Collaborative truck-drone delivery is a crucial model of drone involvement in urban logistics, addressing drone limitations in load capacity and endurance. However, regional constraints, including damage, blockades, p...
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Collaborative truck-drone delivery is a crucial model of drone involvement in urban logistics, addressing drone limitations in load capacity and endurance. However, regional constraints, including damage, blockades, pollution, and epidemics, pose routing challenges for trucks and drones. This study integrates regional restrictions into the heterogeneous truck-drone routing problem, presenting a mixed-integer programming model for cost minimization. To tackle complexity, we introduce an enhanced graywolfoptimizationalgorithm (EGWO), which improves the initial solution through partition scanning and a heuristic insertion algorithm. EGWO effectiveness is confirmed through enhancements in the standard test library. On average, the heterogeneous truck-drone model achieves a 28.31% cost reduction compared to the single-type truck delivery model. Moreover, deep insights into the impacts of multi-type trucks, the number of no-fly zones and the number of restricted traffic zones on the performance of the heterogeneous truck-drone system are discussed.
With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people&...
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With the acceleration of global climate change and urbanization, the frequency and impact of flood disasters are increasing year by year, making flood emergency management increasingly crucial for safeguarding people's lives, property, and societal stability. To enhance the accuracy of river flow prediction, this study employs an improved gray wolf optimization algorithm (IGWO) to optimize parameters of the Long Short-Term Memory Network (LSTM) model. Experimental results demonstrate that the proposed algorithm significantly improves the accuracy of river flow prediction, achieving higher precision and better generalization compared to traditional machine learning algorithms. This method provides more reliable data support for flood warning systems, aiding in the accurate prediction of flood occurrence timing and intensity, thereby providing scientific basis for flood prevention and mitigation efforts. Moreover, this approach supports hydro-logical research, enhancing understanding of river water cycle processes and ecosystem changes.
Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning ***,this study proposes a cascade co...
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Under ultra-high-speed and harsh conditions,conventional control methods struggle to ensure the path tracking accuracy and driving stability of unmanned vehicles during the turning ***,this study proposes a cascade control to solve this *** on the new vehicle error model that considers vehicle tire sideslip and road curvature,the feedforward-parametric adaptive linear quadratic regulator(LQR)and proportional integral control-based speed-keeping controllers are used to compose the path-tracking cascade optimization controller for unmanned *** improve the adaptability of the unmanned vehicle path-tracking control under harsh driving conditions,the LQR controller parameters are automatically adjusted using a back-propagation neural network,in which the initial weights and thresholds are optimized using the improved grey wolfoptimizationalgorithm according to the driving *** speed-keeping controller reduces the impact on the curve-tracking accuracy under nonlinear vehicle speed ***,a joint model of MATLAB/Simulink and CarSim was established,and simulations show that the proposed control method can achieve stable entry and exit curves at ultra-high speeds for unmanned *** strong wind and ice road conditions,the method exhibits a higher tracking accuracy and is more adaptive and robust to external interference in driving and variable curvature roads than methods such as the feedforward-LQR,preview and pure pursuit controls.
Recently, with the strategic requirements of sustainable development, more and more attention has been paid on the green scheduling problems. Different from the traditional flow-line scheduling problem, the ship plane...
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Recently, with the strategic requirements of sustainable development, more and more attention has been paid on the green scheduling problems. Different from the traditional flow-line scheduling problem, the ship plane block possesses the characteristics of larger volume and weight, so the problem of ship plane block flow-line can be regarded as a blocking flow-line scheduling problem (BFSP). In this paper, considering the complexity of ship building and the particularity of green scheduling problem (e.g., carbon emission and noise of equipment), the green blocking flow-line scheduling problem model of panel block (GBFSP) in shipbuilding was established to minimize the maximum completion time, carbon emission cost and noise cost. Moreover, the improvedgraywolfoptimization (IGWO) algorithm was proposed to solve this problem effectively. In the proposed IGWO algorithm, a ranked order value (ROV) method was performed to realize the transformation from continuous graywolf individual position to discrete optimal solution. Secondly, a nonlinear convergence factor and PSO algorithm were introduced to balance the development and exploration ability of the IGWO algorithm. In addition, variable neighborhood search (VNS) was also used to improve the accuracy and effectiveness of local search. Furthermore, the validity of the proposed IGWO algorithm is verified by some famous benchmark examples. In addition, a real data of a shipyard was used for example verification and multiple numerical experiments. Our results suggested that the model and the IGWO algorithm can solve the problems existing in the ship plane block flow-line effectively. Meanwhile, reducing the impact of pipeline on the environment, and achieving the goal of green production.
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