摘要
A new scheduling model for the bulk ore blending process in iron-making industry is presented , by converting the process into an assembly flow shop scheduling problem with sequence-depended setup time and limited intermediate buffer , and it facilitates the scheduling optimization for this process.To find out the optimal solution of the scheduling problem , an improved genetic algorithm hybridized with problem knowledge-based heuristics is also proposed , which provides high-quality initial solutions and fast searching speed.The efficiency of the algorithm is verified by the computational experiments.
A new scheduling model for the bulk ore blending process in iron-making industry is presented , by converting the process into an assembly flow shop scheduling problem with sequence-depended setup time and limited intermediate buffer , and it facilitates the scheduling optimization for this process.To find out the optimal solution of the scheduling problem , an improved genetic algorithm hybridized with problem knowledge-based heuristics is also proposed , which provides high-quality initial solutions and fast searching speed.The efficiency of the algorithm is verified by the computational experiments.
基金
Item Sponsored by National Key Technology Research and Development Program in 11th Five-Year Plan of China ( 2006AA04Z184 )
National Natural Science Foundation of China ( 60974023 )
