A Scatter Search Algorithm For A Steelmaking-Continuous Casting Schedule Problem With Combination Of Ladle Allocation

During the production of steel products,the production process of steelmaking-continuous casting(SCC)is the more important production process,and a reasonable scheduling for SCC is an effective method to reduce resources and energy consumption,increase production efficiency,reduce production costs and increase profits.In addition,ladles service molten steel from the end of steelmaking process to the end of continuous casting process.Therefore,the SCC scheduling considering ladles connect SCC process closely and further to reduce consumption of resources and energy.Therefore,for integrated SCC scheduling with considering the service paths of ladles,reasonable and effective scheduling plans play a critical role for saving and reducing energy of steel production process.This thesis studies the SCC scheduling problemincluding the processes of steel production,refining continuous casting and the service paths of ladles at load or no-load.Considering the constraints of technological process in steel production and ladles-self,the scheduling task contains to allocate equipment(including ladles),determine the production sequence and the start and end of time for all charge.The objectives are to minimize the maximum completion time and the waiting time of the charge and equipment.This thesis establishes a non-linear mathematical programming model for the proposed scheduling problem.According to the problem characteristics,this thesis develops a new scatter search(SS)algorithm and compares it with the genetic algorithm.Experiments are performed on different weights,different example sizes,different ladle counts,and different equipment combinations,and the effects of different parameters on the performance of the algorithm are analyzed.Experimental results show that the optimization results obtained by the SS algorithm are significantly better than the genetic algorithm,and the SS algorithm is suitable for solving the studied scheduling problem.