Application of Bio-inspired Metaheuristics to Guillotined Cutting Processes Optimize in an Glass Industry
Nowadays, sustainability is becoming a strong worry of our society. It can be defined as using resources to meet the needs of the present without compromising the ability of future generations to meet their own needs. An optimized cutting process minimizes the materials waste and is an important factor for production systems performance at glassworks industries, impacting directly in the products final cost formation and contributing for more environmentally sustainable products and production processes. Several studies have shown that combinations of bio-inspired meta-heuristics, more specifically, the Genetic Algorithms (GA) and Ant Colony Optimization (ACO) are efficient techniques to solving constraint satisfaction problems and combinatorial optimization problems. GA and ACO are bio-inspired meta-heuristics techniques suitable for random guided solutions in problems with large search spaces. GA are search methods inspired by the natural evolution theory, presenting good results in global searches. ACO is based on the attraction of ants by pheromone trails while searching for food and uses a feedback system that enables rapid convergence in good solutions. The results from the combination of these two techniques, when compared with the results from usual processes, are encouraging and have presented interesting solutions to the problem of optimizing guillotined cutting processes.
KeywordsGenetic Algorithms Ant Colony Optimization Guillotined Cutting Glass Industry Sustainability
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