The concept of green scheduling, which deals with the environmental impact of the scheduling process, is becoming increasingly important due to growing environmental concerns. Most green scheduling problem variants focus on modelling the energy consumption during the execution of the schedule. However, the dynamic unrelated machines environment is rarely considered, mainly because it is difficult to manually design simple heuristics, called dispatching rules (DRs), which are suitable for solving dynamic, non-standard scheduling problems. Using hyperheuristics, especially genetic programming (GP), alleviates the problem since it enables the automatic design of new DRs. In this study, we apply GP to automatically design DRs for solving the green scheduling problem in the unrelated machines environment under dynamic conditions. The total energy consumed during the system execution is optimised along with two standard scheduling criteria. The three most commonly investigated green scheduling problem variants from the literature are selected, and GP is adapted to generate appropriate DRs for each. The experiments show that GP-generated DRs efficiently solve the problem under dynamic conditions, providing a trade-off between optimising standard and energy-related criteria.

由于环境问题日益严重，绿色调度的概念（处理调度过程对环境的影响）变得越来越重要。大多数绿色调度问题变体都集中在对调度执行期间的能耗进行建模。但是，很少考虑动态的不相关机器环境，主要是因为很难手动设计简单的启发式方法，称为调度规则 （DR），这些启发式方法适用于解决动态、非标准的调度问题。使用超启发式方法，尤其是遗传编程 （GP），可以缓解这个问题，因为它可以自动设计新的 DR。在本研究中，我们应用 GP 自动设计 DR，以解决动态条件下不相关机器环境中的绿色调度问题。系统执行期间消耗的总能量与两个标准调度标准一起进行优化。从文献中选择了三种最常研究的绿色调度问题变体，并调整了 GP 以为每个变体生成适当的 DR。实验表明，GP 生成的 DR 在动态条件下有效地解决了问题，在优化标准和能源相关标准之间提供了权衡。