Scheduling a realistic hybrid flow shop with stage skipping and adjustable processing time in steel plants

This paper studies a new realistic hybrid flow shop (HFS) scheduling problem with stage skipping and adjustable processing time in steelmaking- continuous casting (SCC) production process. The SCC scheduling problem is solved to determine the machine allocations, starting times and ending times for all operations of all charges (jobs). Through clarifying the production objectives and constraints related to stage skipping and adjustable processing time, a new SCC scheduling model is built. We develop an improved genetic algorithm (GA) to address the scheduling problem. For calibrating our GA, four encoding methods, two selection operators and three crossover operators, which are effective and widely used in regular HFS scheduling problems are compared and analyzed. In addition, a quality improvement approach is developed to embed into the GA to further optimize each solution obtained by the decoding heuristic. Moreover, to accelerate the local optimization and avoid premature convergence, a new elitist strategy and a restart strategy are employed in our GA. Computational experiments based on instances generated from a practical production process show that the proposed improved GA is effective for solving the SCC scheduling problem.