Artificial intelligence algorithm-based multi-objective optimization model of flexible flow shop smart scheduling

Abstract

Abstract In recent years, the manufacturing industry is facing rigorous development challenges due to rapid-changing market conditions, high energy consumption, and high production costs. Using intelligent scheduling technology is one of the effective strategies to reduce energy consumption and production costs. The production scheduling for flexible manufacturing system is a complicated nonlinear programming issue due to its flexibility and complex constraint conditions. To solve this issue, this work reviews the production scheduling process of the flexible flow shop. A classical mathematical model based on genetic algorithm is established to optimize the production schedule. On this basis, a real world typical flexible flow shop, the ball grinder mill of ceramic and cement industry, is taken as a case study to show the modeling process and verify the performance of the artificial intelligence algorithm-based multi-objective optimization model.