Batch Scheduling Problem And Cooperative Optimization Of The Immunoassay Equipment Based On Migrating Bird Optimization Algorithm

With the development in modern medicine and the increasing demand for immune testing in China,the hospital laboratories are required to test numerous batches of blood samples in a short time.Under the demand of batch immune testing,the hospital laboratory is facing a great challenge on the accuracy and efficiency of immune testing.Automatic immune testing equipment can replace manual testing operations.However,it is difficult to meet the requirements of hospital immune testing only by automating the testing operations.The efficiency of batch immune testing should be effectively improved,the key of which lies in reasonable batch scheduling.That is to divide the batch blood samples into several sub-batches so that the multiple machines can be effectively utilized,and to optimize the testing order so that the total makespan of batch immune testing can be significantly reduced.Therefore,this paper studies the batch scheduling problem of automatic immune testing equipment for batch immune testing.Mathematical model of the problem is established.The coding and decoding scheme and searching operators for the problem are designed.And the Migrating Bird Optimization algorithm is improved and used to optimize batch splitting scheme and scheduling process.The main research contents of this paper are as follows:(1)According to the working principle of automatic immune testing equipment,the importance of batch scheduling to testing efficiency is analyzed.The batch scheduling problem of the equipment is summarized into a batch scheduling problem of Flexible Job Shop Problem with complex constraints.The optimization objective and constraints of the problem are determined,and the mathematical model of the problem is established.Moreover,the difficulties involved in solving the problem are analyzed,and the solution route is formulated according to the difficulties.(2)For the complex constrains,some constraint processing methods are designed.A coding and decoding scheme which conforms to all constraints is designed to eliminate root causes of infeasible solution.At the same time,the coarse-grained and fine-grained searching operators are designed to ensure that the constraints are not destroyed.Under the effect of the proposed scheme,the neighborhood structure can be enriched and the searching efficiency can be improved.Four typical test cases and four equipment test cases are simulated in the experiment.The experimental results show that the proposed searching operator can improve the accuracy of the problem optimization.(3)Through theoretical analysis,the Migrating Bird Optimization algorithm is used to solve the problem.The strong local search of the algorithm can improve the ability of searching in the complex feasible region.The performance of the algorithm is analyzed from many aspects,based on which the algorithm is improved.A Competitive Cooperative Migrating Bird Optimization(CCMBO)algorithm is proposed,which uses the competitive leading bird replacement method and the deceleration adjustment stage in order to improve the efficiency of cooperative optimization.Furthermore,the V-shaped flight stage of the bird population is improved to increase the diversity of the population.Experimental results show that the improved algorithm can effectively solve the batch scheduling problem of automatic immune testing equipment.(4)In order to further improve the efficiency of medium and large-scale batch immune testing,a Multi-Leader Competitive Cooperative Migrating Bird Optimization(ML-CCMBO)algorithm is proposed.In order to improve the ability of breaking away from local optimum,functionally differentiated bird sub-populations are applied,and a stage-based neighborhood search mechanism is introduced.Experimental results show that the proposed algorithm has better global searching ability and stability,and significantly improves the batch testing efficiency of the automatic immune testing equipment.