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For one intelligent processing system which inclusionRGV, how to effectively use various resources to rationally and dynamically perform dynamic scheduling to improve production efficiency is the key. This paper studies only the material processing operations of a single process. According to the processing process of a given material, we need to focus on analyzing its dynamic scheduling strategy. In a material processing system with an established 8-hour working time, maximizing the amount of material processing is the primary goal.  However, the increased amount of material processing is obtained by continuously completing the accumulation of work tasks. Therefore, the core is to convert the material processing quantity maximization model in the system into a task selection planning model based on time loss minimization, and seek each with a greedy algorithm. A task selects the optimal solution locally, and approximates each local optimal combination as a global optimal.

RGV dynamic scheduling optimization model based on greedy algorithm