Application of Optimal Scheduling Model Based on Improved Genetic Algorithm in Electric Power Mobile Operation

Abstract

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.