Articles

Lifecycle architectures for rental fleets couple pricing, vehicle assignment, maintenance scheduling and disposal timing, and the profit they add over revenue-based scoring has two sources that behave differently. Mileage steering changes the odometer reading at which a vehicle reaches disposal, and decision-focused forecast training reduces ranking regret at that point. Both sources matter only where the cost-optimal disposal mileage lies within a band around an odometer mark at which wholesale prices drop. The band half-width equals the residual-value forecast error divided by the slope of the retention margin. On realized retirements of midsize sedans at one enterprise rental operator, 28.2% of vehicles fall inside the band. Coupling adds $206 to $228 per vehicle there and $7 to $13 elsewhere, differences indistinguishable from zero. Mileage steering supplies 60.7% of the premium and survives a step-aware forecaster; the decision-focused component falls from $67.60 to $8.20 once the forecaster learns the price drops. Richer telematics raise profit by $86 to $118 per vehicle in every cell, so architecture and data are separable contributions. Operators can restrict the cost of coupling to vehicles inside the band.

Fleet management systems increasingly fold a vehicle's predicted mechanical condition into the same decision that assigns it to a delivery, a route, or a shift, treating the health signal as one more input alongside distance and time windows. This article derives the condition under which that practice improves fleet-level outcomes instead of relocating cost from one part of the fleet to another. Two bodies of prior work bear on the question without meeting: optimization research that couples maintenance forecasts with routing decisions treats forecast reliability as fixed, while cost-benefit research on prognostics quantifies how forecast error erodes value only up to the maintenance-scheduling decision. Reading the two together yields a closed-form breakeven inequality linking a diagnostic classifier's recall and false-positive rate to the ratio between a missed-failure cost and a false-exclusion cost, scaled by the fleet's base fault rate. Applied to published component-level classifier performance and to two documented vehicle telemetry regimes, the inequality holds comfortably for every heavy-duty component category under baseline operating costs, producing a positive net benefit at each, but flips sign for a constrained on-board-diagnostic regime once exclusion cost and base fault rate shift toward values realistic for a tightly scheduled light-duty fleet, generating a measured net loss per dispatched vehicle. The result identifies fleet heterogeneity in sensing infrastructure as the variable that determines whether condition-aware dispatch pays for itself.

The growth in demand for rail transportation systems within cities, together with high-speed and long-distance transportation running on a rail network, raises the issues of both rail safety and security. If an accident or an attack occurs, its consequences can be extremely severe. To mitigate the impact of these events, the real-time monitoring of a rail system is required. In that case, the improvements in monitoring can be achieved using artificial intelligence algorithms such as neural networks. Neural networks have been used to achieve real-time incident identification in monitoring the track quality in terms of classifying the graphical outputs of an ultrasonic system working with the rails and track bed, to predict incidents on the rail infrastructure due to transmission channels becoming blocked, and also to attempt scheduling preemptive and preventative maintenance. In terms of forecasting incidents and accidents on board the trains, neural networks have been used to model passenger behavior and optimize responses during a train station evacuation. In tackling the incidents and accidents occurring on rail transport, we contribute with two methodologies to detect anomalies in real-time and identify the level of security risk: at the maintenance level with personnel operating along the railways, and onboard passenger trains. These methodologies were evaluated on real-world datasets and shown to be able to achieve a high accuracy in the results. The results generated from these case studies also reveal the potential for network-wide applications, which could enhance security and safety on railway networks by offering the possibility of better managing network disruptions and more rapidly identifying security issues. The speed and coverage of the information generated through the implementation of these methodologies have implications in utilizing prediction for decision support and enhancing safety and security on board the rail network.

Managing supply chains efficiently has become a major concern for organizations. One of the important factors to optimize in supply chain management is logistics. The advent of technology and the increase in data availability allow for the enhancement of the efficiency of logistics in a supply chain. This discussion focuses on the blending of analytics with innovation in logistics to improve the operations of a supply chain. An approach is presented on how predictive analytics can be used to improve logistics operations. In order to analyze big data in logistics effectively, an artificial intelligence computational technique, specifically deep learning, is employed. Two case studies are illustrated to demonstrate the practical employability of the proposed technique. This reveals the power and potential of using predictive analytics in logistics to project various KPI values ahead in the future based on the contemporary data from the logistics operations; sheds light on the innovative technique of employing deep learning through deep learning-based predictive analytics in logistics; suggests incorporating innovative techniques like deep learning with predictive analytics to develop an accurate forecasting technique in logistics and optimize operations and prevent disruption in the supply chain. The network of supply chains has become more complex, necessitating the need for the latest technological advancements. The sectors that have gained a fair amount of attention for the application of technology to optimize their operations are manufacturing, healthcare, aerospace, and the automotive industry. A little attention has been diverted to the logistics sector; many describe how analytics and artificial intelligence can be used in the logistics sector to achieve higher optimization. Currently, significant research has been done in optimizing logistics operations. Nevertheless, with the explosive volume of historical data being produced by the logistics operations of an organization, there is a great opportunity to learn valuable insights from the data accumulated over time for more long-term strategic planning. To develop the logistics operations in an organization, the use of historical data is essential to understand the trends in the operations. For example, regular maintenance planning and resource allocation based on trends are long-term activities that will not affect logistics operations immediately but can affect the business’s strategic planning in the long run. A predictive analysis technique employed on historical data of logistics can narrow down conclusions based on the future trends of logistics operations. Thus, the technique can be used to prevent the disruption of the supply chain.