This article deals with energy saving in the hybrid flow shop scheduling problem with batch production at last stage, which has important application in energy-intensive steelmaking-continuous casting (SCC) process. We first establish a mixed integer programming model to reduce extra energy consumption, and then adopt genetic algorithm to solving the scheduling problem. Based on traditional genetic algorithm (TGA), the calculation of the fitness function as well as adaptive crossover and mutation are designed. Due to the complexity of the problem in this article, we then propose an efficient adaptive genetic algorithm (EAGA) to improve the search ability of TGA. The EAGA has new features including layered strategies and enhanced adaptive adjustment method. To evaluate the proposed model and algorithm, we conduct computational experiments under practical background and compare the EAGA with the several algorithms presented previously. The results illustrate that scheduling with our model can greatly reduce the extra energy consumption. Meanwhile, the proposed EAGA is very efficient in comparison.

中文翻译：

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后期批量生产混合流水车间调度节能的高效自适应遗传算法

本文主要研究末级批量生产的混合流水车间调度问题的节能问题，该问题在高耗能炼钢-连铸（SCC）工艺中有重要应用。我们首先建立混合整数规划模型以减少额外的能量消耗，然后采用遗传算法解决调度问题。在传统遗传算法（TGA）的基础上，设计了适应度函数的计算以及自适应交叉和变异。由于本文问题的复杂性，我们提出了一种高效的自适应遗传算法（EAGA）来提高TGA的搜索能力。EAGA 具有包括分层策略和增强的自适应调整方法在内的新功能。为了评估所提出的模型和算法，我们在实际背景下进行了计算实验，并将 EAGA 与之前介绍的几种算法进行了比较。结果表明，使用我们的模型进行调度可以大大减少额外的能源消耗。同时，提出的 EAGA 相比之下非常有效。