An energy-efficient hybrid flow shop scheduling problem in steelmaking plants

Abstract

Energy consumption is the basic condition for the smooth operation of steelmaking plant production process. Meanwhile, energy consumption is influenced by the production process. There is an increasing awareness in scheduling research of steelmaking plants that energy consumption needs to be considered. This paper studies a steelmaking plant scheduling problem considering energy saving (SPSP-ES) through a two-level strategy to simultaneously enhance production efficiency and reduce energy cost. The energy consumption with regard to the production process is defined, and a multi-objective scheduling model aiming at minimizing the makespan, average charge comprehensive energy consumption and fluctuation of energy consumption and production is developed. To tackle this problem, an improved multi-objective evolutionary algorithm based on decomposition (IMOEA/D) is proposed. Considering the influence of different processing paths on energy consumption, the discrete integer encoding and heuristic decoding based on processing paths are adopted. In addition, to enhance the local search capability, variable neighbourhood search is introduced in the search process. The superior performance of the developed algorithm is verified through numerical experiments. Furthermore, the results of two scheduling mode, which are energy-saving mode and high-efficiency mode, are compared to evaluate the effectiveness of the proposed model. The results show that the proposed model can decrease the comprehensive energy consumption and fluctuation of energy consumption and production up to 4.4% and 56.8% respectively, which strongly confirms that the scheduling considering energy-related objectives can balance the energy consumption and production, and reduce charge comprehensive energy consumption while improving the production efficiency.