With the increasing concern on greenhouse gas emissions, green scheduling decision in the manufacturing factory is gaining more and more attention. This paper addresses the unrelated parallel machine green scheduling problem (UPMGSP) with criteria of minimizing the makespan and the total carbon emission. To solve the problem, the estimation of distribution evolution memetic algorithm (EDEMA) is proposed. Firstly, based on the minimum machine load first principle, the initialization of the population is proposed. Second, a multi-objective non-dominated sorting approach and the crowding distance are adopted to improve the diversity of individual. Third, to estimate the probability distribution of the solution space, a probability model is presented to enhance the searching ability. Third, five neighbourhood searching operators are designed to handle the job-to-machine assignment. Moreover, the population catastrophe is used to maintain the sustainable diversity of the population. Finally, based on the randomly generated instances of the UPMGSP, extensive computational tests are carried out. The obtained computational results show that the EDEMA has the better searching capability and the better objective value than those of the non-dominated sorting genetic algorithm II and the estimation of distribution evolution algorithm (EDEA) in solving the UPMGSP.

中文翻译：

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无关并行机绿色调度问题的分布演化模因算法估计

随着对温室气体排放的日益关注，制造工厂中的绿色调度决策越来越受到关注。本文以最小化制造期和总碳排放量为标准，解决了无关的并行机绿色调度问题（UPMGSP）。为了解决该问题，提出了一种分布式进化模因算法（EDEMA）的估计方法。首先，基于最小机器负载优先原则，提出了种群的初始化方法。其次，采用多目标非支配排序方法和拥挤距离来提高个体的多样性。第三，为了估计解空间的概率分布，提出了一种概率模型来增强搜索能力。第三，设计了五个邻域搜索运算符来处理作业到机器的分配。此外，人口灾难用于维持人口的可持续多样性。最后，基于UPMGSP的随机生成实例，进行了广泛的计算测试。计算结果表明，在求解UPMGSP问题上，EDEMA具有比非支配排序遗传算法II和分布估计算法（EDEA）更好的搜索能力和更好的客观价值。