Neural Network-Based Metaheuristic Parameterization with Application to the Vehicle Matching Problem in Ride-Hailing Services

Syed, Arslan Ali; Gaponova, Irina; Bogenberger, Klaus

doi:10.1177/0361198119846099

Cited by 5 publications

(3 citation statements)

References 16 publications

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“…As a result, simple models such as decision trees are commonly used. [236] employed frequently used metaheuristics -algorithms that often have numerous parameters, which need to be adjusted to attain optimal performance. Therefore, they additionally proposed a neural network to predict the parameter values that work best for a specific problem instance, and then combining it with a large neighborhood search.…”

Section: ) Matchingmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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Shared mobility has emerged as a sustainable alternative to both private transportation and traditional public transport, promising to reduce the number of private vehicles on roads while offering users greater flexibility. Today, urban areas are home to a myriad of innovative services, including car-sharing, ride-sharing, and micromobility solutions like moped-sharing, bike-sharing, and e-scooter-sharing. Given the intense competition and the inherent operational complexities of shared mobility systems, providers are increasingly seeking specialized decision-support methodologies to boost operational efficiency. While recent research indicates that advanced machine learning methods can tackle the intricate challenges in shared mobility management decisions, a thorough evaluation of existing research is essential to fully grasp its potential and pinpoint areas needing further exploration. This paper presents a systematic literature review that specifically targets the application of Machine Learning for decision-making in Shared Mobility Systems. Our review underscores that Machine Learning offers methodological solutions to specific management challenges crucial for the effective operation of Shared Mobility Systems. We delve into the methods and datasets employed, spotlight research trends, and pinpoint research gaps. Our findings culminate in a comprehensive framework of Machine Learning techniques designed to bolster managerial decision-making in addressing challenges specific to Shared Mobility across various levels.

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Section: ) Matchingmentioning

confidence: 99%

A Systematic Literature Review on Machine Learning in Shared Mobility

Teusch,

Gremmel,

Koetsier

et al. 2023

IEEE Open J. Intell. Transp. Syst.

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“…It adopts vector control to control the excitation winding and terminal voltage, so as to control the torque. It is not the best choice in terms of cost and stability [13,14].…”

Section: Ac Induction Motormentioning

confidence: 99%

Parameterization and Performance of Permanent Magnet Synchronous Motor for Vehicle Based on Motor-CAD and OptiSLang

Yong-ling

2022

Wireless Communications and Mobile Computing

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With the popularity of electric vehicles, electric vehicles have become a hot research topic, and the performance of permanent magnet synchronous motor has become increasingly prominent in recent years. Therefore, the application of permanent magnet synchronous motor in electric vehicles has become a possibility. Based on the analysis of the existing permanent magnet synchronous motor technology for electric vehicles, this paper will start from the motor body design. In this paper, the performance parameters of permanent magnet synchronous motor meeting the requirements of vehicle operation are estimated by analyzing the parameters of electric vehicle, and the motor is modeled based on the motor design software Motor-CAD. In this paper, the parametric analysis module in the software is used to optimize the parameters of motor permanent magnet, stator slot width, air gap magnetic density, and air gap length. Finally, this paper uses OptiSLang software to simulate and analyze the permanent magnet synchronous motor. It improves the driving performance of automobile motor and reduces energy consumption, so that the permanent magnet synchronous motor can better meet the requirements of electric vehicles. The experimental results show that the optimal range of motor split ratio is large, in the range of 0.85~0.9.

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“…Given the size and dynamic nature of the ride-hailing matching problem in large cities, many approaches involve metaheuristic methods to generate sub-optimal solutions [18,19].…”

Section: Ride-hailing Matching and Rebalancingmentioning

confidence: 99%