logistics managers

Abstract: Physical Internet (PI) is an emerging concept that applies the digital internet as a design metaphor for the development of sustainable, interoperable, and collaborative freight transportation. It is considered a way to bring logistics into the next generation of transformation. Effective tools for organizing and integrating knowledge are essential for navigating the emerging research in this area. In this study, we explore the application of Graph Retrieval Augmented Generation (GraphRAG) in the context of PI, using GPT-4o mini and Neo4j to construct a knowledge graph and systematically analyze existing PI-related literature. Our approach synthesizes scattered research findings, highlights emerging trends, and identifies knowledge gaps. Furthermore, we demonstrate that GraphRAG improves accessibility by structuring complex information into interconnected graphs and provides a deeper understanding of underlying research dynamics. This research will contribute to future research and innovation by providing a new method of information analysis in the PI domain.

Abstract: Unmanned Aerial Vehicles (UAVs) performing deadline-bound analytics over large rural areas cannot reliably offload workloads to sparse cellular base stations. We propose an approach that uses scheduled public buses as \textit{mobile fogs}: a UAV hands off data to a bus during a halt, and the bus carries it until its route enters cellular coverage. Because a public bus follows a fixed route and timetable, a handover depends on \textit{where} and \textit{when} the bus next reaches a cellular zone, rather than how near the stop is. We formulate a Mission Scheduling Problem over this model, co-scheduling UAV routes with the placement of each analytics task on the UAV edge, a stationary fog, or a bus, under deadline, energy, and cost constraints. Our \textit{Divide and Assign} (DA) heuristic selects the cheapest halt that still meets a task's deadline. Across 36 workload configurations in a rural region, derived from real cellular and transit data, DA achieves up to 20\% higher utility than the strongest heuristic and up to 41\% higher utility than the strongest adapted-prior scheduler, while incurring the lowest aggregate cost. The transit tier handles up to 71\% of drop-offs, raises task completion to 100\%, and reduces recharging cycles by up to 31\%. Finally, in the presence of traffic variability, the adaptive variant recovers 93\% of the utility the delays cost and maintains completion rates above 97\%.

Abstract: A contract-logistics spare-parts operator is paid on order-level service: an order counts only if every requested line is fulfilled, yet forecasters are selected based on line-level forecast accuracy. This disconnect matters when demand is intermittent and lumpy, histories are short, and lead times span months. We benchmarked 38 forecasting methods spanning classical, intermittent-demand, machine-learning, deep-learning, and pretrained foundation models. A common decision-aware protocol evaluates them on an industrial panel drawn from a live contract and two public datasets. Forecast-accuracy rank and order-service rank are negatively correlated on the industrial panel, at -0.555, across methods evaluated on 20,330 real multi-item orders. Service is more closely associated with the direction of cumulative forecast bias, including over-prediction during zero-demand periods, than with point accuracy. Examining bias in Chronos-2's instance normalization yields a training-free correction that lifts the per-material fill proxy from 77.5% to 92.0% (14.5 percentage points) at the 90% policy target and raises the complete-order fill rate from 54% to 63%. For reproducibility, we release RUF (Regenerate-Until-Fidelity), a method for generating fidelity-certified synthetic panels on which the findings reproduce. For intermittent demand, the lowest-error forecast need not deliver the highest service. Bias direction helps explain this gap, which can be reduced without retraining.