Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation
Mobile operation belongs to the innovation of a business model. At present, the management form of mobile operation is still in manual management, and there are many problems in manual management. In order to solve the current situation, an optimized scheduling model for power mobile jobs based on i...
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IEEE
2024-01-01
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| Series: | IEEE Access |
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| Online Access: | https://ieeexplore.ieee.org/document/10400483/ |
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| author | Rui Fan Huiying Jing Zhixin Jing |
| author_facet | Rui Fan Huiying Jing Zhixin Jing |
| author_sort | Rui Fan |
| collection | DOAJ |
| description | Mobile operation belongs to the innovation of a business model. At present, the management form of mobile operation is still in manual management, and there are many problems in manual management. In order to solve the current situation, an optimized scheduling model for power mobile jobs based on improved genetic algorithm is proposed. The model’s objective is to enhance scheduling efficiency and accuracy of power mobile jobs while minimizing automation, scheduling costs, and fault impact. The research conducts simulation experiments to validate the model’s efficacy. In the model considering the total task completion time and the cost of idle hours, the algorithm performance of the model is significantly proposed. When the model has completed around 70 iterations, it converges and maintains a fitness value in the range of 600 to 800. In the task assignment of the model, the total task completion time is shortened by 3 hours, the task assignment of each team is more uniform, and the path planning of each team is more reasonable. The research utilizes a genetic algorithm to intelligently schedule human resources, automating the scheduling process and achieving the lowest cost for completing the work. |
| format | Article |
| id | doaj-art-432f1af29b28423692ab362070640035 |
| institution | Kabale University |
| issn | 2169-3536 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-432f1af29b28423692ab3620706400352025-01-28T00:00:54ZengIEEEIEEE Access2169-35362024-01-0112109461096010.1109/ACCESS.2024.335436910400483Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile OperationRui Fan0https://orcid.org/0009-0003-8086-4449Huiying Jing1Zhixin Jing2Marketing Department, State Grid East Inner Mongolia Electric Power Supply Company Ltd., Hohhot, ChinaDevelopment Planning Department, State Grid East Inner Mongolia Electric Power Supply Company Ltd., Hohhot, ChinaMarketing Department, State Grid East Inner Mongolia Electric Power Supply Company Ltd., Hohhot, ChinaMobile operation belongs to the innovation of a business model. At present, the management form of mobile operation is still in manual management, and there are many problems in manual management. In order to solve the current situation, an optimized scheduling model for power mobile jobs based on improved genetic algorithm is proposed. The model’s objective is to enhance scheduling efficiency and accuracy of power mobile jobs while minimizing automation, scheduling costs, and fault impact. The research conducts simulation experiments to validate the model’s efficacy. In the model considering the total task completion time and the cost of idle hours, the algorithm performance of the model is significantly proposed. When the model has completed around 70 iterations, it converges and maintains a fitness value in the range of 600 to 800. In the task assignment of the model, the total task completion time is shortened by 3 hours, the task assignment of each team is more uniform, and the path planning of each team is more reasonable. The research utilizes a genetic algorithm to intelligently schedule human resources, automating the scheduling process and achieving the lowest cost for completing the work.https://ieeexplore.ieee.org/document/10400483/Automationdelay lossesgenetic algorithmoverdue losspower mobile operationscheduling optimization |
| spellingShingle | Rui Fan Huiying Jing Zhixin Jing Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation IEEE Access Automation delay losses genetic algorithm overdue loss power mobile operation scheduling optimization |
| title | Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation |
| title_full | Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation |
| title_fullStr | Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation |
| title_full_unstemmed | Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation |
| title_short | Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation |
| title_sort | application of optimal scheduling model based on improved genetic algorithm in electric power mobile operation |
| topic | Automation delay losses genetic algorithm overdue loss power mobile operation scheduling optimization |
| url | https://ieeexplore.ieee.org/document/10400483/ |
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