| China’s telecommunications industry is gradually strengthening network construction,actively promoting the strategy of strengthening the network power.In order to implement the speed-up and cost-reduction,it is of great significance to study the maintenance of base stations in network operation and maintenance.By the end of 2019,the total number of communication base stations deployed in China has exceeded 8 million,and the revenue of telecommunication business has exceeded 1.31 trillion yuan.Based on the analysis of a large number of relevant literature at home and abroad,this paper focuses on the study of the optimization of "comprehensive dispatch of field operations" in the field of base station maintenance in network operation and maintenance,hoping to improve the operation and maintenance efficiency of the base station,reduce the maintenance cost of the base station,and reduce the communication charges.By analyzing the influencing factors and processing flow of base station maintenance,it is found that this is a real-time multi-objective and multi-farm routing problem that needs to consider the class of work orders.It is necessary to simplify and reduce the dimension of the problem by applying adaptive time window step scheduling strategy in combination with space-time data technology.This paper elaborates the process of building the model of field job comprehensive dispatch and each link of solving the model,in which the genetic algorithm is applied to solve the problem.In addition,in order to further improve the solving efficiency,a new genetic algorithm which improves the crossover operator is proposed and implemented to replace the standard genetic algorithm in the solving process.In view of the current theoretical and engineering status in the field of field job scheduling optimization in network operation and maintenance,the main research contents and work of this paper include the following:(1)Spatial-temporal data analysis.Combining with space-time data technology,it is a prerequisite for comprehensive scheduling of field operations to clarify the status of each influencing factor,that is,maintenance personnel,tools,accessories,real-time location of vehicles,parking status,types of work orders and service skill level requirements.(2)Establishment and solution of the model for comprehensive dispatch of field operations.Firstly,by analyzing the influence factors and operation process of base station maintenance in network operation and maintenance,the problem assumption and process simplification are completed.Secondly,the optimization objective function is set,various constraints are refined,and a comprehensive scheduling mathematical model for field operations is obtained.Again,the adaptive time window step-by-step scheduling strategy suitable for model solving is applied to divide the problem into N easy-to-solve subproblems,do a good job of coding and combine with the actual project data design and experiment of a large generation and maintenance enterprise.Finally,the genetic algorithm is applied to solve the sub-problems,which provides a new idea and means of implementation for the field job comprehensive scheduling problem.(3)Research on the improvement of swarm intelligence algorithm.Because of the limitations of genetic algorithm in the process of solving the comprehensive scheduling model of field jobs.On the basis of studying all kinds of heuristic algorithms,this paper summarizes all kinds of improvement ideas,studies the balance between global optimization ability and convergence speed of swarm intelligence algorithm,puts forward and implements an improved theory based on reward and penalty coefficients and a genetic algorithm based on improved crossover operator.After testing,the performance of this algorithm is significantly improved on some benchmark test functions,which is supplemented by Experiments show that the improved crossover operator genetic algorithm can further improve the efficiency of field job comprehensive scheduling model. |
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