Due to the complex physical changes and chemical reactions in the process of the steelmaking-continuous casting(SCC),the uncertainty of processing time is a common and representative uncertainty fac-tor.Therefore,it is necessary to consider the uncertain processing time before scheduling,to enhance the robustness of schedules and reduce the repair frequency of dynamic scheduling.For SCC scheduling problem with processing time uncertainty,the processing time is described by a three-parameter interval.A multi-objective optimization model with interval-valued is established to minimize the total waiting time and the total earliness/tardiness of casting time.To solve this problem,an improved fast elitist non-dominated sorting genetic algorithm(NSGAⅡ+)based on a classification evolution strategy is presented.Firstly,a decoding scheme considering the reverse order and a hybrid population initialization based on machine rules are proposed combining interval number operation.Then a classification evolution strategy is adopted to determine the crossover and mutation operators according to the crowding distance.The re-mutation of repeated individuals is proposed to maintain the diversity of the population.Finally,the results of the experiments based on actual SCC production data shows the effectiveness of the proposed NSGAⅡ+in solving quality and efficiency.Note that if the upper and lower limits and intermediate parameter of all three parameter intervals are the same,the problem is transferred into a static scheduling based on standard processing time.If two of the upper and lower limits and intermediate parameters of all three parameter intervals have the same value,it will degenerate into a two-parameter interval number problem.Thus,the model and algorithm proposed in this paper are also applicable to the above two problems.
关键词
炼钢—连铸/生产调度/加工时间不确定/区间多目标优化/遗传算法
Key words
steelmaking-continuous casting/production scheduling/processing time uncertainty/interval multi-objective optimization/genetic algorithm
维普
万方数据