Конференції молодих вчених, студентів та аспірантів (Conferences of Young Scientists, Students and Postgraduate Students)

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  • Документ
    Dynamic scheduling strategy of intelligent RGV
    (2020) Weikang, K.
    With the development of the intelligent industry, intelligent processing systems have received extensive attention, and the planning of the production process is the key to the entire processing system. This paper mainly studies the dynamic scheduling problem of RGV in different situations without faults, and gives the corresponding algorithm, discusses the scheduling strategy and operation efficiency, and tests the practicability of the model and the effectiveness of the algorithm. In the case of a trouble-free process, firstly, the maximum production material in a unit shift is the goal, and the above blanking time, waiting time, remaining working time, moving time and other constraints are used, and the recursive relationship between loading time and waiting time is used at the same time A 0-1 RGV dynamic scheduling 0-1 planning model for a trouble-free process is established. Based on this model, using the idea of directed acyclic shortest, the corresponding global optimization algorithm is designed, and the three sets of data are used to calculate the optimal scheduling strategy of RGV within the unit shift. At this time, the system's operating efficiency is 365, 347, 374 per shift. Finally, a process error detection model is established, and sensitivity analysis is performed to determine that the model has strong stability. Comparing the global optimization algorithm with the traditional greedy local optimization algorithm, the improvement percentages are 2.52%, 3.27%, and 2.18%, indicating that the algorithm is effective. For the case of two programs without failure, the improvement is based on the case-one model. Increase the tool selection 0-1 variable, tool selection position, cleaning sequence and work flow constraints, with the goal of completing the second process with the largest amount of materials, and establish a trouble-free two process RGV dynamic scheduling 0-1 model. Combined with this model, the corresponding global optimization algorithm is designed. Then based on the 3 sets of data, 3 sets of tool arrangements are calculated. At the same time, the optimal scheduling strategy of RGV in one shift was obtained. At this time, the system's operating efficiency was 248, 203, and 243 per shift, respectively. Finally, based on a process error detection model, a two-process error detection model was established, and a sensitivity analysis was performed to determine that the model is more stable. Comparing the global optimization algorithm with the traditional greedy local optimization algorithm, the improvement percentages are 5.46%, 0.49%, and 0.41%, indicating that the algorithm is effective. Finally, the paper evaluates the advantages and disadvantages of the model, proposes improvements, promotes the model, and obtains the intelligent RGV dynamic scheduling strategy in the event of a fault.