Analysis And Optimization Of Dynamic Service System

Service systems in many areas of life will have queues,and it is often necessary to queue up when paying at the supermarket or calling the call center for business.The application of queuing theory to study and optimize the service system can reasonably allocate service resources and reduce queuing.This paper uses the queuing network model,approximate Bayesian calculation,and cost optimization model to study a common type of service system in life: this service system provides two stages of service,customers arrive at the service system in turn,all customers enter the first service node to receive the first stage of service,after the service is completed,some customers leave the service system directly;The other part of the customer enters the next service node to receive the second stage of service,and only leaves the service system after the completion of the two-stage service.This kind of service system is very common in computer communication,hospital visits,call centers and other fields,so this paper focuses on this type of service system: first,the queuing theory is used to establish a queuing network model for such service system and give expressions of queuing indicators such as average captain and average waiting time;Then,an approximate Bayesian algorithm is given to estimate the customer arrival rate and other parameters in the model,and the effectiveness of the method is verified by simulation simulation.Finally,starting from the interests of both the service system and customers,under the goal of minimizing the total cost of the system,a cost optimization model is constructed for such service systems,the optimal service rate of the two nodes of the service system is solved,and suggestions are made for the operation and management of the service system.In real life,the number of customers arriving at the service system at different times is not the same,and the number of customers arriving at certain times is far more than or below the average number,if the service system has been providing services at a fixed service rate,it will lead to a mismatch between service resources and customer demand,waste of service resources or crowded queues,especially when it is necessary to queue for a long time,customers will be dissatisfied or even give up service.Therefore,service system managers need to adjust service rates to respond to dynamically arriving service demand.In this paper,the phenomenon of secondary calls in the manual service stage of the call center system is empirically analyzed,and the analysis data characteristics find that customers dynamically arrive at the human service system,so according to the change of customer arrival,different time periods are divided,and the approximate Bayesian algorithm is used to estimate the queuing parameters,and the queuing network model is constructed to study the actual operation of the human service system.The cost optimization model is applied to optimize the manual service system,the optimal service rate of the two nodes in different time periods is calculated,and the operation status of the service system under the optimal service rate is analyzed,and the results show that the cost optimization model can reduce the total cost of the system,reduce the average captain and average waiting time to a certain extent,and improve the satisfaction of managers and customers.Based on the results of simulation and empirical analysis,the approximate Bayesian algorithm can accurately estimate the parameters of the queuing system and help build an accurate queuing network model.The cost optimization model can obtain the optimal service rate of the two nodes,provide the service system manager with operation and decision-making basis,reasonably arrange and adjust the service resources of the two nodes,and reduce the waste of resources and customer waiting loss of the service system.