Genetic Algorithm for Minimizing Raw Material Waste in a Flow Shop Scheduling Problem with No-Wait Constraints and Early Setups

The problem of Flow Shop Scheduling (FSS) is widely studied due to its relevance in production environments, such as the food industry. This work addresses an emerging variation of FSS, aiming to minimize raw material waste under no-wait constraints, early setups and limited machine capacity. An adapted Genetic Algorithm (GA) was proposed to solve the problem, incorporating mechanisms specific to the industrial context. The model was validated in eight real instances of a large Brazilian dairy producer, comparing its results with those of the solution employed by the company. The experiments demonstrated that GA surpasses the current approach in solution quality, maintaining feasible computational times for real operations, evidencing the potential of the proposed approach in reducing losses in production processes with the constraints considered.