Drone-assisted parcel delivery to remote islands is increasingly replacing traditional methods, offering improved efficiency and enhanced service reliability. This paper addresses the drone scheduling problem in islan...
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Drone-assisted parcel delivery to remote islands is increasingly replacing traditional methods, offering improved efficiency and enhanced service reliability. This paper addresses the drone scheduling problem in island delivery (DSP-ID) by optimising drone delivery routes. In particular, we first introduce a bi-objective mixed-integer linear programming model that concurrently optimises delivery time and energy consumption. To address the model, both a heuristic non-dominated sorting genetic algorithm II (NSGA-II) and an exact augmented epsilon-constraint method are developed. The efficacy and robustness of the proposed model and algorithms are evaluated through experiments across various scales. Results indicate that both algorithms yield high-quality solutions for DSP-ID in small-scale scenarios. However, as the problem size expands, the performance of the augmented epsilon-constraint method wanes under time constraints, whereas the NSGA-II consistently delivers high-quality solutions. Additionally, we provide decision-makers with actionable insights for selecting the most effective drone delivery routes.
Signal control strategy has a huge effect not only on intersection operations but also on traffic emissions. The basic assumptions are first proposed. Using the second-by-second data of vehicular velocity and accelera...
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Signal control strategy has a huge effect not only on intersection operations but also on traffic emissions. The basic assumptions are first proposed. Using the second-by-second data of vehicular velocity and acceleration, the mathematical expressions are then presented to calibrate the emission factors during green/red on the basis of vehicle-specific power. Given some necessary constraints, a single-objectiveoptimisationmodel and a bi-objective optimisation model are formulated with the concern of traffic emissions. Considering three levels of traffic demands and two methods of signal timing, the numerical examples are carried out by utilising the VISSIM and MATLAB software packages. The findings indicate that the emission factors during green are explicitly greater than those during red and they are all stable for each pollutant and for each lane group;a scientific signal control system can simultaneously reduce vehicle delay and traffic emissions for isolated intersections.
With the rapid development of novel vehicle technologies, electric vertical take-off and landing (eVTOL) vehicles are becoming a new transport servicing model to achieve better urban air mobility (UAM) systems. The UA...
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With the rapid development of novel vehicle technologies, electric vertical take-off and landing (eVTOL) vehicles are becoming a new transport servicing model to achieve better urban air mobility (UAM) systems. The UAM systems can efficiently utilise low-altitude airspace resources, providing a solution to alleviate congestion in urban ground traffic. This paper delves into an integrated optimisation problem aimed at addressing decision-making processes related to the strategic planning and service operation of UAM systems, considering both demand uncertainty and spatial equity. The problem encompasses various decision components, including parking stand numbers at vertiports and vertistops, eVTOL fleet sizing as well as eVTOL fleet allocation and operations. Additionally, we introduce a spatial equity metric and establish a bi-objective optimisation model to balance the trade-off between service profitability and spatial equity considerations. We transform the bi-objective optimisation model to a tractable single-objective formulation using epsilon-constraint approach and linearisation technique. In this paper, we employ a scenario-based robust optimisation framework that incorporates the interval robust method to capture the demand uncertainty, enhancing resilience against uncertain factors. We evaluate the model performance using a small-scale example and further validate the proposed model through a real-world case study. Numerical analysis results demonstrate that the scenario-based robust optimisation framework can ensure the robustness of decision-making against the effect of uncertain conditions. Furthermore, numerical experiments reveal a trade-off between profitability and spatial equity, potentially requiring a partial sacrifice of profit to attain a desired equity level. Finally, we propose valuable policy recommendations to guide the decision-making processes of UAM service providers.
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