The Research Of Re-entrant Scheduling And Optimization Of The Immunoassay Analyzer Equipment

Now, clinical biochemical tests have basically automated. Automatic analyzer changesthe original manual process sampling, dilution, mixing, incubation, testing, results calculationand display, cleaning steps all or part of into automation, so as to reduce the influence ofsubjective factors by the operators, especially when a testing equipment detects a variety ofprojects simultaneously, not only improves efficiency, but also reduces the intensity ofoperators. Aiming at solve the motion optimization problems of Immunoassay Analyzerequipment, especially for multiple test items at the same time, how to arrange each module’smovement, then to improve the biochemical index detective efficiency and degree ofautomation, so the rule scheduling and combined with bottleneck equipment, intelligentalgorithm is used to achieve optimization scheduling in this paper.The main contributions are summarized as follows:(1). According to the characteristics of the Immunoassay Analyzer equipment schedulingsystem: constraints are numerous, parallel and serial coexistence and so on, this paper designsre-entrant scheduling optimization model, defines constraints and objective function. Themodel fully considers the movement constraints, and reasonably arranges the workingsequence of the modules in order to minimize the total completion time.(2). Because the resource of washing machine is limited, so this paper puts forward anew kind of optimization scheduling method, combining the preliminary algorithm and theadjustment algorithm, to get satisfied solution of high quality with the smaller amount ofcalculation. A preliminary feasible solution is achieved by using bottleneck equipment processpriority scheduling rules, then through the time parameter calculator, all start time andcompletion time of the processes are obtained, that’s to say, satisfactory scheduling solution.The scheduling method is simple, feasible, and has a quick solution speed, and can get highquality approximate optimal solution.(3). Improved Simulated annealing algorithm and Ant Colony Optimization are used toachieve optimal solution. This paper is not only an example to verify the validity andreliability of the method, but also show that this method can greatly improve the working efficiency of the equipments and has practical applications.(4). Simulation results demonstrate the advantages and disadvantages of the algorithms,and further suggest improvements direction.