Research On The Scheduling Problems Of The Biochemical Immunodetection Equipment Based On Petri Nets

As the biochemical immunodetection equipment being widely used in clinical detection, when the hospital face an increasing number of patients everyday, semi-automatic or manual testing equipments can not meet the requirements any more, then the automatic biochemical immunodetection equipment gradually become a research hotspot and future trends. The automatic biochemical immunodetection equipment has achieved that a series of operations like sampling, adding reagents, incubating, shifting boards, washing, testing, printing the results and so on can be executed automaticly, which can reduce manual intervention in the detection process and improve the detection accuracy and detection efficiency to a certain extent. However, if the detection equipment only realizes automation, it still can not meet the requirements of clinical detection efficiency. Then we need futher scheduling detection process of batch testing projects to shorten detection time. This paper will combine petri net model with intelligent algorithms to reasonably arrange the execution sequence of detection projects and guide the device coordinate with each other. After that, it can improve equipment utilization and achieve load balance. Finally, it will complete the goal that minimizing the total finish time. The main research contents are as follows:(1)The paper detailedly analysis the scheduling characteristics of detection equipments and most of its detection process. According to the special features of detection equipments, such as cluster tools, re-entrant, dual arms, parallel machines, no buffer, a lot of constraints and so on, The paper classified the scheduling problem of the detection equipments as a scheduling problem of dual-arm cluster tools, so the method of solving the problem of dual-arm cluster tools can be used to research the scheduling problem of the detection equipments.(2)Time colored Petri nets(TCPN) are used to establish a common model for most detection process of biochemical immunodetection equipments, which can describe all the scheduling features of the equipment. In addition, in order to verify the feasibility and rationality of the TCPN model, this paper analysis the deadlock, the structural properties and the dynamic nature.(3)To improve solving efficiency, use the improved ant colony algorithm to solve time colored Petri net model. Furthermore, the ant colony algorithm is design for the transition, so the algorithm doesn’t depend on the problem itself, which improves the versatility of the algorithm. While the ant colony algorithm is modified to avoid precocity and get local optimal solutions.(4)To verify the effectiveness of the proposed method, this paper use it in the practical examples. At the same time, compare the method with basic ant colony algorithms and other improved ant colony algorithm. The results show that the improved ant colony algorithm performs better and gets the shortest total finish time.(5)In order to find the global optimal solution, this paper embeds the improved the ant colony algorithm in the framework of the cultural algorithm to construct hybrid algorithm to solve the Petri nets model. Utilize the feature that all populations evolved independ- ently in the population spaces and belief spaces collect informations of all populations to guide the evolution of the populations, so it effectively expands search range. In the latter part of the algorithm, add chaotic disturbance in belief spaces to further avoid the local optimum solution. Finally, compared with ant colony optimization algorithm, the improved chaotic ant colony algorithm culture is proved have the advantage in seaching capability and stability through examples.