warehouse floor in parallel with existing operations. Individual modules should function by means of plug-and-play to allow the simplest possible integration and maintenance. In addition to mechanical load requirements for individual modules, a further challenge lies in path planning and control to ensure an efficiently and collision-free operation of the collective modular floor. Therefore, two questions will be answered in this paper: First, how does such a modular tile look like and what does it consist of? Second, what must the overall control concept look like in order to achieve an increase in efficiency for SMEs? In this paper a mechanical concept for an innovative new modular transport method for warehouse settings is presented. Additionally, the corresponding control concept is derived. Key factors to consider in the development of the mechanical part are the method of transportation with a limited budget such that an SME is able to profit off of it. Furthermore, the module is tasked with transporting goods weighting a couple of tons and enduring weights of loaded forklifts when not being used actively, thus enabling a restrictive-free floor where workers and forklifts can operate freely. To guarantee the latter part the controlling system must be able to avoid collisions with humans and other goods while still performing their own tasks. At the end of this paper the concept of a promising heavy-duty conveyor module with a corresponding movement algorithm is achieved
Condition-Based Lubrication of Roller Chains
Full text linkDie Schmierung von Rollenketten stellt einen wichtigen Instandhaltungsapekt dar, um sowohl Verschleißfortschritt als auch Ger¨auschentwicklung und Reibungsverluste zu reduzieren. Die richtige Schmierstoffmenge bereitzustellen, ohne dabei Trockenlauf oder ¨uberm¨aßigen Schmierstoffverbrauch zu riskieren, bedarf Informationen ¨uber den aktuellen Schmierzustandder Kette. In diesem Beitrag werden zwei Methoden pr¨asentiert, um den Schmierzustand einer Rollenkette im laufenden Betrieb zu erfassen und zur Steuerung eines automatischen Kettenschmiersystems zu verwenden. Die Ergebnisse zeigen, dass zustandsbasierte Schmierung sowohl den Verschleiß der Kette als auch den Schmierstoffverbrauch im Vergleich zumanuellem oder zeitgesteuertem Schmieren reduzieren kann.Lubrication of roller chains is an important maintenance aspect to reduce wear progress as well as noise emissions and friction losses. Supplying the right amount of lubricant to the chain joints without risking dry operation or wasting large amounts of lubricant requires knowledge of the current lubrication state of the chain. This paper presents two methods to detect the lubrication state of a roller chain during operation and use this data to control an automatic chain lubrication system. The results show that condition-based lubrication can reduce chain wear and lubricant consumption in comparison to manual or time-based lubrication