Abstract
The production of aerospace shell products is directly related to the safety and reliability of aerospace products. In this work, the aerospace shell product digital production line scheduling problem (ASPDPLSP) was studied, and a solution was developed. In the production process, it is necessary to decide the processing machine and time of each operation. In order to create a scientific shell product production plan, we propose an operation scheduling algorithm (OSA). Based on the constraints of the inspection process, the OSA has two heuristic task scheduling rules. Then, in order to further optimize the product production plan, an improved genetic algorithm (IGA) is proposed. Considering the repeatability caused by random search, a method for the initial population generation with similar and diverse characteristics is proposed. Two of these generation rules retain symmetry and randomness. IGA was used to optimize the order in which the products were processed, resulting in lower costs. Simulation experiments showed that the proposed algorithm solved ASPDPLSP well and provided suggestions to produce aerospace shell products.
Highlights
With the increasing production pressure of aerospace products, the production line is moving in the direction of digitalization and intelligence
We considered the effect of the people involved in the automated production process and the waiting process in the product processing process, developed efficient task planning algorithms and improved genetic algorithms, obtained reasonable production plans, and optimized these production plans to reduce the production costs
The improved genetic algorithm improves the evolution of the biological gene and the ASPDPLSP based on the simple genetic algorithm, in order to improve the speed while ensuring the optimization effect
Summary
With the increasing production pressure of aerospace products, the production line is moving in the direction of digitalization and intelligence. The FJSP of the fuzzy processing time was taken as the research object, and an effective teaching–learning-based optimization algorithm was designed in [7]. For the scheduling problem with the fuzzy processing time, a bionic artificial bee colony algorithm was designed in [8] We considered the effect of the people involved in the automated production process and the waiting process in the product processing process, developed efficient task planning algorithms and improved genetic algorithms, obtained reasonable production plans, and optimized these production plans to reduce the production costs. The innovation of this study was to design a reasonable scheduling algorithm for the aerospace shell production line and reduce the production cost while realizing a digital production.
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