The Fleet Assignment Problem (FAP) of aircraft scheduling in airlines is studied, and the optimization model of FAP is proposed. The objective function of this model is revenue maximization, and it considers comprehensively the difference of scheduled flights and aircraft models in flight areas and mean passenger flows. In order to solve the model, a self-adapting genetic algorithm is supposed to solve the model, which uses natural number coding, adjusts dynamically crossover and mutation operator probability, and adopts intelligent heuristic adjusting to quicken optimization pace. The simulation with production data of an airline shows that the model and algorithms suggested in this paper are feasible and have a good application value.

Yaohua Li

Na Tan

Study on Fleet Assignment Problem Model and Algorithm

Imperfect data stream leads to tree size explosion and detrimental accuracy problems. Overfitting problem and the imbalanced class distribution reduce the performance of the original decision-tree algorithm for stream mining. In this paper, we propose an incremental optimization mechanism to solve these problems. The mechanism is called Optimized Very Fast Decision Tree (OVFDT) that possesses an optimized node-splitting control mechanism. Accuracy, tree size, and the learning time are the significant factors influencing the algorithm&#x2019;s performance. Naturally a bigger tree size takes longer computation time. OVFDT is a pioneer model equipped with an incremental optimization mechanism that seeks for a balance between accuracy and tree size for data stream mining. It operates incrementally by a test-then-train approach. Three types of functional tree leaves improve the accuracy with which the tree model makes a prediction for a new data stream in the testing phase. The optimized node-splitting mechanism controls the tree model growth in the training phase. The experiment shows that OVFDT obtains an optimal tree structure in both numeric and nominal datasets.

Hang Yang

Simon Fong

Incremental Optimization Mechanism for Constructing a Decision Tree in Data Stream Mining

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Mathematical Problems in Engineering is a broad-based journal publishing&nbsp;results of rigorous engineering research across all disciplines, carried out using mathematical tools.

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Chief Editor, Professor Guangming Xie, is currently a full professor of dynamics and control with the College of Engineering, Peking University. His research interests include complex system dynamics and control and intelligent and biomimetic robots.

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Mathematical Problems in Engineering

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Mathematical Problems in Engineering

Data Mining and Knowledge Discovery in Industrial Engineering

Data Mining and Knowledge Discovery in Industrial Engineering

Articles

Study on Fleet Assignment Problem Model and Algorithm

Incremental Optimization Mechanism for Constructing a Decision Tree in Data Stream Mining