Intelligent optimisation scheduling of raw material area in sausage production line Online publication date: Mon, 13-May-2024
by Zhonghua Han; Mingpeng Guan; Hangyu Liu; Daliang Chang; Liangliang Sun
International Journal of Simulation and Process Modelling (IJSPM), Vol. 20, No. 4, 2023
Abstract: The production process in the raw material area of the sausage production line needs to be further optimised to improve production efficiency and reduce the waiting time for suctioning of emulsifying fillings. In this study, we propose an improved Q-learning method for scheduling in raw material area (RMSPS-IQL). To overcome the problem of low learning efficiency caused by multi-operation and multi-tasking in the production line scheduling process, a priority stranding probability is added to the action selection method based on the epsilon-greedy strategy. In addition, to determine the execution time of the action during continuous working, a stranding action starting control method based on the probability of filling deterioration was designed. Comparative analysis of the results of multiple simulation schemes suggested that the RMSPS-IQL effectively reduces the waiting time for suctioning fillings from emulsifying pot, and improves the productivity of the production line.
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