Abstract
With the increasing demand for automotive glass, improving the efficiency of automotive glass manufacturing systems can make full use of production resources and reduce the waste of natural and social resources. Therefore, this work aims to provide an efficient method for a real-world two-stage hybrid flow shop scheduling problem with small batches in an automotive glass manufacturing system. For the investigated problem, there is a significant setup time at the first stage, the second stage is served by machines that should not be interrupted, and each batch has a due date. Such constraints make this scheduling problem challenging. To solve this problem, a mixed integer linear programming model is established. Also, two properties and three theorems are given to enhance the problem-solving process. Subsequently, an efficient genetic algorithm is carefully designed to solve large-scale problems by considering the properties of the system. Meanwhile, an improvement scheme is proposed to decrease the running time of the algorithm, and experimental results show that this scheme can reduce the running time by 280 s at most from the average results of different scale problems. Finally, extensive experiments are carried out and a real-world case is solved to demonstrate the efficiency and effectiveness of the designed genetic algorithm. Also, the Taguchi method is adopted to tune the parameters for the designed algorithm.
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