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Studying on a Genetic-Simulation Optimization Algorithm Method for Steel Crane Scheduling Problem

机译：钢铁起重机调度问题的遗传模拟优化算法方法研究

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摘要

For solving crane scheduling problem in steelmaking-continuous casting process, a hybrid method of integrating genetic algorithm and simulation was formulated to minimize the operation time of tasks by considering full ladles, empty ladles and auxiliary tasks as transportation tasks. The crane selected sequence was designed as chromosome coding. The initial population was generated by employing the crane selection rules in the simulation model, and then evolved optimized by using the genetic operations such as selection, crossover and mutation. The chromosome is evaluated through generating a feasible crane schedule by employing the attribute updating rules and collision eliminating rules in the simulation model. The new crane scheduling could be generated through simulation model based on the new species. Therefore, more excellent individuals will be produced along with the evolutionary process. Thus a better crane scheduling scheme could be obtained finally. In the process of initialization, the high quality initial population was generated in the simulation model. In the iterative process, simulation model also could generate the feasible crane scheduling schemes based on the given cranes selection sequence. To validate this model, experiments were conducted by using the production data in the casting span of a steel plant. The results demonstrate the feasibility and efficiency of this method, which provides a useful tool for crane scheduling in actual production.
机译：为了解决炼钢连铸过程中的起重机调度问题，提出了一种将遗传算法与仿真相结合的混合方法，以将满钢包，空钢包和辅助任务作为运输任务，以最小化任务的运行时间。起重机选择的序列被设计为染色体编码。通过在模拟模型中采用起重机选择规则来生成初始种群，然后通过使用遗传操作（例如选择，交叉和突变）进行优化优化。通过在仿真模型中采用属性更新规则和碰撞消除规则，通过生成可行的起重机计划来评估染色体。通过基于新物种的仿真模型可以生成新的起重机调度。因此，随着进化过程将产生更多优秀的个体。因此，最终可以获得更好的起重机调度方案。在初始化过程中，在仿真模型中生成了高质量的初始种群。在迭代过程中，仿真模型还可以根据给定的起重机选择顺序生成可行的起重机调度方案。为了验证该模型，使用钢厂铸造跨度中的生产数据进行了实验。结果证明了该方法的可行性和有效性，为实际生产中的起重机调度提供了有用的工具。

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