This paper focuses on an energy-efficient job-shop scheduling problem within a machine speed scaling framework, where productivity is affected by deterioration. To alleviate the deterioration effect, necessary maintenance activities must be put in place during the scheduling process. In addition to sequencing operations on machines, the problem at hand aims to determine the appropriate speeds of machines and positions of maintenance activities for the schedule, in order to minimise the total weighted tardiness and total energy consumption simultaneously. To deal with this problem, a multi-population, multi-objective memetic algorithm is proposed, in which the solutions are distributed into sub-populations. Besides a general local search, an advanced objective-oriented local search is also executed periodically on a portion of the population. These local search methods are designed based on a new disjunctive graph introduced to cover the solution space. Furthermore, an efficient non-dominated sorting method for bi-objective optimisation is developed. The performance of the memetic algorithm is evaluated via a series of comprehensive computational experiments, comparing it with state-of-the-art algorithms presented for job-shop scheduling problems with/without considering energy efficiency. Experimental results confirm that the proposed algorithm can outperform other algorithms being compared across a range of performance metrics.

本文重点关注机器速度缩放框架内的节能作业车间调度问题，其中生产率受恶化的影响。为了减轻恶化的影响，在计划过程中必须进行必要的维护活动。除了对机器进行排序操作外，当前的问题还在于确定时间表的机器适当速度和维护活​​动的位置，以便同时使总加权拖延和总能耗最小化。为了解决这个问题，提出了一种多种群，多目标的模因算法，该算法将解决方案分配到子种群中。除了一般的本地搜索之外，还定期对一部分人口执行面向目标的高级本地搜索。这些局部搜索方法是根据引入的新析取图设计的，以覆盖解决方案空间。此外，开发了一种用于双目标优化的有效非支配排序方法。通过一系列综合计算实验评估了模因算法的性能，并将其与针对有/无能源效率的车间调度问题提出的最新算法进行了比较。实验结果证实，所提出的算法可以在性能指标范围内胜过其他算法。通过一系列综合计算实验评估了模因算法的性能，并将其与针对有/无能源效率的车间调度问题提出的最新算法进行了比较。实验结果证实，所提出的算法可以在性能指标范围内胜过其他算法。通过一系列综合计算实验评估了模因算法的性能，并将其与针对有/无能源效率的车间调度问题提出的最新算法进行了比较。实验结果证实，所提出的算法可以在性能指标范围内胜过其他算法。