Smart Scheduling: Improve your production planning

Smart Scheduling: Improve your production planning

Industry has evolved and benefited from advanced technological solutions that automate and monitor industrial processes on the factory floor, increasing production and quality levels.

One of the application areas for these technological solutions is Smart Scheduling. This Smart Scheduling aims to optimize the industrial production process, reducing production costs and meeting all the requirements of the orders placed with the manufacturer, thus fulfilling a multi-objective purpose.

The industrial planning of a factory is a crucial factor in its success. Constructing a plan that minimizes costs and improves production rates is one of the biggest challenges manufacturers face due to the high complexity inherent in the nature of the problem.

As this is a complex optimization problem, the use of artificial intelligence tools, such as Genetic Algorithms, to find an optimized solution can be advantageous due to their characteristics and their usefulness in this type of optimization problem.

A genetic algorithm is a meta-heuristic search method and a type of evolutionary algorithm, which is widely used today to solve optimization problems, such as planning or routing problems.

An approach to this industrial production planning problem was developed using a genetic algorithm combined with the Tabu Search algorithm. The developed solution aims to optimize production planning by reducing setup time and production costs (scrap rate and energy consumption) while meeting established production deadlines (e.g., delivery deadlines for customer orders). One of the advantages of the implemented approach is that it allows customization by the manufacturer at the planning algorithm level, as it can define which factors are most important in the optimization process. This customization of the solution is a relevant aspect, as for certain manufacturers it may be more advantageous to reduce the amount of scrap, while for others it may be more important to reduce the setup time spent on production.

The solution was tested in a real production environment, where it was found that improvements were achieved compared to the actual planning executed by the factory in terms of setup time, energy consumption, and the amount of scrap. It is important to highlight that in applying the algorithm to the real case, it was possible to reduce the total production time and increase production efficiency.

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