Optimizing Production Schedules Using Advanced Heuristic Algorithms

The optimization of heuristic algorithms on production schedules of automated systems is one of the most modern issues in technical cybernetics whose importance relates to the effectiveness of economic activity on the one hand, and the productivity of work performed on the other hand. This paper focuses on utilizing hybrid approaches for the optimization of complex production schedules. Proposed on the basis of Genetic Algorithm (GA), Simulated Annealing (SA), and Ant Colony Optimization (ACO), the algorithm was evaluated with benchmark datasets. The scheduling efficiency and total production time was improved through the use of the integrated genetic algorithm compared to traditional methods. This novel integration strategy developed from the research enables its adaptation to changes in production technologies, thus facilitating dynamic solutions for automated systems.