PRODUCTION SCHEDULING OPTIMIZATION MODEL BASED ON DYNAMIC PROGRAMMING AND GENETIC ALGORITHM
Keywords:
Production decision, Dynamic programming, Genetic algorithmAbstract
In view of the problem that enterprises can not improve their profits due to the difficulty in planning reasonable decision-making schemes in real life, this paper proposes a dynamic programming model of production decision-making based on genetic algorithm. Firstly, a joint target benefit model considering the detection cost of parts, the purchase price of parts, semi-finished products, the assembly cost of finished products, the loss of replacement, semi-finished products, the disassembly cost of finished products, the disassembly of semi-finished products into parts, the disassembly compensation of finished products into semi-finished products, and the market price is established. Then, the genetic algorithm is used to optimize the variables of whether to detect parts, whether to detect semi-finished products, and whether to disassemble finished products ( 0-1 ). Finally, taking an actual production plan as an example, the validity of the model is verified, and the decision-making plan when the profit is the largest is obtained. This paper proposes a genetic algorithm-based dynamic programming model for production decision-making, which comprehensively considers various cost and price factors to optimize decision variables, thereby improving production efficiency and profit margins, and is verified to be valid through an actual production plan.References
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