Multi-object optimization of flexible flow shop scheduling with batch process — Consideration total electricity consumption and material wastage. (10th May 2018) - Zeng, Zhiqiang; Hong, Mengna; Man, Yi; Li, Jigeng; Zhang, Yanzhong; Liu, Huanbin
Abstract: Electricity consumption and material wastage of manufacturing industries have directly or indirectly influenced the environment. Many manufacturing enterprises are committed to improving energy and material efficiencies. To achieve this goal, one of the high-efficiency ways is to optimize the scheduling. In this study, a multi-object optimization model is constructed for a particular kind of batch processing scheduling problem existed in flexible flow shops, where the objects include makespan, electricity consumption and material wastage. First, the models for electricity consumption and material wastage are built, based on which a mathematic model for scheduling problem is built afterwards. Finally, a hybrid non-dominated sorting genetic algorithm II (NSGA-II) method is employed to solve the multi-object optimization scheduling model. A case study based on a real-world paper mill industry is presented to demonstrate the effectiveness of the method and its application in practice. Moreover, a real-life scheduling problem is investigated, and the result shows that the proposed method is more efficient than manual scheduling in energy and material saving. In fact, many tissue paper mills or other manufacturing industries schedule by manual labor in China, indicating that the proposed method has a significant potential in improving energy and material efficiencies in the studied flexible flow shop, and in energy and material saving in similar industries. Highlights: A new mathematics model for flow shop problem with consideration electricity consumption and material wastage is proposed. A hybrid NSGA-II method is developed to solve the proposed model. The electricity consumption and material wastage can be reduced simultaneously. The electricity consumption and material wastage can be reduced by 2.3% and 5.9%.