Energy-efficient steelmaking-continuous casting scheduling problem with temperature constraints and its solution using a multi-objective hybrid genetic algorithm with local search

With the increasing energy price and the urgent demand of manufacturing enterprises for energy conservation, energy-efficient scheduling (EES) technology has been widely investigated and applied in industry and academia. The steelmaking-continuous casting (SCC) production is the main energy-consuming sector and the key process for quality control of steel manufacturing. Due to the high-temperature characteristics of SCC production, the temperature drop deriving from nonprocessing process could directly lead to energy loss and increase the energy consumption of each procedure, which has important influence on the total energy consumption. Therefore, the energy-efficient steelmaking-continuous casting scheduling problem with temperature constraints (EESCCSPT) was concerned and a multi-objective mathematical programming model was introduced to minimize the penalty of due date deviation and the extra energy consumption measured by temperature drop. Comparing to the general scheduling problem, the constraint of minimum casting superheat and the constraint of target tapping temperature generated by the high-temperature technical requirements were directly considered to ensure schedule feasibility in terms of temperature. A multi-objective hybrid genetic algorithm combined with local search (MOHGALS) was presented, in which the enhanced evolutionary mechanisms combined with the improved genetic operators and the local search were also designed. Results of computational experiments showed that MOHGALS was more feasible and effective than NSGA-II and SPEA2 on the EESCCSPT. (C) 2020 Elsevier B.V. All rights reserved.