Research On The Dynamic Scheduling Problem Of The Immunoassay Equipment Based On Multi-agent

With the development of science and technology,automatic immunoassay equipment replaces manual testing operations,because the degree of automation and accuracy of immunoassay equipment is improved.In order to further improve the efficiency of the process of immune testing,scholars have conducted research on the scheduling problem of immunoassay equipment.But most of the research is on the static scheduling problem.Actually,emergency events such as emergency immunoassay often occur in the process of testing.In this case,the process of testing needs to be dynamically scheduled.In this dissertation,the following research is completed for this scheduling problem:(1)According to the basic composition and work flow of immunoassay equipment,the similarity between immune test scheduling problem and job shop scheduling problem is analyzed,and the scheduling problem is extracted into a kind of dynamic job shop scheduling problem with parallel machines.The optimization objective and constraints of the problem are determined,and the mathematical model of the problem is established.(2)For the static scheduling problem when the information of the testing items is known and the dynamic scheduling problem with emergency events,a multi-agent based scheduling optimization system is established to realize the pre-reactive scheduling in the process of testing.In the system,the structure and functions of Management Agent,Scheduling Agent,Device Agents,Item Agents are designed in detail.The Management Agent is responsible for the global management of the system,and the Scheduling Agent is responsible for arranging the execution sequence of the project procedure.Referring to the robot operating system,the TSA(Topicservice-Action)communication mechanism of the multi-agent system is designed to realize the interaction and cooperation between different agents and the coordinated control of the system.(3)For the pre-scheduling problem in the process of testing,the hybrid algorithm ABCTLBO,which combines the artificial bee colony(ABC)and the teaching-learning-based optimization(TLBO),is studied as the pre-scheduling algorithm of immune testing.The algorithm is based on ABC's framework,and an improved TLBO with strong local search capability is embedded in the onlooker bees stage.At the same time,the crossover and mutation operators are selected appropriately at each stage of the algorithm.The experiments show that ABC-TLBO has a strong ability to optimize job shop scheduling problems and can be used for pre-scheduling of the immune testing.(4)For the dynamic scheduling problem with unexpected events in the testing process,the dynamic scheduling rules based on the hierarchical evolution gene expression programming(HE-GEP)and the contract network protocol are used to implement reactive scheduling.In order to make up for the shortcomings of GEP's weak local search ability and low algorithm efficiency,the chromosomes were improved to a full binary tree structure,corresponding mutation and crossover operators were designed,and a hierarchical evolution strategy was proposed.The experiments show that the local search capability of HE-GEP has been significantly enhanced.Compared with GEP,it has better performance and can find better scheduling rules.(5)The simulation of scheduling examples in the process of immune testing is carried out.The effectiveness of ABC-TLBO for the pre-scheduling of immune testing is verified.Two types of dynamic events such as emergency task insertion and sub-device failure are simulated.The rules constructed by HE-GEP are compared with seven commonly used rules(FIFO,SPT,LPT,LNPT,LRM,LR,MOR).The experiments show that the HE-GEP Rule performs better than the other seven rules,thus proving the validity and practicability of the scheduling rule constructed by the HE-GEP proposed in this dissertation.