To reduce airport operating costs and minimize environmental pollution, converting ground-handling vehicles from fuel-powered to electric ones is inevitable. However, this transformation introduces complexity in sched...
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To reduce airport operating costs and minimize environmental pollution, converting ground-handling vehicles from fuel-powered to electric ones is inevitable. However, this transformation introduces complexity in scheduling due to additional factors, such as battery capacities and charging requirements. This study models the electric ground-handling vehicle scheduling problem as a bi-objective integer programming model to address these challenges. The objectives of this model are to minimize the total travel distance of vehicles serving flights and the standard deviation of the total occupancy time for each vehicle. In order to solve this model and generate optimal scheduling solutions, this study combines the non-dominated sorting genetic algorithm 2 (NSGA2) with the large neighborhood search (LNS) algorithm, proposing a novel NSGA2-LNS algorithm. A dynamic priority method is used by the NSGA2-LNS to construct the initial population, thereby speeding up the convergence. The NSGA2-LNS employs the LNS algorithm to overcome the problem that metaheuristic algorithms often lack clear directions in the process of finding solutions. In addition, this study designs the correlation-based destruction operator and the priority-based repair operator in the NSGA2-LNS algorithm, thereby significantly enhancing its ability to find optimal solutions for the electric ground-handling vehicle scheduling problem. The algorithm is verified using flight data from Chengdu Shuangliu International Airport and is compared with manual scheduling methods and traditional multi-objective optimization algorithms. Experimental results demonstrate that the NSGA2-LNS can rapidly solve the scheduling problem of allocating electric ground-handling vehicles for hundreds of flights and produce high-quality scheduling solutions.
The objective of the present study is to develop a Customized Automated Machine Learning (CAML) framework to support machine learning models in manufacturing processes. The proposed CAML framework features a user-frie...
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The objective of the present study is to develop a Customized Automated Machine Learning (CAML) framework to support machine learning models in manufacturing processes. The proposed CAML framework features a user-friendly interface that enables users to perform key tasks and predict outcomes based on user-defined process parameters. In the present study, machine learning-based prediction of output responses is employed to estimate the process parameters during the cold extrusion process. The input responses are Die angle, Ram speed and Coefficient of Friction while the output responses are Extrusion Force, Damage Factor, Displacement of work piece and Extrusion Time. The framework utilized various machine learning algorithms, including Linear Regression, Ridge, Lasso, Elastic Net, Polynomial Regression, Gaussian Process Regressor, XGB Regressor, LGBM Regressor, Random Forest, Gradient Boosting Regressor, AdaBoost Regressor, Bagging Regressor, Extra-Trees Regressor, KNN Regressor, Nu SVR, Support Vector Regression (SVR), Kernel Ridge, RANSAC Regressor, Huber Regressor, LarsCV, Orthogonal Matching Pursuit, and Bayesian Ridge. The evaluation was performed by analyzing the aggregate R-2 score and aggregate Root Mean Absolute Error (RMSE). Additionally, for optimization, a machine learning-based algorithm, Multimodal Optimization NSGA-2 (non-dominated sorting genetic algorithm 2), is employed to predict extrusion process parameters and enhance the efficiency of the actual extrusion operation. This approach bridges the gap between simulation results and real-world production systems. For Extrusion Force, Displacement of work piece and Extrusion Time. accuracies are 99.38%, 99.80%, and 99.25% respectively, with error percentages below 1% for GBR. However, the Damage Factor, with smaller values 0.017, 0.014), showed higher error (8.24%), where XGBR proved more consistent. The training of all machine learning models (MLMs) took 17.89645 s based on R2 and 18.9131 s based on R
The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. geneticalgorithms are applied to this purpose and three different popular algorithms capable to deal w...
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The paper presents strategies optimization for an existing automated warehouse located in a steelmaking industry. geneticalgorithms are applied to this purpose and three different popular algorithms capable to deal with multi-objective optimization are compared. The three algorithms, namely the Niched Pareto geneticalgorithm, the non-dominated sorting genetic algorithm 2 and the Strength Pareto geneticalgorithm2, are described in details and the achieved results are widely discussed;moreover several statistical tests have been applied in order to evaluate the statistical significance of the obtained results.
In this paper, we study the Reporting Cells scheme, a popular strategy used to control the movement of mobile terminals in the Public Land Mobile Networks. In contrast to previously published works, we propose a multi...
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ISBN:
(纸本)9783319076171;9783319076164
In this paper, we study the Reporting Cells scheme, a popular strategy used to control the movement of mobile terminals in the Public Land Mobile Networks. In contrast to previously published works, we propose a multiobjective approach that allows us to avoid the drawbacks of the linear aggregation of the objective functions. Furthermore, we provide a novel formulation to take into account aspects of the Reporting Cells that have not been considered in previous works. Experimental results show that our proposal outperforms other optimization techniques published in the literature.
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