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Wyświetlanie 1-2 z 2
Tytuł:
A coordinated optimization of rewarded users and employees in relocating station-based shared electric vehicles
Autorzy:
Yu, Lan
Liu, Jiaming
Sun, Zhuo
Powiązania:
https://bibliotekanauki.pl/articles/2172124.pdf
Data publikacji:
2022
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
shared electric vehicle
user reward mechanism
collaborative relocating
SCE–UA
wspólny pojazd elektryczny
mechanizm nagradzania użytkowników
Opis:
To solve the mismatch between the supply and demand of shared electric vehicles (SEVs) caused by the uneven distribution of SEVs in space and time, an SEV relocating optimization model is designed based on a reward mechanism. The aim of the model is to achieve a cost-minimized rebalancing of the SEV system. Users are guided to attend the relocating SEVs by a reward mechanism, and employees can continuously relocate multiple SEVs before returning to the supply site. The optimization problem is solved by a heuristic column generation algorithm, in which the driving routes of employees are added into a pool by column generation iteratively. In the pricing subproblem of column generation, the Shuffled Complex Evolution–University of Arizona (SCE–UA) is designed to generate a driving route. The proposed model is verified with the actual data of the Dalian city. The results show that our model can reduce the total cost of relocating and improve the service efficiency.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2022, 32, 4; 523--535
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
Tytuł:
An effective data reduction model for machine emergency state detection from big data tree topology structures
Autorzy:
Iaremko, Iaroslav
Senkerik, Roman
Jasek, Roman
Lukastik, Petr
Powiązania:
https://bibliotekanauki.pl/articles/2055178.pdf
Data publikacji:
2021
Wydawca:
Uniwersytet Zielonogórski. Oficyna Wydawnicza
Tematy:
OPC UA
OPC tree
principal component analysis
PCA
big data analysis
data reduction
machine tool
anomaly detection
emergency states
analiza głównych składowych
duży zbiór danych
redukcja danych
wykrywanie anomalii
stan nadzwyczajny
Opis:
This work presents an original model for detecting machine tool anomalies and emergency states through operation data processing. The paper is focused on an elastic hierarchical system for effective data reduction and classification, which encompasses several modules. Firstly, principal component analysis (PCA) is used to perform data reduction of many input signals from big data tree topology structures into two signals representing all of them. Then the technique for segmentation of operating machine data based on dynamic time distortion and hierarchical clustering is used to calculate signal accident characteristics using classifiers such as the maximum level change, a signal trend, the variance of residuals, and others. Data segmentation and analysis techniques enable effective and robust detection of operating machine tool anomalies and emergency states due to almost real-time data collection from strategically placed sensors and results collected from previous production cycles. The emergency state detection model described in this paper could be beneficial for improving the production process, increasing production efficiency by detecting and minimizing machine tool error conditions, as well as improving product quality and overall equipment productivity. The proposed model was tested on H-630 and H-50 machine tools in a real production environment of the Tajmac-ZPS company.
Źródło:
International Journal of Applied Mathematics and Computer Science; 2021, 31, 4; 601--611
1641-876X
2083-8492
Pojawia się w:
International Journal of Applied Mathematics and Computer Science
Dostawca treści:
Biblioteka Nauki
Artykuł
    Wyświetlanie 1-2 z 2

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