The paper addresses the Coil-Order Allocation problem in steel industry via Genetic Algorithms through two approaches: a basic solution with a standard objective function and an advanced method incorporating a Fuzzy Inference System to mimic human decision-making. Both solutions were tested on real-world data from a tinplate production plant, achieving significant improvements in orders fulfillment and material utilization compared to manual allocation. The basic genetic approach outperforms the baseline in efficiency, while the fuzzy-genetic method demonstrate flexibility for complex, customizable optimization. The results show the potential of combining heuristic techniques and fuzzy logic to enhance industrial operations.
Beyond optimality: Genetic Algorithms and Fuzzy Inference for Coil-Order allocation in the steel industry
Vannucci M.
;Colla V.;Laid L.;
2025-01-01
Abstract
The paper addresses the Coil-Order Allocation problem in steel industry via Genetic Algorithms through two approaches: a basic solution with a standard objective function and an advanced method incorporating a Fuzzy Inference System to mimic human decision-making. Both solutions were tested on real-world data from a tinplate production plant, achieving significant improvements in orders fulfillment and material utilization compared to manual allocation. The basic genetic approach outperforms the baseline in efficiency, while the fuzzy-genetic method demonstrate flexibility for complex, customizable optimization. The results show the potential of combining heuristic techniques and fuzzy logic to enhance industrial operations.| File | Dimensione | Formato | |
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