Research On Modeling And Optimization Method For Order And Inventory Planning Problem In Steelmaking Process

Taking the plant wide production process of steel industry as research background,the thesis studies the order and inventory planning problem arising in steelmaking stage.The key decision of the problem is to decide how allocate customer orders to different manufacture cells in each time period.However,to estimate the batching production cost resulted by the above order allocation decision,it has to decide the composition of each batch and the processing sequence of batches on each manufacture cell.Moreover,the problem not only considers the batch production requirements on steelmaking stage,but also takes the material supply demand which is proposed by hot and cold rolling stages to steelmaking stage and on time delivery of customer orders into account.This thesis formulates a mixed integer programming model for the problem,develops an improved differential evolution algorithm to solve the problem.The decision support system is developed based on the model and algorithm.The main research contents include:(1)By considering the attributes of customer orders and process characteristics,the original orders data is being preprocessed so as to reduce problem input size.According to the batch production requirements of the steelmaking stage,warm roller material and hard rolling material supply demand required by the hot rolling stage,material requirements for different cold rolled products and the requirements of product delivery time,a mixed integer programming model is formulated with the objective of minimizing the batching production cost on steelmaking stage.The model is solved by the commercial optimization solver CPLEX for the small-scale instances to verify its correctness.(2)An improved differential evolution algorithm is developed to solve the problem.During the algorithm implementation,we design a discrete coding mechanism based on which a pointer based mutation operator and a crossover operator with repairment are proposed.In order to enhance the local search ability of the algorithm,the neighborhood search strategy is introduced into the algorithm framework.In order to improve the global search ability and convergence speed of the algorithm,an adaptive mechanism is proposed for the mutation strategy and cross factor.The improved differential evolution algorithm is tested on a set of actual production data.The computational results show that the proposed algorithm has significant advantages over the basic difference scheme and the manual method.(3)Based on the actual demand of iron and steel enterprise,by embedding in the proposed model and algorithm,we design and development an order and inventory planning decision support system to assist the planner making plan.The function modules of the system mainly include data management,static parameters configuration,model and algorithm,result evaluation and ect.