Cable production scheduling optimization method based on reinforcement learning

Abstract

The invention discloses a cable production scheduling optimization method based on reinforcement learning. The method comprises the steps: establishing a cable production scheduling optimization modelunder multi-assembly-line and complex resource constraint conditions, wherein the optimization model aims at minimizing the deadline delay punishment cost; on the basis, combining with a hyperheuristic algorithm framework, taking a reinforcement learning mechanism as an HLH strategy of a hyperheuristic algorithm, and designing a simple heuristic rule for the characteristics of the cable production scheduling problem to construct an LLH method set, so the optimal solution of the cable production scheduling problem is realized; the optimization method is low in complexity, and the production and management efficiency of the traditional cable industry can be effectively improved. The method is of great significance to comprehensive promotion of quality improvement, efficiency improvement, transformation and upgrading of the traditional industry.

Description

technical field [0001] The invention relates to an optimization method, in particular to an optimization method for cable production scheduling based on reinforcement learning. Background technique [0002] With the continuous improvement of industrial scale and the continuous development of social economy, cable products have been more and more widely used in important industrial fields such as construction, transportation, automobile, communication, and energy. According to statistics, as early as 2012, the total output value of my country's wire and cable industry has exceeded one trillion yuan, becoming the world's largest wire and cable producer. At the same time, the market competition in the wire and cable industry is becoming increasingly fierce. Enterprises need to reduce production costs and improve production, management and service efficiency by reducing inventory, improving equipment utilization, and rationally allocating human resources. Scheduling optimizatio...

AI Technical Summary

Problems solved by technology

However, the hyperheuristic algorithm has a problem of high computational complexity. The main reason is that the HLH strategy itself takes a lot of time to find the optimal heuristic method. Reducing the algorithm complexity of the HLH strategy is equally important for improving the overall performance of the algorithm. has an important influence

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