Research On Steelmaking Continuous Casting Production Scheduling Method Based On Artificial Bee Colony Algorithm

Iron and steel industry serves as the pillar for the national economy and national defense construction.Steelmaking continuous casting processing is the main process and bottleneck of steel production.Effective steelmaking continuous casting scheduling methods can help to enhance productivity and reduce costs.However,steelmaking continuous casting scheduling is a complex NP-hard problem,which is difficult to solve with traditional optimization approaches.Therefore,it is of much theoretical and practical value to study steelmaking continuous casting scheduling problem.In this thesis,steelmaking continuous casting scheduling problem is deeply studied.Buffering constraints and multi-objective optimization are taken into account according to realistic production.And based on Artificial Bee Colony(ABC)algorithm,effective intelligent scheduling algorithms are designed.Firstly,according to the multi-stage and multi-constraint characteristics of steelmaking continuous casting scheduling problem,a basic mathematical model is constructed.And the research framework for this problem is put forward.Secondly,to address the model above,an improved ABC algorithm is proposed.effective discrete coding and encoding methods are adopted,while a self-adaptive neighborhood approach is devised to enhance local search based on four different neighborhood structures.In addition,individual updating mechanisms of onlooker bee phase and scout bee phase are improved,which further enhance convergence speed and exploitation.The results of numerical experiments prove the effectiveness of the improved ABC algorithm.Thirdly,a mathematical model is established with buffering capacity taken into account.And ABC algorithm is improved according to the problem characteristics.The coding and encoding methods are adjusted to satisfy the buffering constraints.Numerical experiments illustrate that the proposed algorithm exhibits better performance than other algorithms.Then,taking both buffering constraints and multiple objectives into consideration,a mathematical model is constructed and multi-objective ABC algorithm(MOABC)is proposed.On the basis of Pareto optimality theory,external Pareto archive set is introduced.Besides,fast non-dominated sorting method is adopted to improve the individual updating mechanisms of the ABC algorithm.The superiority of the proposed algorithm is verified through comparison with NSGA-II on different instances.Finally,the whole work is summarized,and several directions for further research are put forward.