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Open Access

Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles

Yinan Guo1, Yao Huang2, Shirong Ge1( ), Yizhe Zhang2, Ersong Jiang2, Bin Cheng2, Shengxiang Yang3
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China, and also with the Inner Mongolia Research Institute, China University of Mining and Technology (Beijing), Ordos 017010, China
School of Mechanical Electronic and Information Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China
Institute of Artificial Intelligence, School of Computer Science and Informatics, De Montfort University, Leicester, LE1 9BH, UK
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Abstract

Trackless rubber-tyerd vehicles are the core equipment for auxiliary transportation in inclined-shaft coal mines, and the rationality of their routes plays the direct impact on operation safety and energy consumption. Rich studies have been done on scheduling rubber-tyerd vehicles driven by diesel oil, however, less works are for electric trackless rubber-tyred vehicles. Furthermore, energy consumption of vehicles gives no consideration on the impact of complex roadway and traffic rules on driving, especially the limited cruising ability of electric trackless rubber-tyred vehichles (TRVs). To address this issue, an energy consumption model of an electric trackless rubber-tyred vehicle is formulated, in which the effects from total mass, speed profiles, slope of roadways, and energy management mode are all considered. Following that, a low-carbon routing model of electric trackless rubber-tyred vehicles is built to minimize the total energy consumption under the constraint of vehicle avoidance, allowable load, and endurance power. As a problem-solver, an improved artificial bee colony algorithm is put forward. More especially, an adaptive neighborhood search is designed to guide employed bees to select appropriate operator in a specific space. In order to assign onlookers to some promising food sources reasonably, their selection probability is adaptively adjusted. For a stagnant food source, a knowledge-driven initialization is developed to generate a feasible substitute. The experimental results on four real-world instances indicate that improved artificial bee colony algorithm (IABC) outperforms other comparative algorithms and the special designs in its three phases effectively avoid premature convergence and speed up convergence.

Keywords

electric trackless rubber-tyred vehicleslow-carbonroutingartificial bee colony algorithm

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Complex System Modeling and Simulation
Volume 3 Issue 3,
September 2023
Pages 169-190
DOI: 10.23919/CSMS.2023.0011
Cite this article:
Guo Y, Huang Y, Ge S, et al. Low-Carbon Routing Based on Improved Artificial Bee Colony Algorithm for Electric Trackless Rubber-Tyred Vehicles. Complex System Modeling and Simulation, 2023, 3(3): 169-190. https://doi.org/10.23919/CSMS.2023.0011

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Received: 01 March 2023
Revised: 05 April 2023
Accepted: 02 May 2023
Published: 02 August 2023
© The author(s) 2023.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).
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